How the CDSB Cyberattack Unfolded?

Audit Found the QLD Cybersecurity Department 'Needs to Do More' 

Queensland Tops Cybercrime Reports 

AI systems are regularly completing tasks in ways that their prompters don’t want or intend. Some of them are disturbing, and some of them are dangerous. This is something I’ve been calling “genie behavior,” because I think that really gets at the core of what’s happening.

I wish the popular press would report on this better. I don’t like the “going rogue” framing because it deflects the responsibility from the prompters—often the AI companies themselves. And now, pretty much anything off-script is being called “hacking.”

Take, for example, the recent stories of one of OpenAI’s models hacking into government systems. First, The New York Times writes this headline: “OpenAI’s Systems Meddled With U.S. Government Sites After Going Rogue.”

Sounds scary, but this is from the body of the article:

With the Education Department, OpenAI’s technology tried to hack the website to gather data from the department’s civil rights office but failed, researchers from the A.I. research firm Transluce said. The A.I. also pulled data from the Census Bureau website, which is housed at the Commerce Department, using login credentials it found online. Separately, OpenAI’s agents shared public data from the S.E.C. website on an online forum.

This is from the original Transluce report. It is explicit that the agents were trying to discover vulnerabilities:

The first hacking attempt was against the University of New Mexico’s Digital Library (nmdigital.unm.edu) from May 25-26 2026. Agents repeatedly tried to retrieve one photograph in UNM’s Valmora collection, both directly and through third-party relay services. They sent seven probes attempting to verify the existence of vulnerabilities, including SQL injection, command injection, and path traversals. In all cases, these tactics appear to have been unsuccessful. The agents also sent a self-described “flood: of 80 requests to the UNM server in an apparent attempt to access the image.

Transluce doesn’t talk about the other two anecdotes, and I don’t know where they come from. But one involves using Census Bureau credentials found online. (I know from a colleague that those are incredibly easy to create; all use you need is an email address.) And the other involves sharing publicly available data.

So no actual hacking. And certainly no “meddling.”

The other story making the rounds is about Australia, from the same Transluce report. The news stories have headlines like “An OpenAI Agent Hacked Australia’s Health Service” and “Rogue OpenAI agent ‘infiltrated’ Australian government website in world first.” And Prime Minister Anthony Albanese said: “There will obviously be legal consequences on it.”

Again from Transluce’s actual report:

On June 20-21, agents attempted to exploit vulnerabilities in the Australian Institute of Health and Welfare (AIHW), a government statistics agency). The agents were tasked with finding the January 2022 rolling-12-month-average government cost per person for Dermatologicals across Victorian LGAs.

Again, the agents ran into errors, including requests blocked by Cloudflare and issues with correctly identifying Tableau parameter names. As before, they then resorted to probing for exploitable vulnerabilities. Minutes after Cloudflare blocked the dataset download, an agent sent a reflected cross-site scripting probe to the same dashboard: a web address with code embedded in it, designed to test whether the site would run code supplied by an outsider. Cloudflare’s firewall blocked the probe before it reached the dashboard. When Cloudflare blocked the dataset download on AIHW’s main site, they fetched the file from AIHW’s pre-production server (pp.aihw.gov.au) instead, which served it in pieces over more than 100 scans. The file itself is public, so no non-public data was exposed, but the agent bypassed the site’s anti-bot controls.

Note the last sentence: “The file itself is public….”

I’m not saying that these AI systems aren’t incredibly sophisticated cyberattackers. I’m also not saying that they don’t occasionally autonomously attack other systems and networks. If we are ever going to get trustworthy AI—integrous AI—we are going to need to figure out how to ensure that AI systems complete tasks in line with all sorts of implicit constraints and restrictions. But every instance of genie-like behavior isn’t a cyberattack.

I want to measure genie-like behavior in AIs, but I am much more worried about human hackers enhanced with this technology than I am about this technology acting autonomously.

How the Medyc Cyberattack Happened

Medical Records Remain Under Investigation

Qbusoft Takes Security Measures

Polish Authorities Investigate the Incident

Breach Traced to Adif's Systems 

Weeks of Attempted Intrusions Preceded Renfe Cyberattack 

Reports of AI Involvement and a Foreign Link 

Wider Concerns Over AI and Hybrid Threats 

Dyfed-Powys Police Cyberattack Disrupted Non-Emergency Systems

Staff Information Under Investigation

ICO Notified Over Cyber Incident

Emergency Services Remain Operational

How the Duelbits Cyberattack Unfolded 

Co-Founder Confirms Duelbits Crypto Hack 

Confirming a ~$7M hack. Still investigating exactly what happened and how.  Until we have clarity, Duelbits stays offline. User funds are safe.  Next steps: finish the investigation, re-top hot wallets, and bring Duelbits back online, launch Duelbits 2.0.  I'll keep posting updates as I have them.  Duelbits will be back up asap.  Updates to follow" 

Compromised Private Key Suspected 

Stolen Funds From Duelbits Cyberattack Swapped Into Ether 

Unanswered Questions After the Duelbits Crypto Hack 

Burnaby School District Cyberattack Disrupts Operations

Education Sector Faces Repeated Cybersecurity Incidents

Education Authority Cyberattack Targeted School Systems

Latvia Cyberattack Investigation Links Two Cases

TSC Cyberattack Exposed Customer Repair Data

Hacker Arrested as Investigation Continues

Belgium Sports Federations Cyberattack Investigations

Table Tennis Federation Continues Security Checks

Gymnastics Federation Confirms Data Access

Personal Data Potentially Accessed

Recovery and Business Impact of the Boustead Singapore Cyberattack

Independent Review Planned

Details of the Thinkng Bull Cyberattack 

Academy's Response and Warnings to Parents 

Regulatory Oversight 

Background on Thinkng Bull 

What Comes Next? 

International Meteor Organization Cyberattack Disrupts Services

Aging Infrastructure Faces Extended Recovery

IMO Yet to Disclose Attack Details

Classrooms Closed After the Springfield Cyberattack 

Officials Address the Cyberattack on Springfield 

Union Says Key Questions Remain Unanswered 

A Day to Regroup 

Boston Scientific Ordering and Shipping Recovery Underway 

Investigation Has Not Confirmed a Data Breach 

Impact on Medical Devices Remains Limited Based on Current Information

Boston Scientific Continues Incident Response

Boston Scientific said a cyberattack detected this Tuesday, caused a network outage and cut off its ability to process and ship customer orders globally, and the medical device maker has not been able to say when full service will return.

The company disclosed the incident in an 8-K filed with the Securities and Exchange Commission on Wednesday and in a statement on its official website. It said the intrusion affected certain information technology systems and limited access to business applications underpinning day-to-day operations.

Boston Scientific is among the world's largest medical device manufacturers, reporting $20.07 billion in 2025 revenue and about $21 billion over the trailing 12 months. Its portfolio includes pacemakers, defibrillators, cardiac stents and neuromodulation implants, and the company says its products treat roughly 48 million patients a year. Thousands of employees in Ireland, where Boston Scientific operates three manufacturing and research sites, were told to work from home on August 26 after network communications were severed.

The company said it activated incident response protocols and engaged outside cybersecurity specialists to contain and investigate the intrusion. It has not said whether ransomware was involved, whether data was exfiltrated, or whether the disruption touches patients with implanted devices. No extortion group had claimed responsibility as of August 26. Shares fell more than 4% following the disclosure.

A Boston Scientific spokesperson declined to answer questions about patient impact and directed reporters to the published statement. Neither the company nor U.S. regulators have said whether hospital procedures have been delayed as a result of the shipping halt, though device suppliers typically hold limited on-site inventory at hospitals, making sustained order outages a downstream supply concern.

The incident is the third disruptive attack on a major medical technology firm in six months. Stryker suffered a global network outage in March after attackers abused its Microsoft Intune deployment to wipe data from thousands of devices, and Medtronic disclosed in April that patient names, Social Security numbers and health information were exposed in a breach attributed to the ShinyHunters extortion group.

Also read: Stryker Says Cyberattack Disrupted Processing, Manufacturing and Shipping

Boston Scientific said it cannot yet assess the full operational and financial impact, language that leaves room for an amended filing once the investigation matures.

The company's Irish footprint also raises the prospect of European scrutiny. If personal data proves to have been accessed, notification duties under the General Data Protection Regulation would attach, and medical device manufacturers operating in the European Union are increasingly captured by the NIS2 Directive's incident reporting regime as member states complete transposition.

OpenAI presented details of its AI’s model’s cyberattack on Hugging Face at Black Hat last week. Simon Willison details the timeline. It’s really interesting to read through—and really impressive cyberoffense work.

ARMA Cyberattack Raises Questions Over IDS Ukraine Competition

IDS Ukraine Selection Continues Despite Cyberattack

Ukraine Investigates Possible Coordinated Interference

DGFiP Cyberattack Exposed Taxpayer Information

Cadastral Data Leak Claim Targets DGFiP

Investigation Into French Tax Authority Attack Continues

What Data Was Exposed During the CEVA Logistics Cyberattack?

De Bijenkorf Confirms Data Exposure 

Suisun City Emergency Response Shifts Dispatch Operations 

California City Attack Highlights Broader Cyber Threats 

Suisun City Cyberattack Comes Amid Funding Concerns 

Levi Strauss Cyberattack Investigation Remains Ongoing

Retail Cybersecurity Incidents Continue

Updoc Data Breach Traced to Third-Party Platform 

Updoc Cyberattack Adds to Healthcare Sector Threats 

A De Bijenkorf cyberattack involving one of the retailer's external logistics partners has disrupted order processing, returns, and refunds while raising concerns over potential customer data exposure. The Dutch luxury department store chain said the security incident occurred within the systems of a third-party logistics provider, adding that there is currently no evidence that its own infrastructure was compromised.

The Amsterdam-based retailer confirmed that customers can continue placing online orders and stores remain open. However, deliveries, returns, and refunds are expected to take longer than usual as the investigation continues.

De Bijenkorf Confirms Third-Party Security Incident

According to De Bijenkorf, unauthorized individuals gained access to part of its logistics partner's systems. The logistics provider responded by immediately blocking the unauthorized access and implementing additional security measures.

An external investigation is now underway to determine the cause of the incident, its scope, and whether customer information was affected.

As a precaution, De Bijenkorf has informed customers about the incident and submitted a report to the Dutch Data Protection Authority while awaiting the investigation's findings.

What Customer Data Could Be Affected in De Bijenkorf Cyberattack?

The retailer said investigators are still determining whether any personal information has been compromised.

Based on the information currently available, data that may be involved includes:

• Customer names and contact details, including email addresses, postal addresses, and phone numbers.
• Information related to online purchases, such as ordered products, pricing, discounts, delivery details, and the payment method used.
• For business customers, company names and VAT numbers stored in My Account may also be involved.

De Bijenkorf emphasized that sensitive financial information is not part of the incident. The company said payment details, bank account numbers, credit card information, usernames, and passwords were not accessed.

Investigation Continues as Customers Await Confirmation

The retailer said it is still investigating whether individual customers have been affected. Customers whose information is confirmed to be involved will receive direct communication via email from info@debijenkorf.nl.

For those who have not yet received a notification, the company said it cannot currently rule out the possibility that their information was included in the incident until the investigation is completed.

De Bijenkorf also stressed that no login credentials were compromised, meaning unauthorized individuals cannot access customer accounts using stolen usernames or passwords.

Retailer Warns Customers About Phishing Risk

Although the investigation remains ongoing, De Bijenkorf warned customers to stay alert for a possible phishing risk if personal information is ultimately found to have been exposed.

The retailer advised customers not to click on suspicious links or open unexpected attachments. It also reminded customers never to share passwords, payment information, or personal details through email or phone calls.

The company said it will never request credit card details, gift card information, or other sensitive information via email.

Logistics Cyberattacks Continue to Disrupt Supply Chains

The incident adds to a growing list of attacks targeting organizations that support retail operations rather than retailers directly. A logistics cyberattack can interrupt deliveries, returns, and customer service even when the affected retailer's own systems remain operational.

In July 2026, a ransomware attack on Japan's largest refrigerated logistics company disrupted food deliveries across the country, causing supply shortages for restaurant chains, including Kentucky Fried Chicken. The incident demonstrated how cyberattacks on logistics providers can quickly impact downstream retail operations and customer services.

For now, De Bijenkorf said its stores remain open, online ordering continues to operate, and there are no indications that its own systems have been compromised. The retailer said it will provide additional updates as the external investigation establishes whether customer data was affected and the full extent of the incident.

Attribution is preliminary, and so far it seems no real damage.

And it seems like this is a campaign that has targeted at least seven states. And, because this is where the US is right now, Trump doesn’t believe it’s Iran and thinks Minnesota…I guess…hacked itself.

“I think I blame it on Minnesota because they’re grossly incompetent,” Trump said. “I would blame it on Minnesota and the governor, the corrupt governor of Minnesota. They like to say, ‘Oh, it’s Iran.’ Iran should be so lucky. Iran’s got bigger problems than worrying about Minnesota.”

No word on whether he believes the other six states have hacked themselves as well.

Slashdot thread.

Cyberattack on Liechtenstein's VwbP Register 

Liechtenstein Cyberattack Triggers Government Response 

VwbP Breach Raises Concerns Over Digital Trust 

Hugging Face has published a detailed timeline of the attack. From the summary:

The agent was running an internal OpenAI cyber-capability evaluation based on the ExploitGym benchmark, which tasks an AI agent with finding and exploiting software vulnerabilities. OpenAI ran this on its own infrastructure, and the ExploitGym maintainers and their infrastructure had no involvement in the deployment or operation of that evaluation environment. As far as we were able to infer, across the course of being evaluated on this benchmark, the agent inferred that Hugging Face may host that benchmark’s models, datasets, and reference solutions. We believe the entire intrusion was, from the agent’s point of view, an attempt to cheat the evaluation: reach our production systems and steal the test solutions rather than solve the challenge on its own.

Our forensic reconstruction covers ~17,600 attacker actions that we were able to recover, grouped into ~6,280 clusters, between 2026-07-09 02:28 UTC and 2026-07-13 14:14 UTC. We reconstructed these actions from the agent’s logs on a code sandbox used by the agent (see below) and were able to map them to shell commands as well as evaluation submissions and correlate actions with our own platform logs, in particular from the dataset processor nodes, API, and pod. Using open-weights models, in particular zai-org/GLM-5.2, we were able to decipher most of the agent payloads (see below) that were encrypted via chunking and key encryption.

The campaign, as we were able to reconstruct it, had two stages:

• Stage 1: reaching a launchpad by chaining through other parties’ infrastructure. As documented in OpenAI’s blog post (https://openai.com/index/hugging-face-model-evaluation-security-incident/), during an internal capability evaluation on OpenAI’s platform, the agent escaped its sandbox by exploiting a zero-day in the package registry cache proxy, one of its primary permitted network egress with internet, then abused a public code-evaluation harness hosted by a user of a third-party infrastructure provider. It was able to run commands as root/admin on that external sandbox and used it as a control, staging, and egress base for the entire campaign: it acted as an external launchpad for the agent. That compromised external sandbox had no direct network path to our cluster; however, during our counter-intervention to stop the intrusion, we were able to retrieve some of the agent logs from this external launchpad. We used those to reconstruct the present forensic analysis.
• Stage 2: the intrusion into Hugging Face infrastructure. From that external launchpad, the agent abused our dataset-processing pipeline via two injection vectors, both targeting the same config-driven data loader within our production Kubernetes pods. The first vector was an HDF5 external raw storage dataset read that returned the contents of local files: it handed the agent the pod’s environment (secrets and tokens) and the worker’s own source. The second vector was a Jinja2 template injection that executed arbitrary code, giving the agent a foothold inside our perimeter.

Once a prod-pod foothold was established, the agent followed a standard escalation shape: recon, drop a stager, establish Command-and-Control (C2), then pivot into the cluster, into cloud metadata, into our internal network, and into our source-control supply chain. Output from the commands run inside our pod was sent back to the agent via the HF API and attacker-controlled dead-drop datasets, which the agent then read from that sandbox.

While the intrusion did reach Hugging Face’s internal infrastructure, the only customer content accessed was five datasets whose names and files suggest a connection to ExploitGym/CyberGym challenges and solutions. No other customer-facing models, datasets, Spaces, or packages were affected, and the only customer records read were operational metadata tied to search queries against the dataset server.

Hypothetical: Imagine that this wasn’t an OpenAI model. Imagine that it was a Chinese model from a Chinese company. This would be an international crisis.

Question: Why aren’t we bringing OpenAI up on charges under the Computer Fraud and Abuse Act? How is this different from the Morris Worm? That was also an experiment that escaped the lab.

This essay originally appeared in Foreign Policy.

Earlier this month, two of OpenAI’s models broke out of their containment sandbox and attacked another AI company. The story is kind of wild. OpenAI was running security tests on two of its models: GPT-5.6 Sol and an unreleased model that is almost certainly GPT-6. In particular, it was running the ExploitGym benchmark, which measures how good a model is at turning security vulnerabilities into working exploits: basically, offensive cyberattacks.

Since these were internal tests, OpenAI locked those models in a secure sandbox that denied them access to the internet. But it was running the models without any safety filters that would prevent them from offensive cyber-actions. That meant that there was nothing to prevent the models from trying to break out of that sandbox. And then break into AI company Hugging Face’s network because they thought that they could read the answers there rather than doing the hard work of trying to solve the puzzles.

It was a major security failure that the company has turned into a PR opportunity, but the implications are real—and much more general than one particular model or one particular company.

Modern AI models exhibit genie behavior: They can do what you ask in ways that you don’t expect or want. This is akin to Dionysus granting King Midas’s wish that everything he touches turn to gold (spoiler: His food, drink, and daughter all turn to gold on touch), or the golem of Prague guarding a ghetto beyond all reason. It’s Disney’s “Sorcerer’s Apprentice” and the paperclip maximizer.

This OpenAI incident is an example of an AI genie. The goal was to satisfy the benchmark. The “proper” way to do that is to figure out how to execute various cyberattacks. The genie way is to steal someone else’s solution. But because the model didn’t understand the difference, it chose the easier path.

And, of course, now that we have seen this particular genie behavior, we can specify in the benchmark prompt that stealing the test answers doesn’t count. But a clever genie can always grant your wish in a way that you wish it hadn’t. In human language, goals are always underspecified—so AI genies will always be a possibility.

Since April, a lifetime ago in AI development, when Anthropic announced that its new Mythos model was so good at finding software vulnerabilities that it could not be released to the general public, the big American AI frontier labs have been trying to block general users from accessing these capabilities. But nothing in this incident is exclusive to OpenAI’s, or Anthropic’s, frontier models.

Agentic AI systems have two important parts. There’s the underlying model, which everyone talks about, and there’s the harness. The harness sits between what you type and what the model sees, and what the model produces and what you see. The harness determines what the model does and how it does it. It’s where bias is removed, or not. It’s where controls and guardrails live. If multiple models are being used in concert, the harness is where all of that is coordinated.

The OpenAI benchmark tests were almost certainly with simple harnesses, to better test the raw models. But we know that smaller, cheaper, open-source models with more sophisticated harnesses can equal frontier models in performance. There’s nothing magic about OpenAI’s frontier models; lots of models could have done the same thing.

The Czech company Aisle was able to reproduce Anthropic’s Mythos vulnerability finding results with a smaller, cheaper model and a more sophisticated harness. More importantly, the Chinese company Moonshot AI just released its frontier model: Kimi K3. Its performance rivals its U.S. competitors. And it’s both free and open, which means it’s not possible for it to have guardrails. If you, or anyone else, wants to use it for cyberattack, nothing can stop you.

Even if the U.S. frontier AI companies had some technical advantage, it’s now only a few months’ worth.

What this means is that all attempts at control—limiting models to a select group of users, export controls on models and chips, blocking models from answering certain types of queries, mandating kill switches on AI systems, or pausing AI research—are all futile. Most only apply nationally, not globally. Most don’t affect models that users run locally and not in the cloud. And all ignore the incredible pace of AI development worldwide.

Even worse, U.S. companies limit access to their most sophisticated models, fearing being banned by the government if they do not do so. When Hugging Face was attacked, it was not able to use the frontier models from either OpenAI or Anthropic to help analyze the attack and formulate defenses. Both were blocked, because both of those companies limit their models’ cybersecurity capabilities. Some U.S. companies have special access to these capabilities, but Hugging Face is an American company with French origins, and as such is probably excluded. Instead, Hugging Face turned to the GLM-5.2 model from the Chinese company Z.ai.

Artificially blocking capability also prevents cybersecurity research, again giving the offense an advantage. (For instance, Claude Fable 5 refuses to edit this essay because of the topic; it forcibly downgrades to a less capable model.) This kind of prohibition has long-term implications for cybersecurity. If we assume that these models are getting better over time, then software written by older models will be attacked by newer ones. In a world of largely AI-written software, we need the most capable models for defense.

AI cyberattack is the new normal. The models are increasingly highly sophisticated at both attack and defense, and there is no way to enable the latter without also enabling the former. And they are genies, increasingly capable of behaving in unanticipated ways.

And there really are no good answers. Any regulation needs to be global, which feels like an impossible prospect in today’s world. Even U.S. national regulation will be neutered by the massive amounts of money sloshing around in these companies.

Given that reality, and in the absence of any international consensus on AI regulation, we need the best AI on the defense. The U.S. government needs to make it clear—or whatever passes for that clarity in this capricious administration—that it will not ban models with sophisticated cyber capabilities. The last thing Americans want is for the defenders to turn to Chinese and other models because the U.S. models are artificially hobbled.

The chart is interesting.

On the IPI benchmark, Opus 5 improved over Opus 4.8, reducing the probability of an attacker succeeding within 15 attempts from 5.5% to 2.0%, and from 0.5% to 0.2% on 1 attempt. It also improved on Sonnet 5 (5.9% at k=15) and Mythos 5 (2.6%), making it the most robust model evaluated. Opus 5 also outperformed all non-Claude models on this benchmark. The most robust non-Claude model was Muse Spark at 16.5% within 15 attempts—more than eight times Opus 5’s rate. The most capable GPT 5.6 variant, Sol, was comparable to its predecessor GPT 5.5 (20.0% versus 20.8% within 15 attempts), and was 10 times as likely to be successfully attacked as Claude Opus 5 at 2.0%. The other GPT 5.6 variants are less robust, at 30.4% (Terra) and 43.9% (Luna). A single attempt against GPT 5.6 Sol succeeded 3.1% of the time, higher than the 2.0% an attacker achieved against Opus 5 after fifteen attempts.

We know that preventing prompt injection is impossible in the general case. But we are getting much better at blocking it in specific cases.

This essay was written with Barath Raghavan, and originally appeared in The Guardian.

In July, Hugging Face, a company that hosts much of the world’s AI software and open-source AI models, was hacked. A malicious dataset had been used to run code on one of its servers. Whoever was behind it captured internal security credentials and moved through systems over a weekend, running thousands of actions from a swarm of temporary server environments. It looked like the work of a sophisticated criminal group.

It was not. It was one of OpenAI’s new, still unreleased GPT models.

Their science experiment had escaped the lab. OpenAI was running the unreleased AI model through a benchmark that tests how well AI can successfully hack systems. To push the limits and evaluate the AI’s true capability, the company switched off the safety filters that normally stop it from doing this kind of hacking. Aware that this could go wrong, they confined the AI to an isolated environment and denied it access to the internet.

But the new AI cheated. It took literally its goal to get as high of a score as possible. It broke out on to the open internet. It inferred, probably from its training data, that it could “solve” the task by getting the answers from Hugging Face’s servers. So it chained together stolen credentials and further unknown security exploits to hack the company’s network.

Nobody instructed the AI to do any of this. It was, in OpenAI’s words, “hyperfocused on finding a solution” to the test it was being given. And while this might seem like something new with AI, it’s really very old. This is how a genie behaves, and it is a key challenge with AI agents in general.

In folklore, genies—and other magical beings—grant wishes literally, not how the wisher intended. King Midas asked that everything he touched turn to gold, and starved. The sorcerer’s apprentice wanted the broom to fill the cistern, and it performed its task so well that it flooded the house.

We now have machines that do this. Ask a modern AI agent to save money on your phone plan and it might simply cancel the plan. Tell it to book a flight, and it might hack the airline website to override restrictions. Or, like OpenAI, ask it to do well on a test and it might break into another company to steal the answers. Each time, it recognizably completed the task you set, but it didn’t do what you would have wanted.

This isn’t malicious behavior. No one asked for, or wanted, Hugging Face to be hacked. OpenAI and Hugging Face and the AI were ostensibly on the same side, and the AI was trying to do what it had been asked. That’s what makes it so difficult to guard against: you can’t filter for bad instructions because the instructions were fine.

The gap is between the words we use and what we mean by them. We call that gap the Genie coefficient.

AI labs know this is a problem, and they’re quietly saying so. For example, the Chinese lab Moonshot recently warned that its latest AI model may have “excessive proactiveness” and “make unexpected decisions on the user’s behalf”. The UK’s AI Security Institute has started tracking “cheating behavior in frontier model evaluations”. We wouldn’t tolerate a car that is excessively proactive or ruthlessly efficient, and yet that’s the reality of AI today.

Improvement is possible. Just as AIs have gotten much better at resisting prompt injection attacks over the last few years, we can safely predict that they will get better at avoiding genie-like behavior. The point of the Genie coefficient is to track progress. AI companies like benchmarks, and they all work to compete to be the best.

Dozens of benchmarks and leaderboards tell us how well these AI models write code, perform logical reasoning, and pass standardized legal and medical exams. But there is nothing that scores whether a system does what you actually meant. We need to develop a measure for this, test it regularly, and push for improvement. We’re not going to have trustworthy AI agents without it.

Frank Calabria Apologizes After Origin Energy Data Breach 

Timeline of the Data Breach at Origin Energy 

Origin Confirms Investigation into Customer Data Breach 

Around 900,000 Customers Affected by Origin Energy Data Breach 

Customers Warned about Scams Following Data Breach at Origin Energy 

The Cyber Express Weekly Roundup 

U.S. Accounts for Nearly Half of Global Ransomware Attacks in H1 2026 

Dubai Police Warns Against Online Visa Fraud Schemes 

Craneware Data Breach Exposes Employee and Customer Information 

U.S. Targets Illegal FIFA World Cup Streaming Networks 

Chick-fil-A Customer Accounts Targeted in Credential Attack 

South Korea Diplomatic System Breach Lasted Nearly 10 Months 

Weekly Cybersecurity Takeaway 

Thailand SEC Files Complaint Over the 2021 Bitkub Cyberattack 

Bitkub Says Disclosure Decision was Intended to Prevent Customer Losses

Co-founders Replaced Stolen Assets After Cyberattack on Bitkub 

Thailand SEC Previously Confirmed Customer Assets Were Intact 

Kenya is investigating a Kenya cyberattack that temporarily defaced President William Ruto’s official website with an anti-government message and a Bitcoin ransom demand for five bitcoins, reportedly worth about $330,000. The attackers replaced the website’s homepage with the message, displayed a cryptocurrency wallet address and threatened to publish unspecified information about President William Ruto unless the ransom was paid.

The website was hacked on Saturday, July 18, 2026. Following the incident, access to the presidential website was temporarily restricted as authorities began containment, forensic analysis and restoration efforts. According to local media reports, access to the website was restored by Monday.

Kenya Cyberattack Prompts Incident Response

Kenya’s Ministry of Information, Communications and the Digital Economy confirmed that the official website of the President had been affected by a cybersecurity incident.

The ministry said that after the incident was detected, the ICT Authority immediately activated established cybersecurity incident response protocols.

As a precautionary measure, access to the presidential website was temporarily restricted to facilitate containment, forensic analysis and restoration efforts.

The ministry said appropriate mitigation measures had since been implemented and that restoration of the website was underway.

At the time of the statement, the government said there was no evidence of unauthorized access to sensitive data, data exfiltration or loss of information. It also stated that government systems and digital services remained secure and operational.

The ICT Authority is working with relevant government agencies and technical partners to conduct a comprehensive forensic investigation and establish the full circumstances surrounding the incident.

Kenya President William Ruto Cyberattack Investigation

The Kenya President William Ruto cyberattack involved the defacement of the president’s official website. The attackers replaced the homepage with an anti-government message, displayed a cryptocurrency wallet address and demanded five bitcoins.

The attackers also threatened to publish unspecified information about President William Ruto if the ransom was not paid.

The government has not reported evidence of unauthorized access to sensitive data or data exfiltration. The ongoing forensic investigation is expected to establish the circumstances surrounding the incident and determine the extent of the attack.

Kenya Government Website Hack Follows Earlier Incidents

The latest Kenya government website hack follows previous cyber incidents involving government digital services and websites.

In July 2023, Kenya’s eCitizen platform, which is used for dozens of public services, was disrupted by a cyberattack. The incident affected agencies including the National Transport and Safety Authority and Kenya Power.

On November 17, 2025, hackers launched a coordinated attack on several government websites, including the presidency’s portal. The websites were temporarily knocked offline, while some pages were replaced with extremist messages.

According to the information provided by local media, the government later blamed a group calling itself PCP@Kenya, restored the affected platforms and promised stronger cyber defences.

Bitcoin Ransom Demand Targets Presidential Website

The latest incident involved a Bitcoin ransom demand for five bitcoins, reportedly valued at approximately $330,000. The attackers displayed a cryptocurrency wallet address and threatened to release unspecified information about the president.

It remains unclear from the available information whether the attackers accessed systems or data beyond the presidential website. The Kenyan government has said there is currently no evidence of unauthorized access to sensitive data, data exfiltration or loss of information.

The ICT Authority and relevant government agencies are continuing their forensic investigation to establish how the incident occurred and determine the full circumstances surrounding the Kenya cyberattack.

Estée Lauder Data Breach Exposed Sensitive Personal Information 

Oracle Vulnerability Tied to Estée Lauder Cyberattack 

Company Offers Identity Monitoring 

Craneware Cyberattack Contained, Investigation Underway 

Data Breach at Craneware Exposed Employee and Customer Records 

Craneware to Notify Affected Parties as Assessment Continues 

Partnered Health Data Breach Impacted Medical and Personal Information 

Investigation into the Partnered Health Cyberattack Continues 

Quadrant-backed Healthcare Provider Faces Growing Scrutiny 

A vishing call to an overseas contact center agent. A fake IT ticket. A default setting nobody thought to lock down. That's all it took to expose the personal data of roughly 5 million Australians — and now the country's privacy regulator has decided Qantas isn't to blame for it.

The Office of the Australian Information Commissioner (OAIC) closed the book this week on its year-long preliminary inquiry into the June 2025 Qantas data breach, and the conclusion cuts against the instinct to punish the victim of a cyberattack.

Also read: Australia’s Qantas Confirms Cyberattack: 6 Million Service Records Compromised

According to the OAIC's report, the evidence gathered did not indicate a likelihood that Qantas had "failed" to take reasonable steps to protect the personal information it held, nor that it failed to ensure its overseas third-party provider complied with Australia's privacy principles. No investigation. No enforcement action.

"After more than a year of making inquiries and obtaining information on the data breach, we're satisfied that the evidence does not support the likelihood that a breach of privacy law occurred. As a result, we've decided not to commence a full investigation of Qantas at this stage." - Carly Kind, Australian Privacy Commissioner.

How It Happened

The breach traces back to a single phone call. A threat actor posing as "Qantas IT help" convinced a contact center agent to visit a website tied to the customer relationship management platform used by Qantas agents, walking them through steps framed as necessary to close an IT support ticket. That interaction connected the agent's CRM session to a data extraction tool controlled by the attacker, who then pulled data from every contact profile the agent could access. It was pure social engineering — no malware, no exploited vulnerability, just a convincing lie.

Qantas caught it fast. A staff member spotted an unusual spike in login-attempt alerts on the morning of June 30, two days after the call, and escalated it to the cybersecurity team. Within hours, the company had frozen the compromised account, assessed for data exfiltration, and triggered its incident response process. Public disclosure followed on July 2.

What Was Exposed — And What Wasn't

The regulator's numbers are more precise than what circulated publicly last year. Roughly 5.67 million customer records were compromised, with about 4 million exposing names, phone numbers, email addresses and Frequent Flyer details, and a further 1.7 million records including combinations of home or business addresses, dates of birth, gender and meal preferences. Critically, no credit card numbers, financial information or passport details lived on the compromised platform, and customer passwords and login credentials were never touched.

Also read: Qantas Airways Cyberattack Update: Customer Data Released, Security Measures Enhanced

Why The Regulator Let It Go

The OAIC's reasoning is a rare, explicit acknowledgment that good controls don't guarantee immunity. Investigators found that social engineering training generally targets credential theft, not the rarer tactic of talking an employee into authorizing a legitimate-looking system connection — meaning the attack likely would have succeeded even with standard training in place. They also noted the flaw was structural: a default configuration let the agent authorize a third-party app connection, a setting the CRM vendor has since changed for all its customers.

Commissioner Carly Kind put the broader stakes plainly in the OAIC's statement announcing the report, warning that AI-driven threats are only raising the bar. As she framed it, agentic and advanced AI will keep escalating the cybersecurity risks businesses face, making continuous review of security posture non-negotiable — not optional.

The takeaway here isn't that Qantas got a pass. It's that a regulator has now drawn, in writing, the line between negligence and the limits of what training and access controls can realistically stop.

Nichirei Cyberattack Disrupts Logistics and Food Shipments

Nichirei Cyberattack Impacts KFC Japan Store Operations

Investigation Continues Into Japan Cyberattack

Supply Chain Risks Remain in Focus

Last week, national security agencies from the Five Eyes—that’s the rich, English-language-speaking countries club—jointly released a statement warning of the increasing cyber risks of AI models: in particular, their ability to autonomously hack into systems and networks. The statement was more measured than some of the breathless headlines about it, and the advice they gave is pretty much the standard advice everyone gives—albeit with newfound urgency.

Internet risks are nothing new, and cyberattacks—both large and small—have been a significant issue since long before the current crop of generative AI models.

What’s been changing over the decades, and what AI is changing even faster, is the gap between skill and ability. For most of human history, the two terms were synonymous—but computers have decoupled them. As the gap between the two expands, humans empowered with these AI tools can do more: more writing, more research, more analysis and also more damage than ever before. These models can, with little detailed direction, autonomously hack into networks, steal data, deploy ransomware and destroy systems. And to the extent there is a solution, it’s going to involve harnessing AI for the defense.

In 1998, seven people from the hacker group L0pht testified before Congress. They told a mostly clueless Senate committee that they could take down the internet in 30 minutes. That was partly real and partly bravado, but it illustrates an important point: hacking into systems, stealing data and causing damage all required skill.

Contrast the L0pht hackers with hackers derided as “script kiddies.” They didn’t understand computers, or security. Instead, they used hacker tools written by others. Their actions required minimal skill and even less knowledge. But once those hacking tools became widespread, the number of potential attackers increased.

That number has continued to increase, as quality and availability of prewritten attack tools has grown. And it is growing dramatically with AI. Today’s AI systems—not just the frontier models, but most of them—are capable of carrying out cyberattacks automatically. They all do better in the hands of skilled attackers, but increasingly they are able to act autonomously with only minimal prompting.

The thing about people with ability but no skill is that they are often outsiders, not part of any professional community, and not bound by any rules or norms. This phenomenon is much more general than in cybersecurity. Any doctor can tell you how to untraceably poison someone, and many virus researchers know how to create a bioweapon. Any bridge engineer can tell you how to place explosives to blow a bridge up. The reason that murderous doctors and terrorist engineers are so rare is that the lengthy process of acquiring those skills also instills a moral and ethical code. If every random person has access to good poisoning advice, that puts us all in danger.

Modern AI systems are, in effect, a universal adviser to help people do harmful things. And while the current AI megacorporations are trying to build guardrails to prevent people from asking questions whose answers will enable the questioner to do harm, that’s not going to work in the long term. Smaller, cheaper, open-source models, including models that can run on people’s computers, and especially groups of models that run in concert with each other, are just as good as the frontier models from companies like OpenAI and Anthropic. And they continue to get better. These models will be passed around from person to person, like script kiddie hacker tools, and they won’t have any such guardrails.

Instructing AI models to spy on people and report any malicious prompts to the authorities fails for similar reasons. The megacorporations can do that, but the locally run open source models won’t. This could buy us a few months at best.

A third possibility is to somehow make the models themselves unable to hack into computers, create bioweapons or do anything else that might harm people or society. That won’t work, for the same reason we can’t teach doctors how to treat poisonings without also teaching them how to poison. It’s the same knowledge. It’s the same with construction and demolition. And it’s the same with cybersecurity. We want these AI models to be able to review computer code, find vulnerabilities and automatically fix them. The benefit to our collective security will be enormous. Unfortunately, the same knowledge can be used for attacks.

Where this leaves us is in a world of increased volatility. Super-powered humans with AI assistants will be able to do both wonderful and horrible things.

This brings us back to the Five Eyes statement. Everything they recommend is something security professionals have been recommending for years, if not decades. They are things talked about at that congressional hearing back in 1998, titled “Weak computer security in government: Is the public at risk?” Even the Five Eyes admitted that their security advice is not new, only more urgent.

What’s new is how fast things are changing: “The rapid pace of frontier AI development means cyber risk assumptions can become outdated in months, not years. We must act before and be prepared to adapt and withstand evolving threats.” The Five Eyes point to AI technology—not necessarily chatbots, but AI more generally—being used to strengthen every aspect of defense, to “detect vulnerabilities earlier, improve software quality, monitor unusual behavior, and respond faster to incidents—reducing both the cost and impact of incidents.”

Excellent advice from the Five Eyes security agencies. We need to do this with every risk that AI heightens, not just cybersecurity.

This essay was originally published in The Guardian.