Interesting research: “Teams of LLM Agents can Exploit Zero-Day Vulnerabilities.”

Abstract: LLM agents have become increasingly sophisticated, especially in the realm of cybersecurity. Researchers have shown that LLM agents can exploit real-world vulnerabilities when given a description of the vulnerability and toy capture-the-flag problems. However, these agents still perform poorly on real-world vulnerabilities that are unknown to the agent ahead of time (zero-day vulnerabilities).

In this work, we show that teams of LLM agents can exploit real-world, zero-day vulnerabilities. Prior agents struggle with exploring many different vulnerabilities and long-range planning when used alone. To resolve this, we introduce HPTSA, a system of agents with a planning agent that can launch subagents. The planning agent explores the system and determines which subagents to call, resolving long-term planning issues when trying different vulnerabilities. We construct a benchmark of 15 real-world vulnerabilities and show that our team of agents improve over prior work by up to 4.5×.

The LLMs aren’t finding new vulnerabilities. They’re exploiting zero-days—which means they are not trained on them—in new ways. So think about this sort of thing combined with another AI that finds new vulnerabilities in code.

These kinds of developments are important to follow, as they are part of the puzzle of a fully autonomous AI cyberattack agent. I talk about this sort of thing more here.

Squid humor.

As usual, you can also use this squid post to talk about the security stories in the news that I haven’t covered.

Read my blog posting guidelines here.

This is a current list of where and when I am scheduled to speak:

• I’m appearing on a panel on Society and Democracy at ACM Collective Intelligence in Boston, Massachusetts. The conference runs from June 26 through 29, 2024, and my panel is at 9:00 AM on Friday, June 28.
• I’m speaking on “Reimagining Democracy in the Age of AI” at the Bozeman Library in Bozeman, Montana, USA, July 18, 2024. The event will also be available via Zoom.
• I’m speaking at the TEDxBillings Democracy Event in Billings, Montana, USA, on July 19, 2024.

The list is maintained on this page.

This is really neat demo of the security problems arising from reusing nonces with a symmetric cipher in GCM mode.

As India concluded the world’s largest election on June 5, 2024, with over 640 million votes counted, observers could assess how the various parties and factions used artificial intelligence technologies—and what lessons that holds for the rest of the world.

The campaigns made extensive use of AI, including deepfake impersonations of candidates, celebrities and dead politicians. By some estimates, millions of Indian voters viewed deepfakes.

But, despite fears of widespread disinformation, for the most part the campaigns, candidates and activists used AI constructively in the election. They used AI for typical political activities, including mudslinging, but primarily to better connect with voters.

Deepfakes without the deception

Political parties in India spent an estimated US$50 million on authorized AI-generated content for targeted communication with their constituencies this election cycle. And it was largely successful.

Indian political strategists have long recognized the influence of personality and emotion on their constituents, and they started using AI to bolster their messaging. Young and upcoming AI companies like The Indian Deepfaker, which started out serving the entertainment industry, quickly responded to this growing demand for AI-generated campaign material.

In January, Muthuvel Karunanidhi, former chief minister of the southern state of Tamil Nadu for two decades, appeared via video at his party’s youth wing conference. He wore his signature yellow scarf, white shirt, dark glasses and had his familiar stance—head slightly bent sideways. But Karunanidhi died in 2018. His party authorized the deepfake.

In February, the All-India Anna Dravidian Progressive Federation party’s official X account posted an audio clip of Jayaram Jayalalithaa, the iconic superstar of Tamil politics colloquially called “Amma” or “Mother.” Jayalalithaa died in 2016.

Meanwhile, voters received calls from their local representatives to discuss local issues—except the leader on the other end of the phone was an AI impersonation. Bhartiya Janta Party (BJP) workers like Shakti Singh Rathore have been frequenting AI startups to send personalized videos to specific voters about the government benefits they received and asking for their vote over WhatsApp.

Multilingual boost

Deepfakes were not the only manifestation of AI in the Indian elections. Long before the election began, Indian Prime Minister Narendra Modi addressed a tightly packed crowd celebrating links between the state of Tamil Nadu in the south of India and the city of Varanasi in the northern state of Uttar Pradesh. Instructing his audience to put on earphones, Modi proudly announced the launch of his “new AI technology” as his Hindi speech was translated to Tamil in real time.

In a country with 22 official languages and almost 780 unofficial recorded languages, the BJP adopted AI tools to make Modi’s personality accessible to voters in regions where Hindi is not easily understood. Since 2022, Modi and his BJP have been using the AI-powered tool Bhashini, embedded in the NaMo mobile app, to translate Modi’s speeches with voiceovers in Telugu, Tamil, Malayalam, Kannada, Odia, Bengali, Marathi and Punjabi.

As part of their demos, some AI companies circulated their own viral versions of Modi’s famous monthly radio show “Mann Ki Baat,” which loosely translates to “From the Heart,” which they voice cloned to regional languages.

Adversarial uses

Indian political parties doubled down on online trolling, using AI to augment their ongoing meme wars. Early in the election season, the Indian National Congress released a short clip to its 6 million followers on Instagram, taking the title track from a new Hindi music album named “Chor” (thief). The video grafted Modi’s digital likeness onto the lead singer and cloned his voice with reworked lyrics critiquing his close ties to Indian business tycoons.

The BJP retaliated with its own video, on its 7-million-follower Instagram account, featuring a supercut of Modi campaigning on the streets, mixed with clips of his supporters but set to unique music. It was an old patriotic Hindi song sung by famous singer Mahendra Kapoor, who passed away in 2008 but was resurrected with AI voice cloning.

Modi himself quote-tweeted an AI-created video of him dancing—a common meme that alters footage of rapper Lil Yachty on stage—commenting “such creativity in peak poll season is truly a delight.”

In some cases, the violent rhetoric in Modi’s campaign that put Muslims at risk and incited violence was conveyed using generative AI tools, but the harm can be traced back to the hateful rhetoric itself and not necessarily the AI tools used to spread it.

The Indian experience

India is an early adopter, and the country’s experiments with AI serve as an illustration of what the rest of the world can expect in future elections. The technology’s ability to produce nonconsensual deepfakes of anyone can make it harder to tell truth from fiction, but its consensual uses are likely to make democracy more accessible.

The Indian election’s embrace of AI that began with entertainment, political meme wars, emotional appeals to people, resurrected politicians and persuasion through personalized phone calls to voters has opened a pathway for the role of AI in participatory democracy.

The surprise outcome of the election, with the BJP’s failure to win its predicted parliamentary majority, and India’s return to a deeply competitive political system especially highlights the possibility for AI to have a positive role in deliberative democracy and representative governance.

Lessons for the world’s democracies

It’s a goal of any political party or candidate in a democracy to have more targeted touch points with their constituents. The Indian elections have shown a unique attempt at using AI for more individualized communication across linguistically and ethnically diverse constituencies, and making their messages more accessible, especially to rural, low-income populations.

AI and the future of participatory democracy could make constituent communication not just personalized but also a dialogue, so voters can share their demands and experiences directly with their representatives—at speed and scale.

India can be an example of taking its recent fluency in AI-assisted party-to-people communications and moving it beyond politics. The government is already using these platforms to provide government services to citizens in their native languages.

If used safely and ethically, this technology could be an opportunity for a new era in representative governance, especially for the needs and experiences of people in rural areas to reach Parliament.

This essay was written with Vandinika Shukla and previously appeared in The Conversation.

Public polling is a critical function of modern political campaigns and movements, but it isn’t what it once was. Recent US election cycles have produced copious postmortems explaining both the successes and the flaws of public polling. There are two main reasons polling fails.

First, nonresponse has skyrocketed. It’s radically harder to reach people than it used to be. Few people fill out surveys that come in the mail anymore. Few people answer their phone when a stranger calls. Pew Research reported that 36% of the people they called in 1997 would talk to them, but only 6% by 2018. Pollsters worldwide have faced similar challenges...

The post Using AI for Political Polling appeared first on Security Boulevard.

Public polling is a critical function of modern political campaigns and movements, but it isn’t what it once was. Recent US election cycles have produced copious postmortems explaining both the successes and the flaws of public polling. There are two main reasons polling fails.

First, nonresponse has skyrocketed. It’s radically harder to reach people than it used to be. Few people fill out surveys that come in the mail anymore. Few people answer their phone when a stranger calls. Pew Research reported that 36% of the people they called in 1997 would talk to them, but only 6% by 2018. Pollsters worldwide have faced similar challenges.

Second, people don’t always tell pollsters what they really think. Some hide their true thoughts because they are embarrassed about them. Others behave as a partisan, telling the pollster what they think their party wants them to say—or what they know the other party doesn’t want to hear.

Despite these frailties, obsessive interest in polling nonetheless consumes our politics. Headlines more likely tout the latest changes in polling numbers than the policy issues at stake in the campaign. This is a tragedy for a democracy. We should treat elections like choices that have consequences for our lives and well-being, not contests to decide who gets which cushy job.

Polling Machines?

AI could change polling. AI can offer the ability to instantaneously survey and summarize the expressed opinions of individuals and groups across the web, understand trends by demographic, and offer extrapolations to new circumstances and policy issues on par with human experts. The politicians of the (near) future won’t anxiously pester their pollsters for information about the results of a survey fielded last week: they’ll just ask a chatbot what people think. This will supercharge our access to realtime, granular information about public opinion, but at the same time it might also exacerbate concerns about the quality of this information.

I know it sounds impossible, but stick with us.

Large language models, the AI foundations behind tools like ChatGPT, are built on top of huge corpuses of data culled from the Internet. These are models trained to recapitulate what millions of real people have written in response to endless topics, contexts, and scenarios. For a decade or more, campaigns have trawled social media, looking for hints and glimmers of how people are reacting to the latest political news. This makes asking questions of an AI chatbot similar in spirit to doing analytics on social media, except that they are generative: you can ask them new questions that no one has ever posted about before, you can generate more data from populations too small to measure robustly, and you can immediately ask clarifying questions of your simulated constituents to better understand their reasoning

Researchers and firms are already using LLMs to simulate polling results. Current techniques are based on the ideas of AI agents. An AI agent is an instance of an AI model that has been conditioned to behave in a certain way. For example, it may be primed to respond as if it is a person with certain demographic characteristics and can access news articles from certain outlets. Researchers have set up populations of thousands of AI agents that respond as if they are individual members of a survey population, like humans on a panel that get called periodically to answer questions.

The big difference between humans and AI agents is that the AI agents always pick up the phone, so to speak, no matter how many times you contact them. A political candidate or strategist can ask an AI agent whether voters will support them if they take position A versus B, or tweaks of those options, like policy A-1 versus A-2. They can ask that question of male voters versus female voters. They can further limit the query to married male voters of retirement age in rural districts of Illinois without college degrees who lost a job during the last recession; the AI will integrate as much context as you ask.

What’s so powerful about this system is that it can generalize to new scenarios and survey topics, and spit out a plausible answer, even if its accuracy is not guaranteed. In many cases, it will anticipate those responses at least as well as a human political expert. And if the results don’t make sense, the human can immediately prompt the AI with a dozen follow-up questions.

Making AI agents better polling subjects

When we ran our own experiments in this kind of AI use case with the earliest versions of the model behind ChatGPT (GPT-3.5), we found that it did a fairly good job at replicating human survey responses. The ChatGPT agents tended to match the responses of their human counterparts fairly well across a variety of survey questions, such as support for abortion and approval of the US Supreme Court. The AI polling results had average responses, and distributions across demographic properties such as age and gender, similar to real human survey panels.

Our major systemic failure happened on a question about US intervention in the Ukraine war.  In our experiments, the AI agents conditioned to be liberal were predominantly opposed to US intervention in Ukraine and likened it to the Iraq war. Conservative AI agents gave hawkish responses supportive of US intervention. This is pretty much what most political experts would have expected of the political equilibrium in US foreign policy at the start of the decade but was exactly wrong in the politics of today.

This mistake has everything to do with timing. The humans were asked the question after Russia’s full-scale invasion in 2022, whereas the AI model was trained using data that only covered events through September 2021. The AI got it wrong because it didn’t know how the politics had changed. The model lacked sufficient context on crucially relevant recent events.

We believe AI agents can overcome these shortcomings. While AI models are dependent on  the data they are trained with, and all the limitations inherent in that, what makes AI agents special is that they can automatically source and incorporate new data at the time they are asked a question. AI models can update the context in which they generate opinions by learning from the same sources that humans do. Each AI agent in a simulated panel can be exposed to the same social and media news sources as humans from that same demographic before they respond to a survey question. This works because AI agents can follow multi-step processes, such as reading a question, querying a defined database of information (such as Google, or the New York Times, or Fox News, or Reddit), and then answering a question.

In this way, AI polling tools can simulate exposing their synthetic survey panel to whatever news is most relevant to a topic and likely to emerge in each AI agent’s own echo chamber. And they can query for other relevant contextual information, such as demographic trends and historical data. Like human pollsters, they can try to refine their expectations on the basis of factors like how expensive homes are in a respondent’s neighborhood, or how many people in that district turned out to vote last cycle.

Likely use cases for AI polling

AI polling will be irresistible to campaigns, and to the media. But research is already revealing when and where this tool will fail. While AI polling will always have limitations in accuracy, that makes them similar to, not different from, traditional polling. Today’s pollsters are challenged to reach sample sizes large enough to measure statistically significant differences between similar populations, and the issues of nonresponse and inauthentic response can make them systematically wrong. Yet for all those shortcomings, both traditional and AI-based polls will still be useful. For all the hand-wringing and consternation over the accuracy of US political polling, national issue surveys still tend to be accurate to within a few percentage points. If you’re running for a town council seat or in a neck-and-neck national election, or just trying to make the right policy decision within a local government, you might care a lot about those small and localized differences. But if you’re looking to track directional changes over time, or differences between demographic groups, or to uncover insights about who responds best to what message, then these imperfect signals are sufficient to help campaigns and policymakers.

Where AI will work best is as an augmentation of more traditional human polls. Over time, AI tools will get better at anticipating human responses, and also at knowing when they will be most wrong or uncertain. They will recognize which issues and human communities are in the most flux, where the model’s training data is liable to steer it in the wrong direction. In those cases, AI models can send up a white flag and indicate that they need to engage human respondents to calibrate to real people’s perspectives. The AI agents can even be programmed to automate this. They can use existing survey tools—with all their limitations and latency—to query for authentic human responses when they need them.

This kind of human-AI polling chimera lands us, funnily enough, not too distant from where survey research is today. Decades of social science research has led to substantial innovations in statistical methodologies for analyzing survey data. Current polling methods already do substantial modeling and projecting to predictively model properties of a general population based on sparse survey samples. Today, humans fill out the surveys and computers fill in the gaps. In the future, it will be the opposite: AI will fill out the survey and, when the AI isn’t sure what box to check, humans will fill the gaps. So if you’re not comfortable with the idea that political leaders will turn to a machine to get intelligence about which candidates and policies you want, then you should have about as many misgivings about the present as you will the future.

And while the AI results could improve quickly, they probably won’t be seen as credible for some time. Directly asking people what they think feels more reliable than asking a computer what people think. We expect these AI-assisted polls will be initially used internally by campaigns, with news organizations relying on more traditional techniques. It will take a major election where AI is right and humans are wrong to change that.

This essay was written with Aaron Berger, Eric Gong, and Nathan Sanders, and previously appeared on the Harvard Kennedy School Ash Center’s website.

New research: “Deception abilities emerged in large language models“:

Abstract: Large language models (LLMs) are currently at the forefront of intertwining AI systems with human communication and everyday life. Thus, aligning them with human values is of great importance. However, given the steady increase in reasoning abilities, future LLMs are under suspicion of becoming able to deceive human operators and utilizing this ability to bypass monitoring efforts. As a prerequisite to this, LLMs need to possess a conceptual understanding of deception strategies. This study reveals that such strategies emerged in state-of-the-art LLMs, but were nonexistent in earlier LLMs. We conduct a series of experiments showing that state-of-the-art LLMs are able to understand and induce false beliefs in other agents, that their performance in complex deception scenarios can be amplified utilizing chain-of-thought reasoning, and that eliciting Machiavellianism in LLMs can trigger misaligned deceptive behavior. GPT-4, for instance, exhibits deceptive behavior in simple test scenarios 99.16% of the time (P < 0.001). In complex second-order deception test scenarios where the aim is to mislead someone who expects to be deceived, GPT-4 resorts to deceptive behavior 71.46% of the time (P < 0.001) when augmented with chain-of-thought reasoning. In sum, revealing hitherto unknown machine behavior in LLMs, our study contributes to the nascent field of machine psychology.

Interesting research: “Hyperlink Hijacking: Exploiting Erroneous URL Links to Phantom Domains“:

Abstract: Web users often follow hyperlinks hastily, expecting them to be correctly programmed. However, it is possible those links contain typos or other mistakes. By discovering active but erroneous hyperlinks, a malicious actor can spoof a website or service, impersonating the expected content and phishing private information. In “typosquatting,” misspellings of common domains are registered to exploit errors when users mistype a web address. Yet, no prior research has been dedicated to situations where the linking errors of web publishers (i.e. developers and content contributors) propagate to users. We hypothesize that these “hijackable hyperlinks” exist in large quantities with the potential to generate substantial traffic. Analyzing large-scale crawls of the web using high-performance computing, we show the web currently contains active links to more than 572,000 dot-com domains that have never been registered, what we term ‘phantom domains.’ Registering 51 of these, we see 88% of phantom domains exceeding the traffic of a control domain, with up to 10 times more visits. Our analysis shows that these links exist due to 17 common publisher error modes, with the phantom domains they point to free for anyone to purchase and exploit for under $20, representing a low barrier to entry for potential attackers.

This week, I hosted the seventeenth Workshop on Security and Human Behavior at the Harvard Kennedy School. This is the first workshop since our co-founder, Ross Anderson, died unexpectedly.

SHB is a small, annual, invitational workshop of people studying various aspects of the human side of security. The fifty or so attendees include psychologists, economists, computer security researchers, criminologists, sociologists, political scientists, designers, lawyers, philosophers, anthropologists, geographers, neuroscientists, business school professors, and a smattering of others. It’s not just an interdisciplinary event; most of the people here are individually interdisciplinary...

The post Security and Human Behavior (SHB) 2024 appeared first on Security Boulevard.

Peru has set a lower squid quota for 2024. The article says “giant squid,” but that seems wrong. We don’t eat those.

As usual, you can also use this squid post to talk about the security stories in the news that I haven’t covered.

Read my blog posting guidelines here.

This week, I hosted the seventeenth Workshop on Security and Human Behavior at the Harvard Kennedy School. This is the first workshop since our co-founder, Ross Anderson, died unexpectedly.

SHB is a small, annual, invitational workshop of people studying various aspects of the human side of security. The fifty or so attendees include psychologists, economists, computer security researchers, criminologists, sociologists, political scientists, designers, lawyers, philosophers, anthropologists, geographers, neuroscientists, business school professors, and a smattering of others. It’s not just an interdisciplinary event; most of the people here are individually interdisciplinary.

Our goal is always to maximize discussion and interaction. We do that by putting everyone on panels, and limiting talks to six to eight minutes, with the rest of the time for open discussion. Short talks limit presenters’ ability to get into the boring details of their work, and the interdisciplinary audience discourages jargon.

Since the beginning, this workshop has been the most intellectually stimulating two days of my professional year. It influences my thinking in different and sometimes surprising ways—and has resulted in some new friendships and unexpected collaborations. This is why some of us have been coming back every year for over a decade.

This year’s schedule is here. This page lists the participants and includes links to some of their work. Kami Vaniea liveblogged both days.

Here are my posts on the first, second, third, fourth, fifth, sixth, seventh, eighth, ninth, tenth, eleventh, twelfth, thirteenth, fourteenth, fifteenth and sixteenth SHB workshops. Follow those links to find summaries, papers, and occasionally audio/video recordings of the sessions. Ross maintained a good webpage of psychology and security resources—it’s still up for now.

Next year we will be in Cambridge, UK, hosted by Frank Stajano.

The US Justice Department has dismantled an enormous botnet:

According to an indictment unsealed on May 24, from 2014 through July 2022, Wang and others are alleged to have created and disseminated malware to compromise and amass a network of millions of residential Windows computers worldwide. These devices were associated with more than 19 million unique IP addresses, including 613,841 IP addresses located in the United States. Wang then generated millions of dollars by offering cybercriminals access to these infected IP addresses for a fee.

[…]

This operation was a coordinated multiagency effort led by law enforcement in the United States, Singapore, Thailand, and Germany. Agents and officers searched residences, seized assets valued at approximately $30 million, and identified additional forfeitable property valued at approximately $30 million. The operation also seized 23 domains and over 70 servers constituting the backbone of Wang’s prior residential proxy service and the recent incarnation of the service. By seizing multiple domains tied to the historical 911 S5, as well as several new domains and services directly linked to an effort to reconstitute the service, the government has successfully terminated Wang’s efforts to further victimize individuals through his newly formed service Clourouter.io and closed the existing malicious backdoors.

The creator and operator of the botnet, YunHe Wang, was arrested in Singapore.

Three news articles.

The US is using a World War II law that bans aircraft photography of military installations to charge someone with doing the same thing with a drone.

Microsoft recently caught state-backed hackers using its generative AI tools to help with their attacks. In the security community, the immediate questions weren’t about how hackers were using the tools (that was utterly predictable), but about how Microsoft figured it out. The natural conclusion was that Microsoft was spying on its AI users, looking for harmful hackers at work.

Some pushed back at characterizing Microsoft’s actions as “spying.” Of course cloud service providers monitor what users are doing. And because we expect Microsoft to be doing something like this, it’s not fair to call it spying.

We see this argument as an example of our shifting collective expectations of privacy. To understand what’s happening, we can learn from an unlikely source: fish.

In the mid-20th century, scientists began noticing that the number of fish in the ocean—so vast as to underlie the phrase “There are plenty of fish in the sea”—had started declining rapidly due to overfishing. They had already seen a similar decline in whale populations, when the post-WWII whaling industry nearly drove many species extinct. In whaling and later in commercial fishing, new technology made it easier to find and catch marine creatures in ever greater numbers. Ecologists, specifically those working in fisheries management, began studying how and when certain fish populations had gone into serious decline.

One scientist, Daniel Pauly, realized that researchers studying fish populations were making a major error when trying to determine acceptable catch size. It wasn’t that scientists didn’t recognize the declining fish populations. It was just that they didn’t realize how significant the decline was. Pauly noted that each generation of scientists had a different baseline to which they compared the current statistics, and that each generation’s baseline was lower than that of the previous one.

What seems normal to us in the security community is whatever was commonplace at the beginning of our careers.

Pauly called this “shifting baseline syndrome” in a 1995 paper. The baseline most scientists used was the one that was normal when they began their research careers. By that measure, each subsequent decline wasn’t significant, but the cumulative decline was devastating. Each generation of researchers came of age in a new ecological and technological environment, inadvertently masking an exponential decline.

Pauly’s insights came too late to help those managing some fisheries. The ocean suffered catastrophes such as the complete collapse of the Northwest Atlantic cod population in the 1990s.

Internet surveillance, and the resultant loss of privacy, is following the same trajectory. Just as certain fish populations in the world’s oceans have fallen 80 percent, from previously having fallen 80 percent, from previously having fallen 80 percent (ad infinitum), our expectations of privacy have similarly fallen precipitously. The pervasive nature of modern technology makes surveillance easier than ever before, while each successive generation of the public is accustomed to the privacy status quo of their youth. What seems normal to us in the security community is whatever was commonplace at the beginning of our careers.

Historically, people controlled their computers, and software was standalone. The always-connected cloud-deployment model of software and services flipped the script. Most apps and services are designed to be always-online, feeding usage information back to the company. A consequence of this modern deployment model is that everyone—cynical tech folks and even ordinary users—expects that what you do with modern tech isn’t private. But that’s because the baseline has shifted.

AI chatbots are the latest incarnation of this phenomenon: They produce output in response to your input, but behind the scenes there’s a complex cloud-based system keeping track of that input—both to improve the service and to sell you ads.

Shifting baselines are at the heart of our collective loss of privacy. The U.S. Supreme Court has long held that our right to privacy depends on whether we have a reasonable expectation of privacy. But expectation is a slippery thing: It’s subject to shifting baselines.

The question remains: What now? Fisheries scientists, armed with knowledge of shifting-baseline syndrome, now look at the big picture. They no longer consider relative measures, such as comparing this decade with the last decade. Instead, they take a holistic, ecosystem-wide perspective to see what a healthy marine ecosystem and thus sustainable catch should look like. They then turn these scientifically derived sustainable-catch figures into limits to be codified by regulators.

In privacy and security, we need to do the same. Instead of comparing to a shifting baseline, we need to step back and look at what a healthy technological ecosystem would look like: one that respects people’s privacy rights while also allowing companies to recoup costs for services they provide. Ultimately, as with fisheries, we need to take a big-picture perspective and be aware of shifting baselines. A scientifically informed and democratic regulatory process is required to preserve a heritage—whether it be the ocean or the Internet—for the next generation.

This essay was written with Barath Raghavan, and previously appeared in IEEE Spectrum.

Microsoft recently caught state-backed hackers using its generative AI tools to help with their attacks. In the security community, the immediate questions weren’t about how hackers were using the tools (that was utterly predictable), but about how Microsoft figured it out. The natural conclusion was that Microsoft was spying on its AI users, looking for harmful hackers at work.

Some pushed back at characterizing Microsoft’s actions as “spying.” Of course cloud service providers monitor what users are doing. And because we expect Microsoft to be doing something like this, it’s not fair to call it spying...

The post Online Privacy and Overfishing appeared first on Security Boulevard.

Interesting story of breaking the security of the RoboForm password manager in order to recover a cryptocurrency wallet password.

Grand and Bruno spent months reverse engineering the version of the RoboForm program that they thought Michael had used in 2013 and found that the pseudo-random number generator used to generate passwords in that version—­and subsequent versions until 2015­—did indeed have a significant flaw that made the random number generator not so random. The RoboForm program unwisely tied the random passwords it generated to the date and time on the user’s computer­—it determined the computer’s date and time, and then generated passwords that were predictable. If you knew the date and time and other parameters, you could compute any password that would have been generated on a certain date and time in the past.

If Michael knew the day or general time frame in 2013 when he generated it, as well as the parameters he used to generate the password (for example, the number of characters in the password, including lower- and upper-case letters, figures, and special characters), this would narrow the possible password guesses to a manageable number. Then they could hijack the RoboForm function responsible for checking the date and time on a computer and get it to travel back in time, believing the current date was a day in the 2013 time frame when Michael generated his password. RoboForm would then spit out the same passwords it generated on the days in 2013.

Technology was once simply a tool—and a small one at that—used to amplify human intent and capacity. That was the story of the industrial revolution: we could control nature and build large, complex human societies, and the more we employed and mastered technology, the better things got. We don’t live in that world anymore. Not only has technology become entangled with the structure of society, but we also can no longer see the world around us without it. The separation is gone, and the control we thought we once had has revealed itself as a mirage. We’re in a transitional period of history right now...

The post Seeing Like a Data Structure appeared first on Security Boulevard.

Technology was once simply a tool—and a small one at that—used to amplify human intent and capacity. That was the story of the industrial revolution: we could control nature and build large, complex human societies, and the more we employed and mastered technology, the better things got. We don’t live in that world anymore. Not only has technology become entangled with the structure of society, but we also can no longer see the world around us without it. The separation is gone, and the control we thought we once had has revealed itself as a mirage. We’re in a transitional period of history right now.

We tell ourselves stories about technology and society every day. Those stories shape how we use and develop new technologies as well as the new stories and uses that will come with it. They determine who’s in charge, who benefits, who’s to blame, and what it all means.

Some people are excited about the emerging technologies poised to remake society. Others are hoping for us to see this as folly and adopt simpler, less tech-centric ways of living. And many feel that they have little understanding of what is happening and even less say in the matter.

But we never had total control of technology in the first place, nor is there a pretechnological golden age to which we can return. The truth is that our data-centric way of seeing the world isn’t serving us well. We need to tease out a third option. To do so, we first need to understand how we got here.

Abstraction

When we describe something as being abstract, we mean it is removed from reality: conceptual and not material, distant and not close-up. What happens when we live in a world built entirely of the abstract? A world in which we no longer care for the messy, contingent, nebulous, raw, and ambiguous reality that has defined humanity for most of our species’ existence? We are about to find out, as we begin to see the world through the lens of data structures.

Two decades ago, in his book Seeing Like a State, anthropologist James C. Scott explored what happens when governments, or those with authority, attempt and fail to “improve the human condition.” Scott found that to understand societies and ecosystems, government functionaries and their private sector equivalents reduced messy reality to idealized, abstracted, and quantified simplifications that made the mess more “legible” to them. With this legibility came the ability to assess and then impose new social, economic, and ecological arrangements from the top down: communities of people became taxable citizens, a tangled and primeval forest became a monoculture timber operation, and a convoluted premodern town became a regimented industrial city.

This kind of abstraction was seemingly necessary to create the world around us today. It is difficult to manage a large organization, let alone an interconnected global society of eight billion people, without some sort of structure and means to abstract away details. Facility with abstraction, and abstract reasoning, has enabled all sorts of advancements in science, technology, engineering, and math—the very fields we are constantly being told are in highest demand.

The map is not the territory, and no amount of intellectualization will make it so. Creating abstract representations by necessity leaves out important detail and context. Inevitably, as Scott cataloged, the use of large-scale abstractions fails, leaving leadership bewildered at the failure and ordinary people worse off. But our desire to abstract never went away, and technology, as always, serves to amplify intent and capacity. Now, we manifest this abstraction with software. Computing supercharges the creative and practical use of abstraction. This is what life is like when we see the world the way a data structure sees the world. These are the same tricks Scott documented. What has changed is their speed and their ubiquity.

Each year, more students flock to computer science, a field with some of the highest-paying, most sought-after jobs. Nearly every university’s curriculum immediately introduces these students to data structures. A data structure enables a programmer to organize data—about anything—in a way that is easy to understand and act upon in software: to sort, search, structure, organize, or combine that data. A course in data structures is exercise after exercise in building and manipulating abstractions, ones that are typically entirely divorced from the messy, context-laden, real-world data that those data structures will be used to store.

As students graduate, most join companies that demand these technical skills—universally seen as essential to computer science work—who see themselves as “changing the world,” often with even grander ambitions than the prosaic aims of state functionaries cataloged by Scott.

Engineers are transforming data about the world around us into data structures, at massive scale. They then employ another computer science trick: indirection. This is the ability to break apart some sociotechnical process—to “disrupt”—and replace each of the now-broken pieces with abstractions that can interface with each other. These data structures and abstractions are then combined in software to take action on this view of reality, action that increasingly has a human and societal dimension.

Here’s an example. When the pandemic started and delivery orders skyrocketed, technologists saw an opportunity: ghost kitchens. No longer did the restaurant a customer was ordering from actually have to exist. All that mattered was that the online menu catered to customer desires. Once ordered, the food had to somehow get sourced, cooked, and packaged, sight unseen, and be delivered to the customer’s doorstep. Now, lots of places we order food from are subject to this abstraction and indirection, more like Amazon’s supply chain than a local diner of yore.

Facebook sees its users like a data structure when it classifies us into ever more precise interest categories, so as to better sell our attention to advertisers. Spotify sees us like a data structure when it tries to play music it thinks we will like based on the likes of people who like some of the same music we like. TikTok users often exclaim and complain that its recommendations seem to uncannily tap into latent desires and interests, leading many to perform psychological self-diagnosis using their “For You” page.

Data structures dominate our world and are a byproduct of the rational, modern era, but they are ushering in an age of chaos. We need to embrace and tame, but not extinguish, this chaos for a better world.

Machines

Historian of technology Lewis Mumford once wrote that clocks enabled the division of time, and that enabled the regimentation of society that made the industrial revolution possible. This transformation, once fully underway around the world in the 20th century, fundamentally changed the story of society. It shifted us away from a society centered around interpersonal dynamics and communal interactions to one that was systematic and institutional.

We used to take the world in and interpret it through human eyes. The world before the industrial revolution wasn’t one in which ordinary people interacted with large-scale institutions or socio-technical systems. It wasn’t possible for someone to be a “company man” before there was a corporate way of doing things that in theory depended only on rules, laws, methods, and principles, not on the vicissitudes of human behavior.

Since the beginning of the industrial revolution, workers and the natural world have been subject to abstraction. This involves the use of abstract reason over social preferences. Knowledge about the world was no longer in our heads but out in the world. So we got newspapers, instruction manuals, bylaws, and academic journals. And we should be clear: this was largely an improvement. The era of systems—of modernity—was an improvement on what came before. It’s better for society to have laws rather than rulers, better for us to lean on science than superstition. We can’t and shouldn’t go back.

The tools of reason enabled the “high modernists,” as Scott calls them, to envision a world shaped entirely by reason. But such reason was and is never free of personal biases. It always neglects the messiness of reality and the tacit and contextual knowledge and skill that is needed to cope with that mess—and this is where trouble began to arise.

Workers were and are treated as cogs in the industrial machine, filling a narrow role on an assembly line or performing a service job within narrow parameters. Nature is treated as a resource for human use, a near-infinite storehouse of materials and dumping ground for wastes. Even something as essential and grounding as farming is seen as mechanistic—”a farm is a factory in a remote area,” as put by one John Deere executive—where plants are machines that take in nitrogen, phosphorus, and potassium and produce barely edible dent corn. There’s even a popular myth that eminent business theorist W.E. Deming said: “If you can’t measure it, you can’t manage it”—lending credence to the measurement and optimization mindset.

The abstractions nearly write themselves. Though, leaving nothing to chance, entrepreneurs and their funders have flocked to translating these precomputing abstractions for the age of data structures. This is happening in both seen and unseen ways. Uber and Lyft turned people into driving robots that follow algorithmic guidance from one place to another. Amazon made warehouse workers perform precisely defined tasks in concert with literal robots. Agtech companies turn farms into data structures to then optimize the application of fertilizer, irrigation water, pesticides, and herbicides.

Beyond simply dividing time, computation has enabled the division of information. This is embodied at the lowest levels—bits and packets of data flowing through the Internet—all the way up to the highest levels, where many jobs can be described as a set of information-processing tasks performed by one worker only to be passed along to another. But this sort of computing—that’s just worn-out optimization techniques that date back to last century’s Taylorism—didn’t move us into the unstable world we’re in today. It was a different sort of computation that did that.

Computation

Today we’re in an era where computing not only abstracts our world but also defines our inner worlds: the very thoughts we have and the ways we communicate.

It is this abstracted reality that is presented to us when we open a map on our phones, search the Internet, or “engage” on social media. It is this constructed reality that shapes the decisions businesses make every day, governs financial markets, influences geopolitical strategy, and increasingly controls more of how global society functions. It is this synthesized reality we consume when the answers we seek about the world are the entire writings of humanity put into a blender and strained out by a large language model.

The first wave of this crested a decade ago only to crash down on us. Back then, search engines represented de facto reality, and “just Google it” became a saying: whatever the search engine said was right. But in some sense that was a holdover from the previous “modern” era but with a large data structure—the search engine’s vast database—replacing some classic source of truth such as the news media or the government. We all had a hope that with enough data, and algorithms to sift through it all, we could have a simple technological abstraction over the messiness of reality with a coherent answer no matter what the question was.

As we move toward the future promised by some technologists, our human-based view of the world and that of the data structures embedded in our computing devices will converge. Why bother to make a product at all when you can just algorithmically generate thousands of “ghost products,” in the hopes that someone will buy.

Scott’s critiques of datafication remain. We are becoming increasingly aware that things are continuous spectra, not discrete categories. Writing about the failure of contact tracing apps, activist Cory Doctorow said, “We can’t add, subtract, multiply or divide qualitative elements, so we just incinerate them, sweep up the dubious quantitative residue that remains, do math on that, and simply assert that nothing important was lost in the process.”

A pair of augmented-reality glasses may no longer let us see the world unfiltered by data structures but instead dissect and categorize every experience. A person on the street is no longer an individual but a member of a subcategory of “person” as determined by an AI classifier. A street is no longer the place you grew up but an abstraction from a map. And a local cafe is no longer a community hangout but a data structure containing a menu, a list of reservation options, and a hundred 5-star ratings.

Whether as glasses we look through or simply as screens on our devices, reality will be augmented by the data structures that categorize the world around us. Just as search engines caused the rise of SEO, where writers tweak their writing to attract search engines rather than human readers, this augmented reality will result in its own optimizations. We may be seeing the first signs of this with “Thai Food Near Me” as the literal name of businesses that are trying to satisfy the search function of mapping apps. Soon, even the physical form of things in the world may be determined in a coevolution with technology, where the form of things in the real world, even a dish at a restaurant, is chosen by what will look best when seen through our technological filters. It’s a data layer on top of reality. And the problems get worse when the relative importance of the data and reality flip. Is it more important to make a restaurant’s food taste better, or just more Instagrammable?

People are already working to exploit the data structures and algorithms that govern our world. Amazon drivers hang smartphones in trees to trick the system. Songwriters put their catchy choruses near the beginning to exploit Spotify’s algorithms. And podcasters deliberately mispronounce words because people comment with corrections and those comments count as “engagement” to the algorithms.

These hacks are fundamentally about the breakdown of “the system.” (We’re not suggesting that there’s a single system that governs society but rather a mess of systems that interact and overlap in our lives and are more or less relevant in particular contexts.) Systems work according to rules, either ones made consciously by people or, increasingly, automatically determined by data structures and algorithms. But systems of rules are, by their nature, trying to create a map for a messy territory, and rules will always have loopholes that can be taken advantage of.

The challenge with previous generations of tech—and the engineers who built them—is that they got stuck in the rigidity of systems. That’s what the company man was all about: the processes of the company, of Taylorism, of the McKinsey Way, of Scrum software development, of effective altruism, and of so many more. These all promised certainty, control, optimality, correctness, and sometimes even virtue: all just manifestations of a rigid and “rational” way of thinking and solving problems. Making systems work in this way at a societal level has failed. This is what Scott was saying in his seminal book. It was always doomed to fail.

Fissures

Seeing like a state was all about “legibility.” But the world is too difficult to make legible today. That’s where data structures, algorithms, and AI come in: humans no longer need to manually create legibility. Nor do humans even need to consume what is made legible. Raw data about the world can be fed into new AI tools to create a semblance of legibility. We can then have yet more automated tools act upon this supposed representation of the world, soon with real-life consequences. We’re now delegating the process of creating legibility to technology. Along the way, we’ve made it approximate: legible to someone or something else but not to the person who actually is in charge.

Right now, we’re living through the last attempts at making those systems work, with a perhaps naive hope and a newfound belief in AI and the data science that fuels it. The hope is that, because we have better algorithms that can help us make sense of even more data, we can somehow succeed at making systems work where past societies have failed. But it’s not going to work because it’s the mode of thought that doesn’t work.

The power to see like a state was intoxicating for government planners, corporate efficiency experts, and adherents to high modernism in general. But modern technology lets us all see like a state. And with the advent of AI, we all have the power to act on that seeing.

AI is made up of data structures that enable a mapping from the messy multidimensional reality that we inhabit to categories and patterns that are useful in some way. Spotify may organize songs into clever new musical genres invented by its AI, but it’s still an effort to create legibility out of thin air. We’re sending verbose emails with AI tools that will just be summarized by another AI. These are all just concepts, whether they’re created by a human mind or by a data structure or AI tool. And while concepts help us understand reality, they aren’t reality itself.

The problem we face is at once simple to explain and fiendishly difficult to do something about. It’s the interplay of nebulosity and pattern, as scholar David Chapman puts it: reality is nebulous (messy), but to get on with our lives, we see patterns (make sense of it in context-dependent ways). Generally, we as people don’t have strict rules for how to make breakfast, and we don’t need the task explained to us when a friend asks us for a cup of coffee. But that’s not the case for a computer, or a robot, or even a corporate food service, which can’t navigate the intricacies and uncertainties of the real world with the flexibility we expect of a person. And at an even larger scale, our societal systems, whether we’re talking about laws and governments or just the ways our employers expect us to get our jobs done, don’t have that flexibility built into them. We’ve seen repeatedly how breaking corporate or government operations into thousands of disparate, rigid contracts ends in failure.

Decades ago, the cracks in these rational systems were only visible to a few, left for debate in the halls of universities, board rooms, and militaries. Now, nebulosity, complexity, and the breakdown of these systems is all around for everyone to see. When teenagers are training themselves to see the world the way social-media ranking algorithms do, and can notice a change in real time, that’s how we know that the cracks are pervasive.

The complexity of society today, and the failure of rigid systems to cope, is scary to many. Nobody’s in charge of, or could possibly even understand, all these complex technological systems that now run our global society. As scholar Brian Klaas puts it, “the cognitive shortcuts we use to survive are mismatched with the complex reality we now navigate.” For some, this threat demands dramatic action, such as replacing some big system we have—say, capitalism—with an alternative means of organizing society. For others, it demands throwing out all of modernity to go back to a mythical, simpler golden age: one with more human-scale systems of order and authority, which they imagine was somehow better. And yet others see the cracks in the system but hope that with more data and more tweaks, it can be repaired and our problems will be definitively solved.

However, it’s not this particular system that failed but rather the mode of society that depends on rigid systems to function. Replacing one rigid system with another won’t work. There’s certainly no golden age to return to. And simpler forms of society aren’t options for us at the scale of humanity today. So where does that leave us?

Tension

The ability to see like a data structure afforded us the technology we have today. But it was built for and within a set of societal systems—and stories—that can’t cope with nebulosity. Worse still is the transitional era we’ve entered, in which overwhelming complexity leads more and more people to believe in nothing. That way lies madness. Seeing is a choice, and we need to reclaim that choice. However, we need to see things and do things differently, and build sociotechnical systems that embody this difference.

This is best seen through a small example. In our jobs, many of us deal with interpersonal dynamics that sometimes overwhelm the rules. The rules are still there—those that the company operates by and laws that it follows—meaning there are limits to how those interpersonal dynamics can play out. But those rules are rigid and bureaucratic, and most of the time they are irrelevant to what you’re dealing with. People learn to work with and around the rules rather than follow them to the letter. Some of these might be deliberate hacks, ones that are known, and passed down, by an organization’s workers. A work-to-rule strike, or quiet quitting for that matter, is effective at slowing a company to a halt because work is never as routine as schedules, processes, leadership principles, or any other codified rules might allow management to believe.

The tension we face is that on an everyday basis, we want things to be simple and certain. But that means ignoring the messiness of reality. And when we delegate that simplicity and certainty to systems—either to institutions or increasingly to software—they feel impersonal and oppressive. People used to say that they felt like large institutions were treating them like a number. For decades, we have literally been numbers in government and corporate data structures.

Breakdown

As historian Jill Lepore wrote, we used to be in a world of mystery. Then we began to understand those mysteries and use science to turn them into facts. And then we quantified and operationalized those facts through numbers. We’re currently in a world of data—overwhelming, human-incomprehensible amounts of data—that we use to make predictions even though that data isn’t enough to fully grapple with the complexity of reality.

How do we move past this era of breakdown? It’s not by eschewing technology. We need our complex socio-technical systems. We need mental models to make sense of the complexities of our world. But we also need to understand and accept their inherent imperfections. We need to make sure we’re avoiding static and biased patterns—of the sort that a state functionary or a rigid algorithm might produce—while leaving room for the messiness inherent in human interactions. Chapman calls this balance “fluidity,” where society (and really, the tech we use every day) gives us the disparate things we need to be happy while also enabling the complex global society we have today.

These things can be at odds. As social animals, we need the feeling of belonging, like being part of a small tribe. However, at the same time, we have to “belong” in a technological, scientific, and institutional world of eight billion interconnected people. To feel connected to those around us, we need access to cultural creativity, whether it be art, music, literature, or forms of entertainment and engagement that have yet to be invented. But we also need to avoid being fragmented into nanogenres where we can’t share that creativity and cultural appreciation with others. We must be able to be who we are and choose who we associate with on an ever-changing basis while being able to play our parts to make society function and feel a sense of responsibility and accomplishment in doing so. And perhaps most importantly, we need the ability to make sense of the torrent of information that we encounter every day while accepting that it will never be fully coherent, nor does it need to be.

This isn’t meant to be idealistic or something for the distant future. It’s something we need now. How well civilization functions in the coming years depends upon making this a reality. On our present course, we face the nihilism that comes with information overload, careening from a world that a decade ago felt more or less orderly to one in which nothing has any clear meaning or trustworthiness. It’s in an environment like this that polarization, conspiracies, and misinformation thrive. This leads to a loss of societal trust. Our institutions and economic systems are based upon trust. We’ve seen what societies look like when trust disappears: ordinary social systems fail, and when they do work, they are more expensive, capricious, violent, and unfair.

The challenge for us is to think how we can create new ways of being and thinking that move us—and not just a few of us but everyone—to be able to at first cope, and then later thrive, in this world we’re in.

Fluidity

There’s no single solution. It’ll be a million little things, but they all will share the overall themes of resilience in the form of fluidity. Technology’s role in this is vital, helping us make tentative, contextual, partial sense of the complex world around us. When we take a snapshot of a bird—or listen to its song—with an app that identifies the species, it is helping us gain some limited understanding. When we use our phones to find a park, local restaurant, or even a gas station in an unfamiliar city, it is helping us make our way in a new environment. On vacation in France, one of us used our phone’s real-time translation feature to understand what our tour guide was saying. Think of how we use weather apps, fitness apps, or self-guided museum tour apps to improve our lives. We need more tools like this in every context to help us to understand nuance and context beyond the level we have time for in our busy lives.

It’s not enough to have software, AI or otherwise, interpret the world for us. What we need is the ability to seamlessly navigate all the different contexts in our life. Take, for instance, the problem of understanding whether something seen online is true. This was already tricky and is now fiendishly difficult what with the Internet, social media, and now generative AI all laden with plausible untruths. But what does “true” mean, anyway? It’s equally wrong to believe in a universal, singular, objective truth in all situations as to not know what to believe and hold everything to be equally false (or true). Both of these options give propagandists a leg up.

Instead, we need fluidity: in Chapman’s terms, to be able to always ask, “In what sense?” Let’s say you see a video online of something that doesn’t seem physically possible and ask, “Is this real?” A useful technology would help you ask, “In what sense?” Maybe it’s something done physically, with no trickery involved, and it’s just surprising. Maybe it’s a magic trick, or real as in created for a TV show promotion, but not actually something that happened in the physical world. Maybe it was created by a movie special effects team. Maybe it’s propaganda created by a nation state. Sorting through contexts like this can be tedious, and while we intuitively do it all the time, in a technologically complex world we could use some help. It’s important to enable people to continue to communicate and interact in ways that make us feel comfortable, not completely driven either by past social custom or by algorithms that optimize for engagement. Think WhatsApp groups where people just talk, not Facebook groups that are mediated and controlled by Meta.

Belonging is important, and its lack creates uncertainty and a lack of trust. There are lessons we can learn from nontechnological examples. For example, Switzerland has a remarkable number of “associations”—for everything from business groups to bird watching clubs—and a huge number of Swiss residents take part. This sort of thing was once part of American culture but declined dramatically over the 20th century as documented in Putnam’s classic book Bowling Alone. Technology can enable dynamic new ways for people to associate as the online and offline worlds fuse—think of the Internet’s ability to help people find each other—though it must avoid the old mindset of optimization at all costs.

We all struggle with life in our postmodern society, that unplanned experiment of speed, scale, scope, and complexity never before seen in human history. Technology can help by bridging what our minds expect with how systems work. What if every large institution, whether a government or corporation, were to enable us to interact with it not on its terms, in their bureaucratic language and with all the complexity that large systems entail, but with computational tools that use natural language, understand context and nuance, and yet can still interface with the data structures that make its large systems tick. There are some promising early prototypes, such as tools that simplify the process of filling out tedious paperwork. That might feel small, almost trivial. But refined, and in aggregate, this could represent a sea change in how we interact with large systems. They will come to feel no longer as impersonal and imposing bureaucracies but as enablers of functioning and flourishing societies.

And it’s not all about large scale either. Scale isn’t always desirable; as Bill McKibben wrote in Eaarth, we’d probably be better off with the Fortune 500,000 than the Fortune 500. Scale brings with it the ills of Seeing Like a State; the authoritarian high modernist mindset takes over at large scale. And while large organizations can exist, they can’t be the only ones with access to, or ability to, afford new technologies. Enabling the dynamic creation and destruction of new organizations and new types of organization—and legal and technical mechanisms to prevent lock-in and to prevent enclosure of public commons—will be essential to keep this new fluid era thriving. We can create new “federated” networks of organizations and social groups, like we’re seeing in the open social web of Mastodon and similar technologies, ones where local groups can have local rules that differ from, but do not conflict with, their participation in the wider whole.

This shift is not just about how society will work but also how we see ourselves. We’re all getting a bit more used to the idea of having multiple identities, and some of us have gotten used to having a “portfolio career” that is not defined by a single hat that we wear. While today there is often economic precarity involved with this way of living, there need not be, and the more we can all do the things that are the best expressions of ourselves, the better off society will be.

Ahead

As Mumford wrote in his classic history of technology, “The essential distinction between a machine and a tool lies in the degree of independence in the operation from the skill and motive power of the operator.” A tool is controlled by a human user, whereas a machine does what its designer wanted. As technologists, we can build tools, rather than machines, that flexibly allow people to make partial, contextual sense of the online and physical world around them. As citizens, we can create meaningful organizations that span our communities but without the permanence (and thus overhead) of old-school organizations.

Seeing like a data structure has been both a blessing and a curse. Increasingly, it feels like it is an avalanche, an out-of-control force that will reshape everything in its path. But it’s also a choice, and there is a different path we can take. The job of enabling a new society, one that accepts the complexity and messiness of our current world without being overwhelmed by it, is one all of us can take part it. There is a different future we can build, together.

This essay was written with Barath Raghavan, and originally appeared on the Harvard Kennedy School Belfer Center‘s website.

A piece I coauthored with Fredrik Heiding and Arun Vishwanath in the Harvard Business Review:

Summary. Gen AI tools are rapidly making these emails more advanced, harder to spot, and significantly more dangerous. Recent research showed that 60% of participants fell victim to artificial intelligence (AI)-automated phishing, which is comparable to the success rates of non-AI-phishing messages created by human experts. Companies need to: 1) understand the asymmetrical capabilities of AI-enhanced phishing, 2) determine the company or division’s phishing threat severity level, and 3) confirm their current phishing awareness routines.

Here’s the full text.

This video might be a juvenile colossal squid.

As usual, you can also use this squid post to talk about the security stories in the news that I haven’t covered.

Read my blog posting guidelines here.

I don’t think it’s an exaggeration to predict that artificial intelligence will affect every aspect of our society. Not by doing new things. But mostly by doing things that are already being done by humans, perfectly competently.

Replacing humans with AIs isn’t necessarily interesting. But when an AI takes over a human task, the task changes.

In particular, there are potential changes over four dimensions: Speed, scale, scope and sophistication. The problem with AIs trading stocks isn’t that they’re better than humans—it’s that they’re faster. But computers are better at chess and Go because they use more sophisticated strategies than humans. We’re worried about AI-controlled social media accounts because they operate on a superhuman scale.

It gets interesting when changes in degree can become changes in kind. High-speed trading is fundamentally different than regular human trading. AIs have invented fundamentally new strategies in the game of Go. Millions of AI-controlled social media accounts could fundamentally change the nature of propaganda.

It’s these sorts of changes and how AI will affect democracy that I want to talk about.

To start, I want to list some of AI’s core competences. First, it is really good as a summarizer. Second, AI is good at explaining things, teaching with infinite patience. Third, and related, AI can persuade. Propaganda is an offshoot of this. Fourth, AI is fundamentally a prediction technology. Predictions about whether turning left or right will get you to your destination faster. Predictions about whether a tumor is cancerous might improve medical diagnoses. Predictions about which word is likely to come next can help compose an email. Fifth, AI can assess. Assessing requires outside context and criteria. AI is less good at assessing, but it’s getting better. Sixth, AI can decide. A decision is a prediction plus an assessment. We are already using AI to make all sorts of decisions.

How these competences translate to actual useful AI systems depends a lot on the details. We don’t know how far AI will go in replicating or replacing human cognitive functions. Or how soon that will happen. In constrained environments it can be easy. AIs already play chess and Go better than humans. Unconstrained environments are harder. There are still significant challenges to fully AI-piloted automobiles. The technologist Jaron Lanier has a nice quote, that AI does best when “human activities have been done many times before, but not in exactly the same way.”

In this talk, I am going to be largely optimistic about the technology. I’m not going to dwell on the details of how the AI systems might work. Much of what I am talking about is still in the future. Science fiction, but not unrealistic science fiction.

Where I am going to be less optimistic—and more realistic—is about the social implications of the technology. Again, I am less interested in how AI will substitute for humans. I’m looking more at the second-order effects of those substitutions: How the underlying systems will change because of changes in speed, scale, scope and sophistication. My goal is to imagine the possibilities. So that we might be prepared for their eventuality.

And as I go through the possibilities, keep in mind a few questions: Will the change distribute or consolidate power? Will it make people more or less personally involved in democracy? What needs to happen before people will trust AI in this context? What could go wrong if a bad actor subverted the AI in this context? And what can we do, as security technologists, to help?

I am thinking about democracy very broadly. Not just representations, or elections. Democracy as a system for distributing decisions evenly across a population. It’s a way of converting individual preferences into group decisions. And that includes bureaucratic decisions.

To that end, I want to discuss five different areas where AI will affect democracy: Politics, lawmaking, administration, the legal system and, finally, citizens themselves.

I: AI-assisted politicians

I’ve already said that AIs are good at persuasion. Politicians will make use of that. Pretty much everyone talks about AI propaganda. Politicians will make use of that, too. But let’s talk about how this might go well.

In the past, candidates would write books and give speeches to connect with voters. In the future, candidates will also use personalized chatbots to directly engage with voters on a variety of issues. AI can also help fundraise. I don’t have to explain the persuasive power of individually crafted appeals. AI can conduct polls. There’s some really interesting work into having large language models assume different personas and answer questions from their points of view. Unlike people, AIs are always available, will answer thousands of questions without getting tired or bored and are more reliable. This won’t replace polls, but it can augment them. AI can assist human campaign managers by coordinating campaign workers, creating talking points, doing media outreach and assisting get-out-the-vote efforts. These are all things that humans already do. So there’s no real news there.

The changes are largely in scale. AIs can engage with voters, conduct polls and fundraise at a scale that humans cannot—for all sizes of elections. They can also assist in lobbying strategies. AIs could also potentially develop more sophisticated campaign and political strategies than humans can. I expect an arms race as politicians start using these sorts of tools. And we don’t know if the tools will favor one political ideology over another.

More interestingly, future politicians will largely be AI-driven. I don’t mean that AI will replace humans as politicians. Absent a major cultural shift—and some serious changes in the law—that won’t happen. But as AI starts to look and feel more human, our human politicians will start to look and feel more like AI. I think we will be OK with it, because it’s a path we’ve been walking down for a long time. Any major politician today is just the public face of a complex socio-technical system. When the president makes a speech, we all know that they didn’t write it. When a legislator sends out a campaign email, we know that they didn’t write that either—even if they signed it. And when we get a holiday card from any of these people, we know that it was signed by an autopen. Those things are so much a part of politics today that we don’t even think about it. In the future, we’ll accept that almost all communications from our leaders will be written by AI. We’ll accept that they use AI tools for making political and policy decisions. And for planning their campaigns. And for everything else they do. None of this is necessarily bad. But it does change the nature of politics and politicians—just like television and the internet did.

II: AI-assisted legislators

AIs are already good at summarization. This can be applied to listening to constituents:  summarizing letters, comments and making sense of constituent inputs. Public meetings might be summarized. Here the scale of the problem is already overwhelming, and AI can make a big difference. Beyond summarizing, AI can highlight interesting arguments or detect bulk letter-writing campaigns. They can aid in political negotiating.

AIs can also write laws. In November 2023, Porto Alegre, Brazil became the first city to enact a law that was entirely written by AI. It had to do with water meters. One of the councilmen prompted ChatGPT, and it produced a complete bill. He submitted it to the legislature without telling anyone who wrote it. And the humans passed it without any changes.

A law is just a piece of generated text that a government agrees to adopt. And as with every other profession, policymakers will turn to AI to help them draft and revise text. Also, AI can take human-written laws and figure out what they actually mean. Lots of laws are recursive, referencing paragraphs and words of other laws. AIs are already good at making sense of all that.

This means that AI will be good at finding legal loopholes—or at creating legal loopholes. I wrote about this in my latest book, A Hacker’s Mind. Finding loopholes is similar to finding vulnerabilities in software. There’s also a concept called “micro-legislation.” That’s the smallest unit of law that makes a difference to someone. It could be a word or a punctuation mark. AIs will be good at inserting micro-legislation into larger bills. More positively, AI can help figure out unintended consequences of a policy change—by simulating how the change interacts with all the other laws and with human behavior.

AI can also write more complex law than humans can. Right now, laws tend to be general. With details to be worked out by a government agency. AI can allow legislators to propose, and then vote on, all of those details. That will change the balance of power between the legislative and the executive branches of government. This is less of an issue when the same party controls the executive and the legislative branches. It is a big deal when those branches of government are in the hands of different parties. The worry is that AI will give the most powerful groups more tools for propagating their interests.

AI can write laws that are impossible for humans to understand. There are two kinds of laws: specific laws, like speed limits, and laws that require judgment, like those that address reckless driving. Imagine that we train an AI on lots of street camera footage to recognize reckless driving and that it gets better than humans at identifying the sort of behavior that tends to result in accidents. And because it has real-time access to cameras everywhere, it can spot it … everywhere. The AI won’t be able to explain its criteria: It would be a black-box neural net. But we could pass a law defining reckless driving by what that AI says. It would be a law that no human could ever understand. This could happen in all sorts of areas where judgment is part of defining what is illegal. We could delegate many things to the AI because of speed and scale. Market manipulation. Medical malpractice. False advertising. I don’t know if humans will accept this.

III: AI-assisted bureaucracy

Generative AI is already good at a whole lot of administrative paperwork tasks. It will only get better. I want to focus on a few places where it will make a big difference. It could aid in benefits administration—figuring out who is eligible for what. Humans do this today, but there is often a backlog because there aren’t enough humans. It could audit contracts. It could operate at scale, auditing all human-negotiated government contracts. It could aid in contracts negotiation. The government buys a lot of things and has all sorts of complicated rules. AI could help government contractors navigate those rules.

More generally, it could aid in negotiations of all kinds. Think of it as a strategic adviser. This is no different than a human but could result in more complex negotiations. Human negotiations generally center around only a few issues. Mostly because that’s what humans can keep in mind. AI versus AI negotiations could potentially involve thousands of variables simultaneously. Imagine we are using an AI to aid in some international trade negotiation and it suggests a complex strategy that is beyond human understanding. Will we blindly follow the AI? Will we be more willing to do so once we have some history with its accuracy?

And one last bureaucratic possibility: Could AI come up with better institutional designs than we have today? And would we implement them?

IV: AI-assisted legal system

When referring to an AI-assisted legal system, I mean this very broadly—both lawyering and judging and all the things surrounding those activities.

AIs can be lawyers. Early attempts at having AIs write legal briefs didn’t go well. But this is already changing as the systems get more accurate. Chatbots are now able to properly cite their sources and minimize errors. Future AIs will be much better at writing legalese, drastically reducing the cost of legal counsel. And there’s every indication that it will be able to do much of the routine work that lawyers do. So let’s talk about what this means.

Most obviously, it reduces the cost of legal advice and representation, giving it to people who currently can’t afford it. An AI public defender is going to be a lot better than an overworked not very good human public defender. But if we assume that human-plus-AI beats AI-only, then the rich get the combination, and the poor are stuck with just the AI.

It also will result in more sophisticated legal arguments. AI’s ability to search all of the law for precedents to bolster a case will be transformative.

AI will also change the meaning of a lawsuit. Right now, suing someone acts as a strong social signal because of the cost. If the cost drops to free, that signal will be lost. And orders of magnitude more lawsuits will be filed, which will overwhelm the court system.

Another effect could be gutting the profession. Lawyering is based on apprenticeship. But if most of the apprentice slots are filled by AIs, where do newly minted attorneys go to get training? And then where do the top human lawyers come from? This might not happen. AI-assisted lawyers might result in more human lawyering. We don’t know yet.

AI can help enforce the law. In a sense, this is nothing new. Automated systems already act as law enforcement—think speed trap cameras and Breathalyzers. But AI can take this kind of thing much further, like automatically identifying people who cheat on tax returns, identifying fraud on government service applications and watching all of the traffic cameras and issuing citations.

Again, the AI is performing a task for which we don’t have enough humans. And doing it faster, and at scale. This has the obvious problem of false positives. Which could be hard to contest if the courts believe that the computer is always right. This is a thing today: If a Breathalyzer says you’re drunk, it can be hard to contest the software in court. And also the problem of bias, of course: AI law enforcers may be more and less equitable than their human predecessors.

But most importantly, AI changes our relationship with the law. Everyone commits driving violations all the time. If we had a system of automatic enforcement, the way we all drive would change—significantly. Not everyone wants this future. Lots of people don’t want to fund the IRS, even though catching tax cheats is incredibly profitable for the government. And there are legitimate concerns as to whether this would be applied equitably.

AI can help enforce regulations. We have no shortage of rules and regulations. What we have is a shortage of time, resources and willpower to enforce them, which means that lots of companies know that they can ignore regulations with impunity. AI can change this by decoupling the ability to enforce rules from the resources necessary to do it. This makes enforcement more scalable and efficient. Imagine putting cameras in every slaughterhouse in the country looking for animal welfare violations or fielding an AI in every warehouse camera looking for labor violations. That could create an enormous shift in the balance of power between government and corporations—which means that it will be strongly resisted by corporate power.

AIs can provide expert opinions in court. Imagine an AI trained on millions of traffic accidents, including video footage, telemetry from cars and previous court cases. The AI could provide the court with a reconstruction of the accident along with an assignment of fault. AI could do this in a lot of cases where there aren’t enough human experts to analyze the data—and would do it better, because it would have more experience.

AIs can also perform judging tasks, weighing evidence and making decisions, probably not in actual courtrooms, at least not anytime soon, but in other contexts. There are many areas of government where we don’t have enough adjudicators. Automated adjudication has the potential to offer everyone immediate justice. Maybe the AI does the first level of adjudication and humans handle appeals. Probably the first place we’ll see this is in contracts. Instead of the parties agreeing to binding arbitration to resolve disputes, they’ll agree to binding arbitration by AI. This would significantly decrease cost of arbitration. Which would probably significantly increase the number of disputes.

So, let’s imagine a world where dispute resolution is both cheap and fast. If you and I are business partners, and we have a disagreement, we can get a ruling in minutes. And we can do it as many times as we want—multiple times a day, even. Will we lose the ability to disagree and then resolve our disagreements on our own? Or will this make it easier for us to be in a partnership and trust each other?

V: AI-assisted citizens

AI can help people understand political issues by explaining them. We can imagine both partisan and nonpartisan chatbots. AI can also provide political analysis and commentary. And it can do this at every scale. Including for local elections that simply aren’t important enough to attract human journalists. There is a lot of research going on right now on AI as moderator, facilitator, and consensus builder. Human moderators are still better, but we don’t have enough human moderators. And AI will improve over time. AI can moderate at scale, giving the capability to every decision-making group—or chatroom—or local government meeting.

AI can act as a government watchdog. Right now, much local government effectively happens in secret because there are no local journalists covering public meetings. AI can change that, providing summaries and flagging changes in position.

AIs can help people navigate bureaucracies by filling out forms, applying for services and contesting bureaucratic actions. This would help people get the services they deserve, especially disadvantaged people who have difficulty navigating these systems. Again, this is a task that we don’t have enough qualified humans to perform. It sounds good, but not everyone wants this. Administrative burdens can be deliberate.

Finally, AI can eliminate the need for politicians. This one is further out there, but bear with me. Already there is research showing AI can extrapolate our political preferences. An AI personal assistant trained on and continuously attuned to your political preferences could advise you, including what to support and who to vote for. It could possibly even vote on your behalf or, more interestingly, act as your personal representative.

This is where it gets interesting. Our system of representative democracy empowers elected officials to stand in for our collective preferences. But that has obvious problems. Representatives are necessary because people don’t pay attention to politics. And even if they did, there isn’t enough room in the debate hall for everyone to fit. So we need to pick one of us to pass laws in our name. But that selection process is incredibly inefficient. We have complex policy wants and beliefs and can make complex trade-offs. The space of possible policy outcomes is equally complex. But we can’t directly debate the policies. We can only choose one of two—or maybe a few more—candidates to do that for us. This has been called democracy’s “lossy bottleneck.” AI can change this. We can imagine a personal AI directly participating in policy debates on our behalf along with millions of other personal AIs and coming to a consensus on policy.

More near term, AIs can result in more ballot initiatives. Instead of five or six, there might be five or six hundred, as long as the AI can reliably advise people on how to vote. It’s hard to know whether this is a good thing. I don’t think we want people to become politically passive because the AI is taking care of it. But it could result in more legislation that the majority actually wants.

Where will AI take us?

That’s my list. Again, watch where changes of degree result in changes in kind. The sophistication of AI lawmaking will mean more detailed laws, which will change the balance of power between the executive and the legislative branches. The scale of AI lawyering means that litigation becomes affordable to everyone, which will mean an explosion in the amount of litigation. The speed of AI adjudication means that contract disputes will get resolved much faster, which will change the nature of settlements. The scope of AI enforcement means that some laws will become impossible to evade, which will change how the rich and powerful think about them.

I think this is all coming. The time frame is hazy, but the technology is moving in these directions.

All of these applications need security of one form or another. Can we provide confidentiality, integrity and availability where it is needed? AIs are just computers. As such, they have all the security problems regular computers have—plus the new security risks stemming from AI and the way it is trained, deployed and used. Like everything else in security, it depends on the details.

First, the incentives matter. In some cases, the user of the AI wants it to be both secure and accurate. In some cases, the user of the AI wants to subvert the system. Think about prompt injection attacks. In most cases, the owners of the AIs aren’t the users of the AI. As happened with search engines and social media, surveillance and advertising are likely to become the AI’s business model. And in some cases, what the user of the AI wants is at odds with what society wants.

Second, the risks matter. The cost of getting things wrong depends a lot on the application. If a candidate’s chatbot suggests a ridiculous policy, that’s easily corrected. If an AI is helping someone fill out their immigration paperwork, a mistake can get them deported. We need to understand the rate of AI mistakes versus the rate of human mistakes—and also realize that AI mistakes are viewed differently than human mistakes. There are also different types of mistakes: false positives versus false negatives. But also, AI systems can make different kinds of mistakes than humans do—and that’s important. In every case, the systems need to be able to correct mistakes, especially in the context of democracy.

Many of the applications are in adversarial environments. If two countries are using AI to assist in trade negotiations, they are both going to try to hack each other’s AIs. This will include attacks against the AI models but also conventional attacks against the computers and networks that are running the AIs. They’re going to want to subvert, eavesdrop on or disrupt the other’s AI.

Some AI applications will need to run in secure environments. Large language models work best when they have access to everything, in order to train. That goes against traditional classification rules about compartmentalization.

Fourth, power matters. AI is a technology that fundamentally magnifies power of the humans who use it, but not equally across users or applications. Can we build systems that reduce power imbalances rather than increase them? Think of the privacy versus surveillance debate in the context of AI.

And similarly, equity matters. Human agency matters.

And finally, trust matters. Whether or not to trust an AI is less about the AI and more about the application. Some of these AI applications are individual. Some of these applications are societal. Whether something like “fairness” matters depends on this. And there are many competing definitions of fairness that depend on the details of the system and the application. It’s the same with transparency. The need for it depends on the application and the incentives. Democratic applications are likely to require more transparency than corporate ones and probably AI models that are not owned and run by global tech monopolies.

All of these security issues are bigger than AI or democracy. Like all of our security experience, applying it to these new systems will require some new thinking.

AI will be one of humanity’s most important inventions. That’s probably true. What we don’t know is if this is the moment we are inventing it. Or if today’s systems are yet more over-hyped technologies. But these are security conversations we are going to need to have eventually.

AI is fundamentally a power-enhancing technology. We need to ensure that it distributes power and doesn’t further concentrate it.

AI is coming for democracy. Whether the changes are a net positive or negative depends on us. Let’s help tilt things to the positive.

This essay is adapted from a keynote speech delivered at the RSA Conference in San Francisco on May 7, 2024. It originally appeared in Cyberscoop.

No word on how this backdoor was installed:

A software maker serving more than 10,000 courtrooms throughout the world hosted an application update containing a hidden backdoor that maintained persistent communication with a malicious website, researchers reported Thursday, in the latest episode of a supply-chain attack.

The software, known as the JAVS Viewer 8, is a component of the JAVS Suite 8, an application package courtrooms use to record, play back, and manage audio and video from proceedings. Its maker, Louisville, Kentucky-based Justice AV Solutions, says its products are used in more than 10,000 courtrooms throughout the US and 11 other countries. The company has been in business for 35 years.

It’s software used by courts; we can imagine all sort of actors who want to backdoor it.

Brian Krebs reports on research into geolocating routers:

Apple and the satellite-based broadband service Starlink each recently took steps to address new research into the potential security and privacy implications of how their services geolocate devices. Researchers from the University of Maryland say they relied on publicly available data from Apple to track the location of billions of devices globally—including non-Apple devices like Starlink systems—and found they could use this data to monitor the destruction of Gaza, as well as the movements and in many cases identities of Russian and Ukrainian troops.

Really fascinating implications to this research.

Research paper: “Surveilling the Masses with Wi-Fi-Based Positioning Systems:

Abstract: Wi-Fi-based Positioning Systems (WPSes) are used by modern mobile devices to learn their position using nearby Wi-Fi access points as landmarks. In this work, we show that Apple’s WPS can be abused to create a privacy threat on a global scale. We present an attack that allows an unprivileged attacker to amass a worldwide snapshot of Wi-Fi BSSID geolocations in only a matter of days. Our attack makes few assumptions, merely exploiting the fact that there are relatively few dense regions of allocated MAC address space. Applying this technique over the course of a year, we learned the precise
locations of over 2 billion BSSIDs around the world.

The privacy implications of such massive datasets become more stark when taken longitudinally, allowing the attacker to track devices’ movements. While most Wi-Fi access points do not move for long periods of time, many devices—like compact travel routers—are specifically designed to be mobile.

We present several case studies that demonstrate the types of attacks on privacy that Apple’s WPS enables: We track devices moving in and out of war zones (specifically Ukraine and Gaza), the effects of natural disasters (specifically the fires in Maui), and the possibility of targeted individual tracking by proxy—all by remotely geolocating wireless access points.

We provide recommendations to WPS operators and Wi-Fi access point manufacturers to enhance the privacy of hundreds of millions of users worldwide. Finally, we detail our efforts at responsibly disclosing this privacy vulnerability, and outline some mitigations that Apple and Wi-Fi access point manufacturers have implemented both independently and as a result of our work.

Quantum computers are probably coming, though we don’t know when—and when they arrive, they will, most likely, be able to break our standard public-key cryptography algorithms. In anticipation of this possibility, cryptographers have been working on quantum-resistant public-key algorithms. The National Institute for Standards and Technology (NIST) has been hosting a competition since 2017, and there already are several proposed standards. Most of these are based on lattice problems.

The mathematics of lattice cryptography revolve around combining sets of vectors—that’s the lattice—in a multi-dimensional space. These lattices are filled with multi-dimensional periodicities. The hard problem that’s used in cryptography is to find the shortest periodicity in a large, random-looking lattice. This can be turned into a public-key cryptosystem in a variety of different ways. Research has been ongoing since 1996, and there has been some really great work since then—including many practical public-key algorithms.

On April 10, Yilei Chen from Tsinghua University in Beijing posted a paper describing a new quantum attack on that shortest-path lattice problem. It’s a very dense mathematical paper—63 pages long—and my guess is that only a few cryptographers are able to understand all of its details. (I was not one of them.) But the conclusion was pretty devastating, breaking essentially all of the lattice-based fully homomorphic encryption schemes and coming significantly closer to attacks against the recently proposed (and NIST-approved) lattice key-exchange and signature schemes.

However, there was a small but critical mistake in the paper, on the bottom of page 37. It was independently discovered by Hongxun Wu from Berkeley and Thomas Vidick from the Weizmann Institute in Israel eight days later. The attack algorithm in its current form doesn’t work.

This was discussed last week at the Cryptographers’ Panel at the RSA Conference. Adi Shamir, the “S” in RSA and a 2002 recipient of ACM’s A.M. Turing award, described the result as psychologically significant because it shows that there is still a lot to be discovered about quantum cryptanalysis of lattice-based algorithms. Craig Gentry—inventor of the first fully homomorphic encryption scheme using lattices—was less impressed, basically saying that a nonworking attack doesn’t change anything.

I tend to agree with Shamir. There have been decades of unsuccessful research into breaking lattice-based systems with classical computers; there has been much less research into quantum cryptanalysis. While Chen’s work doesn’t provide a new security bound, it illustrates that there are significant, unexplored research areas in the construction of efficient quantum attacks on lattice-based cryptosystems. These lattices are periodic structures with some hidden periodicities. Finding a different (one-dimensional) hidden periodicity is exactly what enabled Peter Shor to break the RSA algorithm in polynomial time on a quantum computer. There are certainly more results to be discovered. This is the kind of paper that galvanizes research, and I am excited to see what the next couple of years of research will bring.

To be fair, there are lots of difficulties in making any quantum attack work—even in theory.

Breaking lattice-based cryptography with a quantum computer seems to require orders of magnitude more qubits than breaking RSA, because the key size is much larger and processing it requires more quantum storage. Consequently, testing an algorithm like Chen’s is completely infeasible with current technology. However, the error was mathematical in nature and did not require any experimentation. Chen’s algorithm consisted of nine different steps; the first eight prepared a particular quantum state, and the ninth step was supposed to exploit it. The mistake was in step nine; Chen believed that his wave function was periodic when in fact it was not.

Should NIST be doing anything differently now in its post–quantum cryptography standardization process? The answer is no. They are doing a great job in selecting new algorithms and should not delay anything because of this new research. And users of cryptography should not delay in implementing the new NIST algorithms.

But imagine how different this essay would be were that mistake not yet discovered? If anything, this work emphasizes the need for systems to be crypto-agile: to be able to easily swap algorithms in and out as research continues. And for using hybrid cryptography—multiple algorithms where the security rests on the strongest—where possible, as in TLS.

And—one last point—hooray for peer review. A researcher proposed a new result, and reviewers quickly found a fatal flaw in the work. Efforts to repair the flaw are ongoing. We complain about peer review a lot, but here it worked exactly the way it was supposed to.

This essay originally appeared in Communications of the ACM.

Fantastic footage of a Dana squid attacking a camera at a depth of about a kilometer.

As usual, you can also use this squid post to talk about the security stories in the news that I haven’t covered.

Read my blog posting guidelines here.

New paper: “Zero Progress on Zero Days: How the Last Ten Years Created the Modern Spyware Market“:

Abstract: Spyware makes surveillance simple. The last ten years have seen a global market emerge for ready-made software that lets governments surveil their citizens and foreign adversaries alike and to do so more easily than when such work required tradecraft. The last ten years have also been marked by stark failures to control spyware and its precursors and components. This Article accounts for and critiques these failures, providing a socio-technical history since 2014, particularly focusing on the conversation about trade in zero-day vulnerabilities and exploits. Second, this Article applies lessons from these failures to guide regulatory efforts going forward. While recognizing that controlling this trade is difficult, I argue countries should focus on building and strengthening multilateral coalitions of the willing, rather than on strong-arming existing multilateral institutions into working on the problem. Individually, countries should focus on export controls and other sanctions that target specific bad actors, rather than focusing on restricting particular technologies. Last, I continue to call for transparency as a key part of oversight of domestic governments’ use of spyware and related components.

Microsoft is trying to create a personal digital assistant:

At a Build conference event on Monday, Microsoft revealed a new AI-powered feature called “Recall” for Copilot+ PCs that will allow Windows 11 users to search and retrieve their past activities on their PC. To make it work, Recall records everything users do on their PC, including activities in apps, communications in live meetings, and websites visited for research. Despite encryption and local storage, the new feature raises privacy concerns for certain Windows users.

I wrote about this AI trust problem last year:

One of the promises of generative AI is a personal digital assistant. Acting as your advocate with others, and as a butler with you. This requires an intimacy greater than your search engine, email provider, cloud storage system, or phone. You’re going to want it with you 24/7, constantly training on everything you do. You will want it to know everything about you, so it can most effectively work on your behalf.

And it will help you in many ways. It will notice your moods and know what to suggest. It will anticipate your needs and work to satisfy them. It will be your therapist, life coach, and relationship counselor.

You will default to thinking of it as a friend. You will speak to it in natural language, and it will respond in kind. If it is a robot, it will look humanoid—­or at least like an animal. It will interact with the whole of your existence, just like another person would.

[…]

And you will want to trust it. It will use your mannerisms and cultural references. It will have a convincing voice, a confident tone, and an authoritative manner. Its personality will be optimized to exactly what you like and respond to.

It will act trustworthy, but it will not be trustworthy. We won’t know how they are trained. We won’t know their secret instructions. We won’t know their biases, either accidental or deliberate.

We do know that they are built at enormous expense, mostly in secret, by profit-maximizing corporations for their own benefit.

[…]

All of this is a long-winded way of saying that we need trustworthy AI. AI whose behavior, limitations, and training are understood. AI whose biases are understood, and corrected for. AI whose goals are understood. That won’t secretly betray your trust to someone else.

The market will not provide this on its own. Corporations are profit maximizers, at the expense of society. And the incentives of surveillance capitalism are just too much to resist.

We are going to need some sort of public AI to counterbalance all of these corporate AIs.

EDITED TO ADD (5/24): Lots of comments about Microsoft Recall and security:

This:

Because Recall is “default allow” (it relies on a list of things not to record) … it’s going to vacuum up huge volumes and heretofore unknown types of data, most of which are ephemeral today. The “we can’t avoid saving passwords if they’re not masked” warning Microsoft included is only the tip of that iceberg. There’s an ocean of data that the security ecosystem assumes is “out of reach” because it’s either never stored, or it’s encrypted in transit. All of that goes out the window if the endpoint is just going to…turn around and write it to disk. (And local encryption at rest won’t help much here if the data is queryable in the user’s own authentication context!)

This:

The fact that Microsoft’s new Recall thing won’t capture DRM content means the engineers do understand the risk of logging everything. They just chose to preference the interests of corporates and money over people, deliberately.

This:

Microsoft Recall is going to make post-breach impact analysis impossible. Right now IR processes can establish a timeline of data stewardship to identify what information may have been available to an attacker based on the level of access they obtained. It’s not trivial work, but IR folks can do it. Once a system with Recall is compromised, all data that has touched that system is potentially compromised too, and the ML indirection makes it near impossible to confidently identify a blast radius.

This:

You may be in a position where leaders in your company are hot to turn on Microsoft Copilot Recall. Your best counterargument isn’t threat actors stealing company data. It’s that opposing counsel will request the recall data and demand it not be disabled as part of e-discovery proceedings.

Experiments in unredacting text that has been pixelated.

From Slashdot:

Apple and Google have launched a new industry standard called “Detecting Unwanted Location Trackers” to combat the misuse of Bluetooth trackers for stalking. Starting Monday, iPhone and Android users will receive alerts when an unknown Bluetooth device is detected moving with them. The move comes after numerous cases of trackers like Apple’s AirTags being used for malicious purposes.

Several Bluetooth tag companies have committed to making their future products compatible with the new standard. Apple and Google said they will continue collaborating with the Internet Engineering Task Force to further develop this technology and address the issue of unwanted tracking.

This seems like a good idea, but I worry about false alarms. If I am walking with a friend, will it alert if they have a Bluetooth tracking device in their pocket?

IBM is selling its QRadar product suite to Palo Alto Networks, for an undisclosed—but probably surprisingly small—sum.

I have a personal connection to this. In 2016, IBM bought Resilient Systems, the startup I was a part of. It became part if IBM’s cybersecurity offerings, mostly and weirdly subservient to QRadar.

That was what seemed to be the problem at IBM. QRadar was IBM’s first acquisition in the cybersecurity space, and it saw everything through the lens of that SIEM system. I left the company two years after the acquisition, and near as I could tell, it never managed to figure the space out.

So now it’s Palo Alto’s turn.

When asked what makes this an “emotional support squid” and not just another stuffed animal, its creator says:

They’re emotional support squid because they’re large, and cuddly, but also cheerfully bright and derpy. They make great neck pillows (and you can fidget with the arms and tentacles) for travelling, and, on a more personal note, when my mum was sick in the hospital I gave her one and she said it brought her “great comfort” to have her squid tucked up beside her and not be a nuisance while she was sleeping.

As usual, you can also use this squid post to talk about the security stories in the news that I haven’t covered.

Read my blog posting guidelines here.

The FBI has seized the BreachForums website, used by ransomware criminals to leak stolen corporate data.

If law enforcement has gained access to the hacking forum’s backend data, as they claim, they would have email addresses, IP addresses, and private messages that could expose members and be used in law enforcement investigations.

[…]

The FBI is requesting victims and individuals contact them with information about the hacking forum and its members to aid in their investigation.

The seizure messages include ways to contact the FBI about the seizure, including an email, a Telegram account, a TOX account, and a dedicated page hosted on the FBI’s Internet Crime Complaint Center (IC3).

“The Federal Bureau of Investigation (FBI) is investigating the criminal hacking forums known as BreachForums and Raidforums,” reads a dedicated subdomain on the FBI’s IC3 portal.

“From June 2023 until May 2024, BreachForums (hosted at breachforums.st/.cx/.is/.vc and run by ShinyHunters) was operating as a clear-net marketplace for cybercriminals to buy, sell, and trade contraband, including stolen access devices, means of identification, hacking tools, breached databases, and other illegal services.”

“Previously, a separate version of BreachForums (hosted at breached.vc/.to/.co and run by pompompurin) operated a similar hacking forum from March 2022 until March 2023. Raidforums (hosted at raidforums.com and run by Omnipotent) was the predecessor hacking forum to both version of BreachForums and ran from early 2015 until February 2022.”

Microsoft is working on a promising-looking protocol to lock down DNS.

ZTDNS aims to solve this decades-old problem by integrating the Windows DNS engine with the Windows Filtering Platform—the core component of the Windows Firewall—directly into client devices.

Jake Williams, VP of research and development at consultancy Hunter Strategy, said the union of these previously disparate engines would allow updates to be made to the Windows firewall on a per-domain name basis. The result, he said, is a mechanism that allows organizations to, in essence, tell clients “only use our DNS server, that uses TLS, and will only resolve certain domains.” Microsoft calls this DNS server or servers the “protective DNS server.”

By default, the firewall will deny resolutions to all domains except those enumerated in allow lists. A separate allow list will contain IP address subnets that clients need to run authorized software. Key to making this work at scale inside an organization with rapidly changing needs. Networking security expert Royce Williams (no relation to Jake Williams) called this a “sort of a bidirectional API for the firewall layer, so you can both trigger firewall actions (by input *to* the firewall), and trigger external actions based on firewall state (output *from* the firewall). So instead of having to reinvent the firewall wheel if you are an AV vendor or whatever, you just hook into WFP.”

This is a current list of where and when I am scheduled to speak:

• I’m giving a webinar via Zoom on Wednesday, May 22, at 11:00 AM ET. The topic is “Should the USG Establish a Publicly Funded AI Option?“

The list is maintained on this page.

Google has patched another Chrome zero-day:

On Thursday, Google said an anonymous source notified it of the vulnerability. The vulnerability carries a severity rating of 8.8 out of 10. In response, Google said, it would be releasing versions 124.0.6367.201/.202 for macOS and Windows and 124.0.6367.201 for Linux in subsequent days.

“Google is aware that an exploit for CVE-2024-4671 exists in the wild,” the company said.

Google didn’t provide any other details about the exploit, such as what platforms were targeted, who was behind the exploit, or what they were using it for.

Some squids are “consorts,” others are “sneakers.” The species is healthiest when individuals have different strategies randomly.

As usual, you can also use this squid post to talk about the security stories in the news that I haven’t covered.

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This is another attack that convinces the AI to ignore road signs:

Due to the way CMOS cameras operate, rapidly changing light from fast flashing diodes can be used to vary the color. For example, the shade of red on a stop sign could look different on each line depending on the time between the diode flash and the line capture.

The result is the camera capturing an image full of lines that don’t quite match each other. The information is cropped and sent to the classifier, usually based on deep neural networks, for interpretation. Because it’s full of lines that don’t match, the classifier doesn’t recognize the image as a traffic sign.

So far, all of this has been demonstrated before.

Yet these researchers not only executed on the distortion of light, they did it repeatedly, elongating the length of the interference. This meant an unrecognizable image wasn’t just a single anomaly among many accurate images, but rather a constant unrecognizable image the classifier couldn’t assess, and a serious security concern.

[…]

The researchers developed two versions of a stable attack. The first was GhostStripe1, which is not targeted and does not require access to the vehicle, we’re told. It employs a vehicle tracker to monitor the victim’s real-time location and dynamically adjust the LED flickering accordingly.

GhostStripe2 is targeted and does require access to the vehicle, which could perhaps be covertly done by a hacker while the vehicle is undergoing maintenance. It involves placing a transducer on the power wire of the camera to detect framing moments and refine timing control.

Research paper.

There’s a new report on how criminals are using generative AI tools:

Key Takeaways:

• Adoption rates of AI technologies among criminals lag behind the rates of their industry counterparts because of the evolving nature of cybercrime.
• Compared to last year, criminals seem to have abandoned any attempt at training real criminal large language models (LLMs). Instead, they are jailbreaking existing ones.
• We are finally seeing the emergence of actual criminal deepfake services, with some bypassing user verification used in financial services.

This attack has been feasible for over two decades:

Researchers have devised an attack against nearly all virtual private network applications that forces them to send and receive some or all traffic outside of the encrypted tunnel designed to protect it from snooping or tampering.

TunnelVision, as the researchers have named their attack, largely negates the entire purpose and selling point of VPNs, which is to encapsulate incoming and outgoing Internet traffic in an encrypted tunnel and to cloak the user’s IP address. The researchers believe it affects all VPN applications when they’re connected to a hostile network and that there are no ways to prevent such attacks except when the user’s VPN runs on Linux or Android. They also said their attack technique may have been possible since 2002 and may already have been discovered and used in the wild since then.

[…]

The attack works by manipulating the DHCP server that allocates IP addresses to devices trying to connect to the local network. A setting known as option 121 allows the DHCP server to override default routing rules that send VPN traffic through a local IP address that initiates the encrypted tunnel. By using option 121 to route VPN traffic through the DHCP server, the attack diverts the data to the DHCP server itself.

Lots of complicated details here: too many for me to summarize well. It involves an obscure Section 230 provision—and an even more obscure typo. Read this.

Squid-shaped purses for sale.

As usual, you can also use this squid post to talk about the security stories in the news that I haven’t covered.

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I have spoken at several TED conferences over the years.

• TEDxPSU 2010: “Reconceptualizing Security”
• TEDxCambridge 2013: “The Battle for Power on the Internet”
• TEDMed 2016: “Who Controls Your Medical Data?”

I’m putting this here because I want all three links in one place.

The Polish Embassy has posted a series of short interview segments with Marian Rejewski, the first person to crack the Enigma.

Details from his biography.

The UK is the first country to ban default passwords on IoT devices.

On Monday, the United Kingdom became the first country in the world to ban default guessable usernames and passwords from these IoT devices. Unique passwords installed by default are still permitted.

The Product Security and Telecommunications Infrastructure Act 2022 (PSTI) introduces new minimum-security standards for manufacturers, and demands that these companies are open with consumers about how long their products will receive security updates for.

The UK may be the first country, but as far as I know, California is the first jurisdiction. It banned default passwords in 2018, the law taking effect in 2020.

This sort of thing benefits all of us everywhere. IoT manufacturers aren’t making two devices, one for California and one for the rest of the US. And they’re not going to make one for the UK and another for the rest of Europe, either. They’ll remove the default passwords and sell those devices everywhere.

Another news article.

Scammers tricked a company into believing they were dealing with a BBC presenter. They faked her voice, and accepted money intended for her.

Meta has threatened to pull WhatsApp out of India if the courts try to force it to break its end-to-end encryption.

During the Cold War, the US Navy tried to make a secret code out of whale song.

The basic plan was to develop coded messages from recordings of whales, dolphins, sea lions, and seals. The submarine would broadcast the noises and a computer—the Combo Signal Recognizer (CSR)—would detect the specific patterns and decode them on the other end. In theory, this idea was relatively simple. As work progressed, the Navy found a number of complicated problems to overcome, the bulk of which centered on the authenticity of the code itself.

The message structure couldn’t just substitute the moaning of a whale or a crying seal for As and Bs or even whole words. In addition, the sounds Navy technicians recorded between 1959 and 1965 all had natural background noise. With the technology available, it would have been hard to scrub that out. Repeated blasts of the same sounds with identical extra noise would stand out to even untrained sonar operators.

In the end, it didn’t work.

A cruise ship is searching for the colossal squid.

As usual, you can also use this squid post to talk about the security stories in the news that I haven’t covered.

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