Session 5D: Side Channels 1

Authors, Creators & Presenters: Lukas Maar (Graz University of Technology), Jonas Juffinger (Graz University of Technology), Thomas Steinbauer (Graz University of Technology), Daniel Gruss (Graz University of Technology), Stefan Mangard (Graz University of Technology)

PAPER
KernelSnitch: Side Channel-Attacks On Kernel Data Structures

The sharing of hardware elements, such as caches, is known to introduce microarchitectural side-channel leakage. One approach to eliminate this leakage is to not share hardware elements across security domains. However, even under the assumption of leakage-free hardware, it is unclear whether other critical system components, like the operating system, introduce software-caused side-channel leakage. In this paper, we present a novel generic software side-channel attack, KernelSnitch, targeting kernel data structures such as hash tables and trees. These structures are commonly used to store both kernel and user information, e.g., metadata for userspace locks. KernelSnitch exploits that these data structures are variable in size, ranging from an empty state to a theoretically arbitrary amount of elements. Accessing these structures requires a variable amount of time depending on the number of elements, i.e., the occupancy level. This variance constitutes a timing side channel, observable from user space by an unprivileged, isolated attacker. While the timing differences are very low compared to the syscall runtime, we demonstrate and evaluate methods to amplify these timing differences reliably. In three case studies, we show that KernelSnitch allows unprivileged and isolated attackers to leak sensitive information from the kernel and activities in other processes. First, we demonstrate covert channels with transmission rates up to 580 kbit/s. Second, we perform a kernel heap pointer leak in less than 65 s by exploiting the specific indexing that Linux is using in hash tables. Third, we demonstrate a website fingerprinting attack, achieving an F1 score of more than 89 %, showing that activity in other user programs can be observed using KernelSnitch. Finally, we discuss mitigations for our hardware-agnostic attacks.

ABOUT NDSS
The Network and Distributed System Security Symposium (NDSS) fosters information exchange among researchers and practitioners of network and distributed system security. The target audience includes those interested in practical aspects of network and distributed system security, with a focus on actual system design and implementation. A major goal is to encourage and enable the Internet community to apply, deploy, and advance the state of available security technologies.

Our thanks to the Network and Distributed System Security (NDSS) Symposium for publishing their Creators, Authors and Presenter’s superb NDSS Symposium 2025 Conference content on the Organizations' YouTube Channel.

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In a nod to the evolving threat landscape that comes with cloud computing and AI and the growing supply chain threats, Microsoft is broadening its bug bounty program to reward researchers who uncover threats to its users that come from third-party code, like commercial and open source software,

The post Microsoft Expands its Bug Bounty Program to Include Third-Party Code appeared first on Security Boulevard.

As they work to fend off the rapidly expanding number of attempts by threat actors to exploit the dangerous React2Shell vulnerability, security teams are learning of two new flaws in React Server Components that could lead to denial-of-service attacks or the exposure of source code.

The post React Fixes Two New RSC Flaws as Security Teams Deal with React2Shell appeared first on Security Boulevard.

Session 5C: Federated Learning 1

Authors, Creators & Presenters: Duanyi Yao (Hong Kong University of Science and Technology), Songze Li (Southeast University), Xueluan Gong (Wuhan University), Sizai Hou (Hong Kong University of Science and Technology), Gaoning Pan (Hangzhou Dianzi University)

PAPER
URVFL: Undetectable Data Reconstruction Attack on Vertical Federated Learning

Vertical Federated Learning (VFL) is a collaborative learning paradigm designed for scenarios where multiple clients share disjoint features of the same set of data samples. Albeit a wide range of applications, VFL is faced with privacy leakage from data reconstruction attacks. These attacks generally fall into two categories: honest-but-curious (HBC), where adversaries steal data while adhering to the protocol; and malicious attacks, where adversaries breach the training protocol for significant data leakage. While most research has focused on HBC scenarios, the exploration of malicious attacks remains limited. Launching effective malicious attacks in VFL presents unique challenges: 1) Firstly, given the distributed nature of clients' data features and models, each client rigorously guards its privacy and prohibits direct querying, complicating any attempts to steal data; 2) Existing malicious attacks alter the underlying VFL training task, and are hence easily detected by comparing the received gradients with the ones received in honest training. To overcome these challenges, we develop URVFL, a novel attack strategy that evades current detection mechanisms. The key idea is to integrate a discriminator with auxiliary classifier that takes a full advantage of the label information and generates malicious gradients to the victim clients: on one hand, label information helps to better characterize embeddings of samples from distinct classes, yielding an improved reconstruction performance; on the other hand, computing malicious gradients with label information better mimics the honest training, making the malicious gradients indistinguishable from the honest ones, and the attack much more stealthy. Our comprehensive experiments demonstrate that URVFL significantly outperforms existing attacks, and successfully circumvents SOTA detection methods for malicious attacks. Additional ablation studies and evaluations on defenses further underscore the robustness and effectiveness of URVFL

ABOUT NDSS
The Network and Distributed System Security Symposium (NDSS) fosters information exchange among researchers and practitioners of network and distributed system security. The target audience includes those interested in practical aspects of network and distributed system security, with a focus on actual system design and implementation. A major goal is to encourage and enable the Internet community to apply, deploy, and advance the state of available security technologies.

Our thanks to the Network and Distributed System Security (NDSS) Symposium for publishing their Creators, Authors and Presenter’s superb NDSS Symposium 2025 Conference content on the Organizations' YouTube Channel.

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The post NDSS 2025 – URVFL: Undetectable Data Reconstruction Attack On Vertical Federated Learning appeared first on Security Boulevard.

Bad actors that include nation-state groups to financially-motivated cybercriminals from across the globe are targeting the maximum-severity but easily exploitable React2Shell flaw, with threat researchers see everything from probes and backdoors to botnets and cryptominers.

The post Attackers Worldwide are Zeroing In on React2Shell Vulnerability appeared first on Security Boulevard.

Session 5C: Federated Learning 1

Authors, Creators & Presenters: Dzung Pham (University of Massachusetts Amherst), Shreyas Kulkarni (University of Massachusetts Amherst), Amir Houmansadr (University of Massachusetts Amherst)

PAPER
RAIFLE: Reconstruction Attacks on Interaction-based Federated Learning with Adversarial Data Manipulation

Federated learning has emerged as a promising privacy-preserving solution for machine learning domains that rely on user interactions, particularly recommender systems and online learning to rank. While there has been substantial research on the privacy of traditional federated learning, little attention has been paid to the privacy properties of these interaction-based settings. In this work, we show that users face an elevated risk of having their private interactions reconstructed by the central server when the server can control the training features of the items that users interact with. We introduce RAIFLE, a novel optimization-based attack framework where the server actively manipulates the features of the items presented to users to increase the success rate of reconstruction. Our experiments with federated recommendation and online learning-to-rank scenarios demonstrate that RAIFLE is significantly more powerful than existing reconstruction attacks like gradient inversion, achieving high performance consistently in most settings. We discuss the pros and cons of several possible countermeasures to defend against RAIFLE in the context of interaction-based federated learning. Our code is open-sourced at https://github.com/dzungvpham/raifle
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ABOUT NDSS
The Network and Distributed System Security Symposium (NDSS) fosters information exchange among researchers and practitioners of network and distributed system security. The target audience includes those interested in practical aspects of network and distributed system security, with a focus on actual system design and implementation. A major goal is to encourage and enable the Internet community to apply, deploy, and advance the state of available security technologies.

Our thanks to the Network and Distributed System Security (NDSS) Symposium for publishing their Creators, Authors and Presenter’s superb NDSS Symposium 2025 Conference content on the Organizations' YouTube Channel.

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Session 5C: Federated Learning 1

Authors, Creators & Presenters: Phillip Rieger (Technical University of Darmstadt), Alessandro Pegoraro (Technical University of Darmstadt), Kavita Kumari (Technical University of Darmstadt), Tigist Abera (Technical University of Darmstadt), Jonathan Knauer (Technical University of Darmstadt), Ahmad-Reza Sadeghi (Technical University of Darmstadt)

PAPER
SafeSplit: A Novel Defense Against Client-Side Backdoor Attacks in Split Learning

Split Learning (SL) is a distributed deep learning approach enabling multiple clients and a server to collaboratively train and infer on a shared deep neural network (DNN) without requiring clients to share their private local data. The DNN is partitioned in SL, with most layers residing on the server and a few initial layers and inputs on the client side. This configuration allows resource-constrained clients to participate in training and inference. However, the distributed architecture exposes SL to backdoor attacks, where malicious clients can manipulate local datasets to alter the DNN's behavior. Existing defenses from other distributed frameworks like Federated Learning are not applicable, and there is a lack of effective backdoor defenses specifically designed for SL. We present SafeSplit, the first defense against client-side backdoor attacks in Split Learning (SL). SafeSplit enables the server to detect and filter out malicious client behavior by employing circular backward analysis after a client's training is completed, iteratively reverting to a trained checkpoint where the model under examination is found to be benign. It uses a two-fold analysis to identify client-induced changes and detect poisoned models. First, a static analysis in the frequency domain measures the differences in the layer's parameters at the server. Second, a dynamic analysis introduces a novel rotational distance metric that assesses the orientation shifts of the server's layer parameters during training. Our comprehensive evaluation across various data distributions, client counts, and attack scenarios demonstrates the high efficacy of this dual analysis in mitigating backdoor attacks while preserving model utility.

ABOUT NDSS
The Network and Distributed System Security Symposium (NDSS) fosters information exchange among researchers and practitioners of network and distributed system security. The target audience includes those interested in practical aspects of network and distributed system security, with a focus on actual system design and implementation. A major goal is to encourage and enable the Internet community to apply, deploy, and advance the state of available security technologies.

Our thanks to the Network and Distributed System Security (NDSS) Symposium for publishing their Creators, Authors and Presenter’s superb NDSS Symposium 2025 Conference content on the Organizations' YouTube Channel.

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Session 5C: Federated Learning 1

Authors, Creators & Presenters: Xiaochen Zhu (National University of Singapore & Massachusetts Institute of Technology), Xinjian Luo (National University of Singapore & Mohamed bin Zayed University of Artificial Intelligence), Yuncheng Wu (Renmin University of China), Yangfan Jiang (National University of Singapore), Xiaokui Xiao (National University of Singapore), Beng Chin Ooi (National University of Singapore)

PAPER
Passive Inference Attacks on Split Learning via Adversarial Regularization

Split Learning (SL) has emerged as a practical and efficient alternative to traditional federated learning. While previous attempts to attack SL have often relied on overly strong assumptions or targeted easily exploitable models, we seek to develop more capable attacks. We introduce SDAR, a novel attack framework against SL with an honest-but-curious server. SDAR leverages auxiliary data and adversarial regularization to learn a decodable simulator of the client's private model, which can effectively infer the client's private features under the vanilla SL, and both features and labels under the U-shaped SL. We perform extensive experiments in both configurations to validate the effectiveness of our proposed attacks. Notably, in challenging scenarios where existing passive attacks struggle to reconstruct the client's private data effectively, SDAR consistently achieves significantly superior attack performance, even comparable to active attacks. On CIFAR-10, at the deep split level of 7, SDAR achieves private feature reconstruction with less than 0.025 mean squared error in both the vanilla and the U-shaped SL, and attains a label inference accuracy of over 98% in the U-shaped setting, while existing attacks fail to produce non-trivial results.

ABOUT NDSS
The Network and Distributed System Security Symposium (NDSS) fosters information exchange among researchers and practitioners of network and distributed system security. The target audience includes those interested in practical aspects of network and distributed system security, with a focus on actual system design and implementation. A major goal is to encourage and enable the Internet community to apply, deploy, and advance the state of available security technologies.

Our thanks to the Network and Distributed System Security (NDSS) Symposium for publishing their Creators, Authors and Presenter’s superb NDSS Symposium 2025 Conference content on the Organizations' YouTube Channel.

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The post NDSS 2025 – Passive Inference Attacks On Split Learning Via Adversarial Regularization appeared first on Security Boulevard.

The exploitation efforts by China-nexus groups and other bad actors against the critical and easily abused React2Shell flaw in the popular React and Next.js software accelerated over the weekend, with threats ranging from stolen credentials and initial access to downloaders, crypto-mining, and the NoodleRat backdoor being executed.

The post Exploitation Efforts Against Critical React2Shell Flaw Accelerate appeared first on Security Boulevard.

The Washington Post last month reported it was among a list of data breach victims of the Oracle EBS-related vulnerabilities, with a threat actor compromising the data of more than 9,700 former and current employees and contractors. Now, a former worker is launching a class-action lawsuit against the Post, claiming inadequate security.

The post Ex-Employee Sues Washington Post Over Oracle EBS-Related Data Breach appeared first on Security Boulevard.

Chinese-sponsored groups are using the popular Brickstorm backdoor to access and gain persistence in government and tech firm networks, part of the ongoing effort by the PRC to establish long-term footholds in agency and critical infrastructure IT environments, according to a report by U.S. and Canadian security offices.

The post China Hackers Using Brickstorm Backdoor to Target Government, IT Entities appeared first on Security Boulevard.

Security and developer teams are scrambling to address a highly critical security flaw in frameworks tied to the popular React JavaScript library. Not only is the vulnerability, which also is in the Next.js framework, easy to exploit, but React is widely used, including in 39% of cloud environments.

The post Dangerous RCE Flaw in React, Next.js Threatens Cloud Environments, Apps appeared first on Security Boulevard.

A threat group dubbed ShadyPanda exploited traditional extension processes in browser marketplaces by uploading legitimate extensions and then quietly weaponization them with malicious updates, infecting 4.3 million Chrome and Edge users with RCE malware and spyware.

The post ShadyPanda’s Years-Long Browser Hack Infected 4.3 Million Users appeared first on Security Boulevard.

Cybersecurity startup Aisle discovered a subtle but dangerous coding error in a Firefox WebAssembly implementation sat undetected for six months despite being shipped with a regression testing capability created by Mozilla to find such a problem.

The post Undetected Firefox WebAssembly Flaw Put 180 Million Users at Risk appeared first on Security Boulevard.

The Cybersecurity Coalition, an industry group of almost a dozen vendors, is urging the Trump Administration and Congress now that the government shutdown is over to take a number of steps to strengthen the country's cybersecurity posture as China, Russia, and other foreign adversaries accelerate their attacks.

The post Cybersecurity Coalition to Government: Shutdown is Over, Get to Work appeared first on Security Boulevard.

The FBI says that account takeover scams this year have resulted in 5,100-plus complaints in the U.S. and $262 million in money stolen, and Bitdefender says the combination of the growing number of ATO incidents and risky consumer behavior is creating an increasingly dangerous environment that will let such fraud expand.

The post FBI: Account Takeover Scammers Stole $262 Million this Year appeared first on Security Boulevard.

Session 4B: Audio Security

Authors, Creators & Presenters:

PAPER
VoiceRadar: Voice Deepfake Detection using Micro-Frequency And Compositional Analysis
Recent advancements in synthetic speech generation, including text-to-speech (TTS) and voice conversion (VC) models, allow the generation of convincing synthetic voices, often referred to as audio deepfakes. These deepfakes pose a growing threat as adversaries can use them to impersonate individuals, particularly prominent figures, on social media or bypass voice authentication systems, thus having a broad societal impact. The inability of state-of-the-art verification systems to detect voice deepfakes effectively is alarming. We propose a novel audio deepfake detection method, VoiceRadar, that augments machine learning with physical models to approximate frequency dynamics and oscillations in audio samples. This significantly enhances detection capabilities. VoiceRadar leverages two main physical models: (i) the Doppler effect to understand frequency changes in audio samples and (ii) drumhead vibrations to decompose complex audio signals into component frequencies. VoiceRadar identifies subtle variations, or micro-frequencies, in the audio signals by applying these models. These micro-frequencies are aggregated to compute the observed frequency, capturing the unique signature of the audio. This observed frequency is integrated into the machine learning algorithm's loss function, enabling the algorithm to recognize distinct patterns that differentiate human-produced audio from AI-generated audio. We constructed a new diverse dataset to comprehensively evaluate VoiceRadar, featuring samples from leading TTS and VC models. Our results demonstrate that VoiceRadar outperforms existing methods in accurately identifying AI-generated audio samples, showcasing its potential as a robust tool for audio deepfake detection.

ABOUT NDSS
The Network and Distributed System Security Symposium (NDSS) fosters information exchange among researchers and practitioners of network and distributed system security. The target audience includes those interested in practical aspects of network and distributed system security, with a focus on actual system design and implementation. A major goal is to encourage and enable the Internet community to apply, deploy, and advance the state of available security technologies.

Our thanks to the Network and Distributed System Security (NDSS) Symposium for publishing their Creators, Authors and Presenter’s superb NDSS Symposium 2025 Conference content on the Organizations' YouTube Channel.

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The Russian state-sponsored group behind the RomCom malware family used the SocGholish loader for the first time to launch an attack on a U.S.-based civil engineering firm, continuing its targeting of organizations that offer support to Ukraine in its ongoing war with its larger neighbor.

The post Russian-Backed Threat Group Uses SocGholish to Target U.S. Company appeared first on Security Boulevard.

Session4A: IoT Security

Authors, Creators & Presenters: Eman Maali (Imperial College London), Omar Alrawi (Georgia Institute of Technology), Julie McCann (Imperial College London)

PAPER
Evaluating Machine Learning-Based IoT Device Identification Models for Security Applications

With the proliferation of IoT devices, network device identification is essential for effective network management and security. Many exhibit performance degradation despite the potential of machine learning-based IoT device identification solutions. Degradation arises from the assumption of static IoT environments that do not account for the diversity of real-world IoT networks, as devices operate in various modes and evolve over time. In this paper, we evaluate current IoT device identification solutions using curated datasets and representative features across different settings. We consider key factors that affect real-world device identification, including modes of operation, spatio-temporal variations, and traffic sampling, and organise them into a set of attributes by which we can evaluate current solutions. We then use machine learning explainability techniques to pinpoint the key causes of performance degradation. This evaluation uncovers empirical evidence of what continuously identifies devices, provides valuable insights, and practical recommendations for network operators to improve their IoT device identification in operational deployments

ABOUT NDSS
The Network and Distributed System Security Symposium (NDSS) fosters information exchange among researchers and practitioners of network and distributed system security. The target audience includes those interested in practical aspects of network and distributed system security, with a focus on actual system design and implementation. A major goal is to encourage and enable the Internet community to apply, deploy, and advance the state of available security technologies.

Our thanks to the Network and Distributed System Security (NDSS) Symposium for publishing their Creators, Authors and Presenter’s superb NDSS Symposium 2025 Conference content on the Organizations' YouTube Channel.

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The post NDSS 2025 – Machine Learning-Based loT Device Identification Models For Security Applications appeared first on Security Boulevard.

A new iteration of the Shai-Hulud malware that ran through npm repositories in September is faster, more dangerous, and more destructive, creating huge numbers of malicious repositories, compromised scripts, and GitHub users attacked, creating one of the most significant supply chain attacks this year.

The post The Latest Shai-Hulud Malware is Faster and More Dangerous appeared first on Security Boulevard.

Session4A: IoT Security

Authors, Creators & Presenters: Haoqiang Wang, Yiwei Fang (Institute of Information Engineering, Chinese Academy of Sciences; School of Cyber Security, University of Chinese Academy of Sciences; Indiana University Bloomington), Yichen Liu (Indiana University Bloomington), Ze Jin (Institute of Information Engineering, Chinese Academy of Sciences; School of Cyber Security, University of Chinese Academy of Sciences; Indiana University Bloomington), Emma Delph (Indiana University Bloomington), Xiaojiang Du (Stevens Institute of Technology), Qixu Liu (Institute of Information Engineering, Chinese Academy of Sciences; School of Cyber Security, University of Chinese Academy of Sciences), Luyi Xing (Indiana University Bloomington)

PAPER

Hidden and Lost Control: on Security Design Risks in IoT User-Facing Matter Controller

Matter is emerging as an IoT industry--unifying standard, aiming to enhance the interoperability among diverse smart home products, enabling them to work securely and seamlessly together. With many popular IoT vendors increasingly supporting Matter in consumer IoT products, we perform a systematic study to investigate how and whether vendors can integrate Matter securely into IoT systems and how well Matter as a standard supports vendors' secure integration. By analyzing Matter development model in the wild, we reveal a new kind of design flaw in user-facing Matter control capabilities and interfaces, called UMCCI flaws, which are exploitable vulnerabilities in the design space and seriously jeopardize necessary control and surveillance capabilities of Matter-enabled devices for IoT users. Therefore we built an automatic tool called UMCCI Checker, enhanced by the large-language model in UI analysis, which enables automatically detecting UMCCI flaws without relying on real IoT devices. Our tool assisted us with studying and performing proof-of-concept attacks on 11 real Matter devices of 8 popular vendors to confirm that the UMCCI flaws are practical and common. We reported UMCCI flaws to related vendors, which have been acknowledged by CSA, Apple, Tuya, Aqara, etc. To help CSA and vendors better understand and avoid security flaws in developing and integrating IoT standards like Matter, we identify two categories of root causes and propose immediate fix recommendations.

ABOUT NDSS
The Network and Distributed System Security Symposium (NDSS) fosters information exchange among researchers and practitioners of network and distributed system security. The target audience includes those interested in practical aspects of network and distributed system security, with a focus on actual system design and implementation. A major goal is to encourage and enable the Internet community to apply, deploy, and advance the state of available security technologies.

Our thanks to the Network and Distributed System Security (NDSS) Symposium for publishing their Creators, Authors and Presenter’s superb NDSS Symposium 2025 Conference content on the Organizations' YouTube Channel.

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The post NDSS 2025 – Hidden And Lost Control: On Security Design Risks In loT User-Facing Matter Controller appeared first on Security Boulevard.

Session4A: IoT Security

Authors, Creators & Presenters: Hangtian Liu (Information Engineering University), Lei Zheng (Institute for Network Sciences and Cyberspace (INSC), Tsinghua University), Shuitao Gan (Laboratory for Advanced Computing and Intelligence Engineering), Chao Zhang (Institute for Network Sciences and Cyberspace (INSC), Tsinghua University), Zicong Gao (Information Engineering University), Hongqi Zhang (Henan Key Laboratory of Information Security), Yishun Zeng (Institute for Network Sciences and Cyberspace (INSC), Tsinghua University), Zhiyuan Jiang (National University of Defense Technology), Jiahai Yang (Institute for Network Sciences and Cyberspace (INSC), Tsinghua University)

PAPER

EAGLEYE: Exposing Hidden Web Interfaces in IoT Devices via Routing Analysis [https://www.ndss-symposium.org/wp-con...](https://www.youtube.com/redirect?event=video_description&redir_token=QUFFLUhqbEEzMmJxSkNwUUhDUkMteHZraTQ1blZ5Sk0zUXxBQ3Jtc0tuZldzQXZxQXJaOGt0VDU2RGNPdGVSbnMzcWxiTVZ1UmJsTzcyaUlCTFdvbmhoWnZRdWQ0UlJiUEs4ekR1UXNCNF9KQmp4UGxKOG5kMHdBdHBiaWh6ckxFaGphY0JVRDZDQ21jUWcyREx2Qy1XVTJqWQ&q=https%3A%2F%2Fwww.ndss-symposium.org%2Fwp-content%2Fuploads%2F2025-399-paper.pdf&v=qXDD2iiIeCg) Hidden web interfaces, i.e., undisclosed access channels in IoT devices, introduce great security risks and have resulted in severe attacks in recent years. However, the definition of such threats is vague, and few solutions are able to discover them. Due to their hidden nature, traditional bug detection solutions (e.g., taint analysis, fuzzing) are hard to detect them. In this paper, we present a novel solution EAGLEYE to automatically expose hidden web interfaces in IoT devices. By analyzing input requests to public interfaces, we first identify routing tokens within the requests, i.e., those values (e.g., actions or file names) that are referenced and used as index by the firmware code (routing mechanism) to find associated handler functions. Then, we utilize modern large language models to analyze the contexts of such routing tokens and deduce their common pattern, and then infer other candidate values (e.g., other actions or file names) of these tokens. Lastly, we perform a hidden-interface directed black-box fuzzing, which mutates the routing tokens in input requests with these candidate values as the high-quality dictionary. We have implemented a prototype of EAGLEYE and evaluated it on 13 different commercial IoT devices. EAGLEYE successfully found 79 hidden interfaces, 25X more than the state-of-the-art (SOTA) solution IoTScope. Among them, we further discovered 29 unknown vulnerabilities including backdoor, XSS (cross-site scripting), command injection, and information leakage, and have received 7 CVEs.

ABOUT NDSS
The Network and Distributed System Security Symposium (NDSS) fosters information exchange among researchers and practitioners of network and distributed system security. The target audience includes those interested in practical aspects of network and distributed system security, with a focus on actual system design and implementation. A major goal is to encourage and enable the Internet community to apply, deploy, and advance the state of available security technologies.

Our thanks to the Network and Distributed System Security (NDSS) Symposium for publishing their Creators, Authors and Presenter’s superb NDSS Symposium 2025 Conference content on the Organizations' YouTube Channel.

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The post NDSS 2025 – EAGLEYE: Exposing Hidden Web Interfaces In loT Devices Via Routing Analysis appeared first on Security Boulevard.

Huntress threat researchers are tracking a ClickFix campaign that includes a variant of the scheme in which the malicious code is hidden in the fake image of a Windows Update and, if inadvertently downloaded by victims, will deploy the info-stealing malware LummaC2 and Rhadamanthys.

The post Attackers are Using Fake Windows Updates in ClickFix Scams appeared first on Security Boulevard.

Session4A: IoT Security

Authors, Creators & Presenters: Christopher Ellis (The Ohio State University), Yue Zhang (Drexel University), Mohit Kumar Jangid (The Ohio State University), Shixuan Zhao (The Ohio State University), Zhiqiang Lin (The Ohio State University)

PAPER

Deanonymizing Device Identities via Side-channel Attacks in Exclusive-use IoTs & Mitigation Wireless technologies like Bluetooth Low Energy (BLE) and Wi-Fi are essential to the Internet of Things (IoT), facilitating seamless device communication without physical connections. However, this convenience comes at a cost--exposed data exchanges that are susceptible to observation by attackers, leading to serious security and privacy threats such as device tracking. Although protocol designers have traditionally relied on strategies like address and identity randomization as a countermeasure, our research reveals that these attacks remain a significant threat due to a historically overlooked, fundamental flaw in exclusive-use wireless communication. We define exclusive-use as a scenario where devices are designed to provide functionality solely to an associated or paired device. The unique communication patterns inherent in these relationships create an observable boolean side-channel that attackers can exploit to discover whether two devices "trust" each other. This information leak allows for the deanonymization of devices, enabling tracking even in the presence of modern countermeasures. We introduce our tracking attacks as IDBleed and demonstrate that BLE and Wi-Fi protocols that support confidentiality, integrity, and authentication remain vulnerable to deanonymization due to this fundamental flaw in exclusive-use communication patterns. Finally, we propose and quantitatively evaluate a generalized, privacy-preserving mitigation we call Anonymization Layer to find a negligible 2% approximate overhead in performance and power consumption on tested smartphones and PCs.

ABOUT NDSS
The Network and Distributed System Security Symposium (NDSS) fosters information exchange among researchers and practitioners of network and distributed system security. The target audience includes those interested in practical aspects of network and distributed system security, with a focus on actual system design and implementation. A major goal is to encourage and enable the Internet community to apply, deploy, and advance the state of available security technologies.

Our thanks to the Network and Distributed System Security (NDSS) Symposium for publishing their Creators, Authors and Presenter’s superb NDSS Symposium 2025 Conference content on the Organizations' YouTube Channel.

Permalink

The post NDSS 2025 – Deanonymizing Device Identities Via Side-Channel Attacks In Exclusive-Use IoTs appeared first on Security Boulevard.

SitusAMC, a services provider with clients like JP MorganChase and Citi, said its systems were hacked and the data of clients and their customers possibly compromised, sending banks and other firms scrambling. The data breach illustrates the growth in the number of such attacks on third-party providers in the financial services sector.

The post Hack of SitusAMC Puts Data of Financial Services Firms at Risk appeared first on Security Boulevard.

SESSION
Session 3D: AI Safety

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Authors, Creators & Presenters: Yan Pang (University of Virginia), Aiping Xiong (Penn State University), Yang Zhang (CISPA Helmholtz Center for Information Security), Tianhao Wang (University of Virginia)

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PAPER
Towards Understanding Unsafe Video Generation
Video generation models (VGMs) have demonstrated the capability to synthesize high-quality output. It is important to understand their potential to produce unsafe content, such as violent or terrifying videos. In this work, we provide a comprehensive understanding of unsafe video generation.

First, to confirm the possibility that these models could indeed generate unsafe videos, we choose unsafe content generation prompts collected from 4chan and Lexica, and three open-source SOTA VGMs to generate unsafe videos. After filtering out duplicates and poorly generated content, we created an initial set of 2112 unsafe videos from an original pool of 5607 videos. Through clustering and thematic coding analysis of these generated videos, we identify 5 unsafe video categories: Distorted/Weird, Terrifying, Pornographic, Violent/Bloody, and Political. With IRB approval, we then recruit online participants to help label the generated videos. Based on the annotations submitted by 403 participants, we identified 937 unsafe videos from the initial video set. With the labeled information and the corresponding prompts, we created the first dataset of unsafe videos generated by VGMs. We then study possible defense mechanisms to prevent the generation of unsafe videos. Existing defense methods in image generation focus on filtering either input prompt or output results. We propose a new approach called sysname, which works within the model's internal sampling process. sysname can achieve 0.90 defense accuracy while reducing time and computing resources by 10 times when sampling a large number of unsafe prompts. Our experiment includes three open-source SOTA video diffusion models, each achieving accuracy rates of 0.99, 0.92, and 0.91, respectively. Additionally, our method was tested with adversarial prompts and on image-to-video diffusion models, and achieved nearly 1.0 accuracy on both settings. Our method also shows its interoperability by improving the performance of other defenses when combined with them.

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ABOUT NDSS
The Network and Distributed System Security Symposium (NDSS) fosters information exchange among researchers and practitioners of network and distributed system security. The target audience includes those interested in practical aspects of network and distributed system security, with a focus on actual system design and implementation. A major goal is to encourage and enable the Internet community to apply, deploy, and advance the state of available security technologies.

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Our thanks to the Network and Distributed System Security (NDSS) Symposium for publishing their Creators, Authors and Presenter’s superb NDSS Symposium 2025 Conference content on the Organizations' YouTube Channel.

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The post NDSS 2025 – Towards Understanding Unsafe Video Generation appeared first on Security Boulevard.

SESSION
Session 3D: AI Safety

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Authors, Creators & Presenters: Haotian Zhu (Nanjing University of Science and Technology), Shuchao Pang (Nanjing University of Science and Technology), Zhigang Lu (Western Sydney University), Yongbin Zhou (Nanjing University of Science and Technology), Minhui Xue (CSIRO's Data61)

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PAPER
GAP-Diff: Protecting JPEG-Compressed Images From Diffusion-Based Facial Customization
Text-to-image diffusion model's fine-tuning technology allows people to easily generate a large number of customized photos using limited identity images. Although this technology is easy to use, its misuse could lead to violations of personal portraits and privacy, with false information and harmful content potentially causing further harm to individuals. Several methods have been proposed to protect faces from customization via adding protective noise to user images by disrupting the fine-tuned models.
Unfortunately, simple pre-processing techniques like JPEG compression, a normal pre-processing operation performed by modern social networks, can easily erase the protective effects of existing methods. To counter JPEG compression and other potential pre-processing, we propose GAP-Diff, a framework of Generating data with Adversarial Perturbations for text-to-image Diffusion models using unsupervised learning-based optimization, including three functional modules. Specifically, our framework learns robust representations against JPEG compression by backpropagating gradient information through a pre-processing simulation module while learning adversarial characteristics for disrupting fine-tuned text-to-image diffusion models. Furthermore, we achieve an adversarial mapping from clean images to protected images by designing adversarial losses against these fine-tuning methods and JPEG compression, with stronger protective noises within milliseconds. Facial benchmark experiments, compared to state-of-the-art protective methods, demonstrate that GAP-Diff significantly enhances the resistance of protective noise to JPEG compression, thereby better safeguarding user privacy and copyrights in the digital world.

ABOUT NDSS
The Network and Distributed System Security Symposium (NDSS) fosters information exchange among researchers and practitioners of network and distributed system security. The target audience includes those interested in practical aspects of network and distributed system security, with a focus on actual system design and implementation. A major goal is to encourage and enable the Internet community to apply, deploy, and advance the state of available security technologies.

Our thanks to the Network and Distributed System Security (NDSS) Symposium for publishing their Creators, Authors and Presenter’s superb NDSS Symposium 2025 Conference content on the Organizations' YouTube Channel.

Permalink

The post NDSS 2025 – GAP-Diff: Protecting JPEG-Compressed Images From Diffusion-Based Facial Customization appeared first on Security Boulevard.

Agencies with the US and other countries have gone hard after bulletproof hosting services providers this month, including Media Land, Hypercore, and associated companies and individuals, while the FiveEyes threat intelligence alliance published BPH mitigation guidelines for ISPs, cloud providers, and network defenders.

The post U.S., International Partners Target Bulletproof Hosting Services appeared first on Security Boulevard.

An attack on the app of CRM platform-provider Gainsight led to the data of hundreds of Salesforce customers being compromised, highlighting the ongoing threats posed by third-party software in SaaS environments and illustrating how one data breach can lead to others, cybersecurity pros say.

The post Salesforce: Some Customer Data Accessed via Gainsight Breach appeared first on Security Boulevard.

SESSION
Session 3D: AI Safety

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Authors, Creators & Presenters: Shuo Shao (Zhejiang University), Yiming Li (Zhejiang University), Hongwei Yao (Zhejiang University), Yiling He (Zhejiang University), Zhan Qin (Zhejiang University), Kui Ren (Zhejiang University)

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PAPER
Explanation as a Watermark: Towards Harmless and Multi-bit Model Ownership Verification via Watermarking Feature Attribution
Ownership verification is currently the most critical and widely adopted post-hoc method to safeguard model copyright. In general, model owners exploit it to identify whether a given suspicious third-party model is stolen from them by examining whether it has particular properties 'inherited' from their released models. Currently, backdoor-based model watermarks are the primary and cutting-edge methods to implant such properties in the released models. However, backdoor-based methods have two fatal drawbacks, including harmfulness and ambiguity. The former indicates that they introduce maliciously controllable misclassification behaviors ( backdoor) to the watermarked released models. The latter denotes that malicious users can easily pass the verification by finding other misclassified samples, leading to ownership ambiguity.

In this paper, we argue that both limitations stem from the 'zero-bit' nature of existing watermarking schemes, where they exploit the status (misclassified) of predictions for verification. Motivated by this understanding, we design a new watermarking paradigm "Explanation as a Watermark (EaaW)", that implants verification behaviors into the explanation of feature attribution instead of model predictions. Specifically, EaaW embeds a 'multi-bit' watermark into the feature attribution explanation of specific trigger samples without changing the original prediction. We correspondingly design the watermark embedding and extraction algorithms inspired by explainable artificial intelligence. In particular, our approach can be used for different tasks (image classification and text generation). Extensive experiments verify the effectiveness and harmlessness of our EaaW and its resistance to potential attacks.

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ABOUT NDSS
The Network and Distributed System Security Symposium (NDSS) fosters information exchange among researchers and practitioners of network and distributed system security. The target audience includes those interested in practical aspects of network and distributed system security, with a focus on actual system design and implementation. A major goal is to encourage and enable the Internet community to apply, deploy, and advance the state of available security technologies.

Our thanks to the Network and Distributed System Security (NDSS) Symposium for publishing their Creators, Authors and Presenter’s superb NDSS Symposium 2025 Conference content on the Organizations' YouTube Channel.

Permalink

The post NDSS 2025 – Explanation As A Watermark appeared first on Security Boulevard.

The SEC dismissed the remain charges in the lawsuit filed in 2023 against software maker SolarWinds and CISO Timothy Brown in the wake of the massive Sunburst supply chain attack, in which a Russian nation-state group installed a malicious update into SolarWInds software that then compromised the systems of some customers.

The post SEC Dismisses Remains of Lawsuit Against SolarWinds and Its CISO appeared first on Security Boulevard.

SESSION
Session 3D: Al Safety

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Authors, Creators & Presenters: Yutong Wu (Nanyang Technological University), Jie Zhang (Centre for Frontier AI Research, Agency for Science, Technology and Research (A*STAR), Singapore), Florian Kerschbaum (University of Waterloo), Tianwei Zhang (Nanyang Technological University)

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PAPER
THEMIS: Regulating Textual Inversion for Personalized Concept Censorship

Personalization has become a crucial demand in the Generative AI technology. As the pre-trained generative model (e.g., stable diffusion) has fixed and limited capability, it is desirable for users to customize the model to generate output with new or specific concepts. Fine-tuning the pre-trained model is not a promising solution, due to its high requirements of computation resources and data. Instead, the emerging personalization approaches make it feasible to augment the generative model in a lightweight manner. However, this also induces severe threats if such advanced techniques are misused by malicious users, such as spreading fake news or defaming individual reputations. Thus, it is necessary to regulate personalization models (i.e., achieve concept censorship) for their development and advancement. In this paper, we focus on the regulation of a popular personalization technique dubbed textbf{Textual Inversion (TI)}, which can customize Text-to-Image (T2I) generative models with excellent performance. TI crafts the word embedding that contains detailed information about a specific object. Users can easily add the word embedding to their local T2I model, like the public Stable Diffusion (SD) model, to generate personalized images. The advent of TI has brought about a new business model, evidenced by the public platforms for sharing and selling word embeddings (e.g., Civitai [1]). Unfortunately, such platforms also allow malicious users to misuse the word embeddings to generate unsafe content, causing damages to the concept owners. We propose THEMIS to achieve the personalized concept censorship. Its key idea is to leverage the backdoor technique for good by injecting positive backdoors into the TI embeddings. Briefly, the concept owner selects some sensitive words as triggers during the training of TI, which will be censored for normal use. In the subsequent generation stage, if a malicious user combines the sensitive words with the personalized embeddings as final prompts, the T2I model will output a pre-defined target image rather than images including the desired malicious content. To demonstrate the effectiveness of THEMIS, we conduct extensive experiments on the TI embeddings with Latent Diffusion and Stable Diffusion, two prevailing open-sourced T2I models. The results demonstrate that THEMIS is capable of preventing Textual Inversion from cooperating with sensitive words meanwhile guaranteeing its pristine utility. Furthermore, THEMIS is general to different uses of sensitive words, including different locations, synonyms, and combinations of sensitive words. It can also resist different types of potential and adaptive attacks. Ablation studies are also conducted to verify our design.

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ABOUT NDSS
The Network and Distributed System Security Symposium (NDSS) fosters information exchange among researchers and practitioners of network and distributed system security. The target audience includes those interested in practical aspects of network and distributed system security, with a focus on actual system design and implementation. A major goal is to encourage and enable the Internet community to apply, deploy, and advance the state of available security technologies.

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Our thanks to the Network and Distributed System Security (NDSS) Symposium for publishing their Creators, Authors and Presenter’s superb NDSS Symposium 2025 Conference content on the Organizations' YouTube Channel.

Permalink

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SESSION
Session 3D: Al Safety

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Authors, Creators & Presenters: Miaomiao Wang (Shanghai University), Guang Hua (Singapore Institute of Technology), Sheng Li (Fudan University), Guorui Feng (Shanghai University)

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PAPER
A Key-Driven Framework for Identity-Preserving Face Anonymization

Virtual faces are crucial content in the metaverse. Recently, attempts have been made to generate virtual faces for privacy protection. Nevertheless, these virtual faces either permanently remove the identifiable information or map the original identity into a virtual one, which loses the original identity forever. In this study, we first attempt to address the conflict between privacy and identifiability in virtual faces, where a key-driven face anonymization and authentication recognition (KFAAR) framework is proposed. Concretely, the KFAAR framework consists of a head posture-preserving virtual face generation (HPVFG) module and a key-controllable virtual face authentication (KVFA) module. The HPVFG module uses a user key to project the latent vector of the original face into a virtual one. Then it maps the virtual vectors to obtain an extended encoding, based on which the virtual face is generated. By simultaneously adding a head posture and facial expression correction module, the virtual face has the same head posture and facial expression as the original face. During the authentication, we propose a KVFA module to directly recognize the virtual faces using the correct user key, which can obtain the original identity without exposing the original face image. We also propose a multi-task learning objective to train HPVFG and KVFA. Extensive experiments demonstrate the advantages of the proposed HPVFG and KVFA modules, which effectively achieve both facial anonymity and identifiability.

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ABOUT NDSS
The Network and Distributed System Security Symposium (NDSS) fosters information exchange among researchers and practitioners of network and distributed system security. The target audience includes those interested in practical aspects of network and distributed system security, with a focus on actual system design and implementation. A major goal is to encourage and enable the Internet community to apply, deploy, and advance the state of available security technologies.

Our thanks to the Network and Distributed System Security (NDSS) Symposium for publishing their Creators, Authors and Presenter’s superb NDSS Symposium 2025 Conference content on the Organizations' YouTube Channel.

Permalink

The post NDSS 2025 – A Key-Driven Framework For Identity-Preserving Face Anonymization appeared first on Security Boulevard.

In today’s fast-evolving digital world, organizations increasingly rely on hybrid workforces, cloud-first strategies, and distributed infrastructures to gain agility and scalability. This transformation has expanded the network into a complex ecosystem spanning on-premises, cloud, and remote endpoints, vastly increasing the attack surface. Cyber adversaries exploit this complexity using stealth techniques like encrypted tunnels, credential misuse,

The post Why Network Monitoring Matters: How Seceon Enables Proactive, Intelligent Cyber Defence appeared first on Seceon Inc.

The post Why Network Monitoring Matters: How Seceon Enables Proactive, Intelligent Cyber Defence appeared first on Security Boulevard.

Overview Recently, NSFOCUS CERT detected that Fortinet issued a security bulletin to fix the FortiWeb authentication bypass and command injection vulnerability (CVE-2025-64446/CVE-2025-58034); Combined exploitation can realize unauthorized remote code execution. At present, the vulnerability details and PoC have been made public, and wild exploitation has been found. Relevant users are requested to take measures to […]

The post Fortinet FortiWeb Authentication Bypass and Command Injection Vulnerability (CVE-2025-64446/CVE-2025-58034) Notice appeared first on NSFOCUS, Inc., a global network and cyber security leader, protects enterprises and carriers from advanced cyber attacks..

The post Fortinet FortiWeb Authentication Bypass and Command Injection Vulnerability (CVE-2025-64446/CVE-2025-58034) Notice appeared first on Security Boulevard.

SESSION
Session 3C: Mobile Security

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Authors, Creators & Presenters: Runze Zhang (Georgia Institute of Technology), Mingxuan Yao (Georgia Institute of Technology), Haichuan Xu (Georgia Institute of Technology), Omar Alrawi (Georgia Institute of Technology), Jeman Park (Kyung Hee University), Brendan Saltaformaggio (Georgia Institute of Technology)

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PAPER
Hitchhiking Vaccine: Enhancing Botnet Remediation With Remote Code Deployment Reuse
For decades, law enforcement and commercial entities have attempted botnet takedowns with mixed success. These efforts, relying on DNS sink-holing or seizing C&C infrastructure, require months of preparation and often omit the cleanup of left-over infected machines. This allows botnet operators to push updates to the bots and re-establish their control. In this paper, we expand the goal of malware takedowns to include the covert and timely removal of frontend bots from infected devices. Specifically, this work proposes seizing the malware's built-in update mechanism to distribute crafted remediation payloads. Our research aims to enable this necessary but challenging remediation step after obtaining legal permission. We developed ECHO, an automated malware forensics pipeline that extracts payload deployment routines and generates remediation payloads to disable or remove the frontend bots on infected devices. Our study of 702 Android malware shows that 523 malware can be remediated via ECHO's takedown approach, ranging from covertly warning users about malware infection to uninstalling the malware.

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ABOUT NDSS
The Network and Distributed System Security Symposium (NDSS) fosters information exchange among researchers and practitioners of network and distributed system security. The target audience includes those interested in practical aspects of network and distributed system security, with a focus on actual system design and implementation. A major goal is to encourage and enable the Internet community to apply, deploy, and advance the state of available security technologies.

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Our thanks to the **[Network and Distributed System Security (NDSS) Symposium][1]** for publishing their Creators, Authors and Presenter’s superb **[NDSS Symposium 2025 Conference][2]** content on the **[organization’s’][1]** **[YouTube][3]** channel.

Permalink

The post NDSS 2025 – Hitchhiking Vaccine: Enhancing Botnet Remediation With Remote Code Deployment Reuse appeared first on Security Boulevard.

SESSION
Session 3C: Mobile Security

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Authors, Creators & Presenters: Chang Yue (Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China), Kai Chen (Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China), Zhixiu Guo (Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China), Jun Dai, Xiaoyan Sun (Department of Computer Science, Worcester Polytechnic Institute), Yi Yang (Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China)

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PAPER
What's Done Is Not What's Claimed: Detecting and Interpreting Inconsistencies in App Behaviors
The widespread use of mobile apps meets user needs but also raises security concerns. Current security analysis methods often fall short in addressing user concerns as they do not parse app behavior from the user's standpoint, leading to users not fully understanding the risks within the apps and unknowingly exposing themselves to privacy breaches. On one hand, their analysis and results are usually presented at the code level, which may not be comprehensible to users. On the other hand, they neglect to account for the users' perceptions of the app behavior. In this paper, we aim to extract user-related behaviors from apps and explain them to users in a comprehensible natural language form, enabling users to perceive the gap between their expectations and the app's actual behavior, and assess the risks within the inconsistencies independently. Through experiments, our tool InconPreter is shown to effectively extract inconsistent behaviors from apps and provide accurate and reasonable explanations. InconPreter achieves an inconsistency identification precision of 94.89% on our labeled dataset, and a risk analysis accuracy of 94.56% on widely used Android malware datasets. When applied to real-world (wild) apps, InconPreter identifies 1,664 risky inconsistent behaviors from 413 apps out of 10,878 apps crawled from Google Play, including the leakage of location, SMS, and contact information, as well as unauthorized audio recording, etc., potentially affecting millions of users. Moreover, InconPreter can detect some behaviors that are not identified by previous tools, such as unauthorized location disclosure in various scenarios (e.g. taking photos, chatting, and enabling mobile hotspots, etc.). We conduct a thorough analysis of the discovered behaviors to deepen the understanding of inconsistent behaviors, thereby helping users better manage their privacy and providing insights for privacy design in further app development.

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ABOUT NDSS
The Network and Distributed System Security Symposium (NDSS) fosters information exchange among researchers and practitioners of network and distributed system security. The target audience includes those interested in practical aspects of network and distributed system security, with a focus on actual system design and implementation. A major goal is to encourage and enable the Internet community to apply, deploy, and advance the state of available security technologies.

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Our thanks to the **[Network and Distributed System Security (NDSS) Symposium][1]** for publishing their Creators, Authors and Presenter’s superb **[NDSS Symposium 2025 Conference][2]** content on the **[organization’s’][1]** **[YouTube][3]** channel.

Permalink

The post NDSS 2025 – Detecting And Interpreting Inconsistencies In App Behaviors appeared first on Security Boulevard.

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SESSION
Session 3C: Mobile Security

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Authors, Creators & Presenters: Yuqing Yang (The Ohio State University), Yue Zhang (Drexel University), Zhiqiang Lin (The Ohio State University)

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PAPER
Understanding Miniapp Malware: Identification, Dissection, and Characterization
Super apps, serving as centralized platforms that manage user information and integrate third-party miniapps, have revolutionized mobile computing but also introduced significant security risks from malicious miniapps. Despite the mandatory miniapp vetting enforced to the built-in miniapp store, the threat of evolving miniapp malware persists, engaging in a continual cat-and-mouse game with platform security measures. However, compared with traditional paradigms such as mobile and web computing, there has been a lack of miniapp malware dataset available for the community to explore, hindering the generation of crucial insights and the development of robust detection techniques. In response to this, this paper addresses the scarcely explored territory of malicious miniapp analysis, dedicating over three year to identifying, dissecting, and examining the risks posed by these miniapps, resulting in the first miniapp malware dataset now available to aid future studies to enhance the security of super app ecosystems. To build the dataset, our primary focus has been on the WeChat platform, the largest super app, hosting millions of miniapps and serving a billion users. Over an extensive period, we collected over 4.5 million miniapps, identifying a subset (19, 905) as malicious through a rigorous cross-check process: 1) applying static signatures derived from real-world cases, and 2) confirming that the miniapps were delisted and removed from the market by the platform. With these identified samples, we proceed to characterize them, focusing on their lifecycle including propagation, activation, as well as payload execution. Additionally, we analyzed the collected malware samples using real-world cases to demonstrate their practical security impact. Our findings reveal that these malware frequently target user privacy, leverage social network sharing capabilities to disseminate unauthorized services, and manipulate the advertisement-based revenue model to illicitly generate profits. These actions result in significant privacy and financial harm to both users and the platform.

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ABOUT NDSS
The Network and Distributed System Security Symposium (NDSS) fosters information exchange among researchers and practitioners of network and distributed system security. The target audience includes those interested in practical aspects of network and distributed system security, with a focus on actual system design and implementation. A major goal is to encourage and enable the Internet community to apply, deploy, and advance the state of available security technologies.

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Our thanks to the **[Network and Distributed System Security (NDSS) Symposium][1]** for publishing their Creators, Authors and Presenter’s superb **[NDSS Symposium 2025 Conference][2]** content on the **[organization’s’][1]** **[YouTube][3]** channel.

Permalink

The post NDSS 2025 – Understanding Miniapp Malware: Identification, Dissection, And Characterization appeared first on Security Boulevard.

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SESSION
Session 3C: Mobile Security

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Authors, Creators & Presenters: Yizhe Shi (Fudan University), Zhemin Yang (Fudan University), Kangwei Zhong (Fudan University), Guangliang Yang (Fudan University), Yifan Yang (Fudan University), Xiaohan Zhang (Fudan University), Min Yang (Fudan University)

PAPER
The Skeleton Keys: A Large Scale Analysis of Credential Leakage in Mini-apps
In recent years, the app-in-app paradigm, involving super-app and mini-app, has been becoming increasingly popular in the mobile ecosystem. Super-app platforms offer mini-app servers access to a suite of powerful and sensitive services, including payment processing and mini-app analytics. This access empowers mini-app servers to enhance their offerings with robust and practical functionalities and better serve their mini-apps. To safeguard these essential services, a credential-based authentication system has been implemented, facilitating secure access between super-app platforms and mini-app servers. However, the design and workflow of the crucial credential mechanism still remain unclear. More importantly, its security has not been comprehensively understood or explored to date. In this paper, we conduct the first systematic study of the credential system in the app-in-app paradigm and draw the security landscape of credential leakage risks. Consequently, our study shows that 21 popular super-app platforms delegate sensitive services to mini-app servers with seven types of credentials. Unfortunately, these credentials may suffer from leakage threats caused by malicious mini-app users, posing serious security threats to both super-app platforms and mini-app servers. Then, we design and implement a novel credential security verification tool, called KeyMagnet, that can effectively assess the security implications of credential leakage. To tackle unstructured and dynamically retrieved credentials in the app-in-app paradigm, KeyMagnet extracts and understands the semantics of credential-use in mini-apps and verifies their security. Last, by applying KeyMagnet on 413,775 real-world mini-apps of 6 super-app platforms, 84,491 credential leaks are detected, spanning over 54,728 mini-apps. We confirm credential leakage can cause serious security hazards, such as hijacking the accounts of all mini-app users and stealing users' sensitive data. In response, we have engaged in responsible vulnerability disclosure with the corresponding developers and are actively helping them resolve these issues. At the time of writing, 89 reported issues have been assigned with CVE IDs.

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ABOUT NDSS
The Network and Distributed System Security Symposium (NDSS) fosters information exchange among researchers and practitioners of network and distributed system security. The target audience includes those interested in practical aspects of network and distributed system security, with a focus on actual system design and implementation. A major goal is to encourage and enable the Internet community to apply, deploy, and advance the state of available security technologies.

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Our thanks to the **[Network and Distributed System Security (NDSS) Symposium][1]** for publishing their Creators, Authors and Presenter’s superb **[NDSS Symposium 2025 Conference][2]** content on the **[organization’s’][1]** **[YouTube][3]** channel.

The post NDSS 2025 – The Skeleton Keys: A Large Scale Analysis Of Credential Leakage In Mini-Apps appeared first on Security Boulevard.

Four U.S. citizens and a Ukrainian national pleaded guilty to their roles in a North Korean IT worker scam that victimized more than 135 U.S. companies and netted more than $2.2 million for the DPRK regime and is military and weapons programs.

The post 4 U.S. Citizens, Ukrainian Plead Guilty in N. Korea IT Worker Scheme appeared first on Security Boulevard.

SESSION
Session 3C: Mobile Security

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Authors, Creators & Presenters: Xiangyu Guo (University of Toronto), Akshay Kawlay (University of Toronto), Eric Liu (University of Toronto), David Lie (University of Toronto)

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PAPER
EvoCrawl: Exploring Web Application Code and State using Evolutionary Search
As more critical services move onto the web, it has become increasingly important to detect and address vulnerabilities in web applications. These vulnerabilities only occur under specific conditions: when 1) the vulnerable code is executed and 2) the web application is in the required state. If the application is not in the required state, then even if the vulnerable code is executed, the vulnerability may not be triggered. Previous work naively explores the application state by filling every field and triggering every JavaScript event before submitting HTML forms. However, this simplistic approach can fail to satisfy constraints between the web page elements, as well as input format constraints. To address this, we present EvoCrawl, a web crawler that uses evolutionary search to efficiently find different sequences of web interactions. EvoCrawl finds sequences that can successfully submit inputs to web applications and thus explore more code and server-side states than previous approaches. To assess the benefits of EvoCrawl we evaluate it against three state-of-the-art vulnerability scanners on ten web applications. We find that EvoCrawl achieves better code coverage due to its ability to execute code that can only be executed when the application is in a particular state. On average, EvoCrawl achieves a 59% increase in code coverage and successfully submits HTML forms 5x more frequently than the next best tool. By integrating IDOR and XSS vulnerability scanners, we used EvoCrawl to find eight zero-day IDOR and XSS vulnerabilities in WordPress, HotCRP, Kanboard, ImpressCMS, and GitLab.

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ABOUT NDSS
The Network and Distributed System Security Symposium (NDSS) fosters information exchange among researchers and practitioners of network and distributed system security. The target audience includes those interested in practical aspects of network and distributed system security, with a focus on actual system design and implementation. A major goal is to encourage and enable the Internet community to apply, deploy, and advance the state of available security technologies.

-----------

Our thanks to the **[Network and Distributed System Security (NDSS) Symposium][1]** for publishing their Creators, Authors and Presenter’s superb **[NDSS Symposium 2025 Conference][2]** content on the **[organization’s’][1]** **[YouTube][3]** channel.

Permalink

The post NDSS 2025 – EvoCrawl: Exploring Web Application Code And State Using Evolutionary Search appeared first on Security Boulevard.

Microsoft mitigated what it called a record-breaking DDoS attack by bad actor using the Aisuru botnet, a collection of about 300,000 infected IoT devices. The size of the attack and the botnet used in it is the latest example of a DDoS environment that continues to scale in pace with the internet.

The post Microsoft Fends Off Massive DDoS Attack by Aisuru Botnet Operators appeared first on Security Boulevard.

SESSION
Session 3B: Wireless, Cellular & Satellite Security

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Authors, Creators & Presenters: Philipp Mackensen (Ruhr University Bochum), Paul Staat (Max Planck Institute for Security and Privacy), Stefan Roth (Ruhr University Bochum), Aydin Sezgin (Ruhr University Bochum), Christof Paar (Max Planck Institute for Security and Privacy), Veelasha Moonsamy (Ruhr University Bochum)

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PAPER

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Spatial-Domain Wireless Jamming with Reconfigurable Intelligent Surfaces

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Wireless communication infrastructure is a cornerstone of modern digital society, yet it remains vulnerable to the persistent threat of wireless jamming. Attackers can easily create radio interference to overshadow legitimate signals, leading to denial of service. The broadcast nature of radio signal propagation makes such attacks possible in the first place, but at the same time poses a challenge for the attacker: The jamming signal does not only reach the victim device but also other neighboring devices, preventing precise attack targeting. In this work, we solve this challenge by leveraging the emerging RIS technology, for the first time, for precise delivery of jamming signals. In particular, we propose a novel approach that allows for environment-adaptive spatial control of wireless jamming signals, granting a new degree of freedom to perform jamming attacks. We explore this novel method with extensive experimentation and demonstrate that our approach can disable the wireless communication of one or multiple victim devices while leaving neighboring devices unaffected. Notably, our method extends to challenging scenarios where wireless devices are very close to each other: We demonstrate complete denial-of-service of a Wi-Fi device while a second device located at a distance as close as 5 mm remains unaffected, sustaining wireless communication at a data rate of 25 Mbit/s. Lastly, we conclude by proposing potential countermeasures to thwart RIS-based spatial domain wireless jamming attacks.

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ABOUT NDSS
The Network and Distributed System Security Symposium (NDSS) fosters information exchange among researchers and practitioners of network and distributed system security. The target audience includes those interested in practical aspects of network and distributed system security, with a focus on actual system design and implementation. A major goal is to encourage and enable the Internet community to apply, deploy, and advance the state of available security technologies.

-----------

Our thanks to the **[Network and Distributed System Security (NDSS) Symposium][1]** for publishing their Creators, Authors and Presenter’s superb **[NDSS Symposium 2025 Conference][2]** content on the **[organization’s’][1]** **[YouTube][3]** channel.

Permalink

The post NDSS 2025 – Spatial-Domain Wireless Jamming With Reconfigurable Intelligent Surfaces appeared first on Security Boulevard.

SESSION
Session 3B: Wireless, Cellular & Satellite Security

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Authors, Creators & Presenters: Jiska Classen (Hasso Plattner Institute, University of Potsdam), Alexander Heinrich (TU Darmstadt, Germany), Fabian Portner (TU Darmstadt, Germany), Felix Rohrbach (TU Darmstadt, Germany), Matthias Hollick (TU Darmstadt, Germany)

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PAPER

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Starshields for iOS: Navigating the Security Cosmos in Satellite Communication

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Apple has integrated satellite communication into their latest iPhones, enabling emergency communication, road- side assistance, location sharing with friends, iMessage, and SMS. This technology allows communication when other wireless services are unavailable. However, the use of satellites poses restrictions on bandwidth and delay, making it difficult to use modern communication protocols with their security and privacy guarantees. To overcome these challenges, Apple designed and implemented a proprietary satellite communication protocol to address these limitations. We are the first to successfully reverse-engineer this protocol and analyze its security and privacy properties. In addition, we develop a simulation-based testbed for testing emergency services without causing emergency calls. Our tests reveal protocol and infrastructure design issues. For example, compact protocol messages come at the cost of missing integrity protection and require an internet-based setup phase. We further demonstrate various restriction bypasses, such as misusing location sharing to send arbitrary text messages on old iOS versions, and sending iMessages over satellite from region-locked countries. These bypasses allow us to overcome censorship and operator control of text messaging services.

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ABOUT NDSS
The Network and Distributed System Security Symposium (NDSS) fosters information exchange among researchers and practitioners of network and distributed system security. The target audience includes those interested in practical aspects of network and distributed system security, with a focus on actual system design and implementation. A major goal is to encourage and enable the Internet community to apply, deploy, and advance the state of available security technologies.

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Our thanks to the **[Network and Distributed System Security (NDSS) Symposium][1]** for publishing their Creators, Authors and Presenter’s superb **[NDSS Symposium 2025 Conference][2]** content on the **[organization’s’][1]** **[YouTube][3]** channel.

Permalink

The post NDSS 2025 – Detecting IMSI-Catchers By Characterizing Identity Exposing Messages In Cellular Traffic appeared first on Security Boulevard.

SESSION
Session 3B: Wireless, Cellular & Satellite Security

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Authors, Creators & Presenters: Tyler Tucker (University of Florida), Nathaniel Bennett (University of Florida), Martin Kotuliak (ETH Zurich), Simon Erni (ETH Zurich), Srdjan Capkun (ETH Zuerich), Kevin Butler (University of Florida), Patrick Traynor (University of Florida)

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PAPER

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Detecting IMSI-Catchers By Characterizing Identity Exposing Messages In Cellular Traffic

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IMSI-Catchers allow parties other than cellular network providers to covertly track mobile device users. While the research community has developed many tools to combat this problem, current solutions focus on correlated behavior and are therefore subject to substantial false classifications. In this paper, we present a standards-driven methodology that focuses on the messages an IMSI-Catcher textit(must) use to cause mobile devices to provide their permanent identifiers. That is, our approach focuses on causal attributes rather than correlated ones. We systematically analyze message flows that would lead to IMSI exposure (most of which have not been previously considered in the research community), and identify 53 messages an IMSI-Catcher can use for its attack. We then perform a measurement study on two continents to characterize the ratio in which connections use these messages in normal operations.

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ABOUT NDSS
The Network and Distributed System Security Symposium (NDSS) fosters information exchange among researchers and practitioners of network and distributed system security. The target audience includes those interested in practical aspects of network and distributed system security, with a focus on actual system design and implementation. A major goal is to encourage and enable the Internet community to apply, deploy, and advance the state of available security technologies.

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Our thanks to the **[Network and Distributed System Security (NDSS) Symposium][1]** for publishing their Creators, Authors and Presenter’s superb **[NDSS Symposium 2025 Conference][2]** content on the **[organization’s’][1]** **[YouTube][3]** channel.

Permalink

The post NDSS 2025 – Detecting IMSI-Catchers By Characterizing Identity Exposing Messages In Cellular Traffic appeared first on Security Boulevard.

SESSION
Session 3B: Wireless, Cellular & Satellite Security

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Authors, Creators & Presenters: Yangtao Deng (Tsinghua University), Qian Wu (Tsinghua University), Zeqi Lai (Tsinghua University), Chenwei Gu (Tsinghua University), Hewu Li (Tsinghua University), Yuanjie Li (Tsinghua University), Jun Liu (Tsinghua University)

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PAPER

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Time-varying Bottleneck Links in LEO Satellite Networks: Identification, Exploits, and Countermeasures
In this paper, we perform a multifaceted study on the security risk involved by the unique time-varying bottleneck links in emerging Low-Earth Orbit (LEO) satellite networks (LSNs). We carry out our study in three steps. First, we profile the spatial and temporal characteristics of bottleneck links and how they might be exploited for bottleneck identification. Thus, the bottleneck links imposes a new risk of link flooding attack (LFA) on LSNs. Second, we propose SKYFALL, a new LFA risk analyzer that enables satellite network operators to simulate various LFA behaviors and comprehensively analyze the consequences on LSN services. Concretely, SKYFALL's analysis based on real-world information of operational LSNs demonstrates that the throughput of legal background traffic could be reduced by a factor of 3.4 if an attacker can manipulate a number of compromised user terminals to continuously congest the bottleneck links. Based on our analysis, we finally discuss the limitations of traditional LFA countermeasures and propose new mitigation strategies for LSNs.

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ABOUT NDSS
The Network and Distributed System Security Symposium (NDSS) fosters information exchange among researchers and practitioners of network and distributed system security. The target audience includes those interested in practical aspects of network and distributed system security, with a focus on actual system design and implementation. A major goal is to encourage and enable the Internet community to apply, deploy, and advance the state of available security technologies.

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Our thanks to the **[Network and Distributed System Security (NDSS) Symposium][1]** for publishing their Creators, Authors and Presenter’s superb **[NDSS Symposium 2025 Conference][2]** content on the **[organization’s’][1]** **[YouTube][3]** channel.

Permalink

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Google is suing the Smishing Triad group behind the Lighthouse phishing-as-a-service kit that has been used over the past two years to scam more than 1 million people around the world with fraudulent package delivery or EZ-Pass toll fee messages and stealing millions of credit card numbers. Google also is backing bills in Congress to address the threat.

The post Google Uses Courts, Congress to Counter Massive Smishing Campaign appeared first on Security Boulevard.

SESSION
Session 3B: Wireless, Cellular & Satellite Security

Authors, Creators & Presenters: Jung-Woo Chang (University of California, San Diego), Ke Sun (University of California, San Diego), Nasimeh Heydaribeni (University of California, San Diego), Seira Hidano (KDDI Research, Inc.), Xinyu Zhang (University of California, San Diego), Farinaz Koushanfar (University of California, San Diego)

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PAPER
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Magmaw: Modality-Agnostic Adversarial Attacks on Machine Learning-Based Wireless Communication Systems

Machine Learning (ML) has been instrumental in enabling joint transceiver optimization by merging all physical layer blocks of the end-to-end wireless communication systems. Although there have been a number of adversarial attacks on ML-based wireless systems, the existing methods do not provide a comprehensive view including multi-modality of the source data, common physical layer protocols, and wireless domain constraints. This paper proposes Magmaw, a novel wireless attack methodology capable of generating universal adversarial perturbations for any multimodal signal transmitted over a wireless channel. We further introduce new objectives for adversarial attacks on downstream applications. We adopt the widely used defenses to verify the resilience of Magmaw. For proof-of-concept evaluation, we build a real-time wireless attack platform using a software-defined radio system. Experimental results demonstrate that Magmaw causes significant performance degradation even in the presence of strong defense mechanisms. Furthermore, we validate the performance of Magmaw in two case studies: encrypted communication channel and channel modality-based ML model. Our code is available at [https://github.com/juc023/Magmaw].

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ABOUT NDSS
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The Network and Distributed System Security Symposium (NDSS) fosters information exchange among researchers and practitioners of network and distributed system security. The target audience includes those interested in practical aspects of network and distributed system security, with a focus on actual system design and implementation. A major goal is to encourage and enable the Internet community to apply, deploy, and advance the state of available security technologies.

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Our thanks to the **[Network and Distributed System Security (NDSS) Symposium][1]** for publishing their Creators, Authors and Presenter’s superb **[NDSS Symposium 2025 Conference][2]** content on the **[organization’s’][1]** **[YouTube][3]** channel.

Permalink

The post NDSS 2025 – Magmaw: Modality-Agnostic Adversarial Attacks appeared first on Security Boulevard.

SESSION
Session 3A: Network Security 1

Authors, Creators & Presenters: Shaoke Xi (Zhejiang University), Tianyi Fu (Zhejiang University), Kai Bu (Zhejiang University), Chunling Yang (Zhejiang University), Zhihua Chang (Zhejiang University), Wenzhi Chen (Zhejiang University), Zhou Ma (Zhejiang University), Chongjie Chen (HANG ZHOU CITY BRAIN CO., LTD), Yongsheng Shen (HANG ZHOU CITY BRAIN CO., LTD), Kui Ren (Zhejiang University)

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PAPER
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MineShark: Cryptomining Traffic Detection at Scale
The rapid growth of cryptojacking and the increase in regulatory bans on cryptomining have prompted organizations to enhance detection ability within their networks. Traditional methods, including rule-based detection and deep packet inspection, fall short in timely and comprehensively identifying new and encrypted mining threats. In contrast, learning-based techniques show promise by identifying content-agnostic traffic patterns, adapting to a wide range of cryptomining configurations. However, existing learning-based systems often lack scalability in real-world detection, primarily due to challenges with unlabeled, imbalanced, and high-speed traffic inputs. To address these issues, we introduce MineShark, a system that identifies robust patterns of mining traffic to distinguish between vast quantities of benign traffic and automates the confirmation of model outcomes through active probing to prevent an overload of model alarms. As model inference labels are progressively confirmed, MineShark conducts self-improving updates to enhance model accuracy. MineShark is capable of line-rate detection at various traffic volume scales with the allocation of different amounts of CPU and GPU resources. In a 10 Gbps campus network deployment lasting ten months, MineShark detected cryptomining connections toward 105 mining pools ahead of concurrently deployed commercial systems, 17.6% of which were encrypted. It automatically filtered over 99.3% of false alarms and achieved an average packet processing throughput of 1.3 Mpps, meeting the line-rate demands of a 10 Gbps network, with a negligible loss rate of 0.2%. We publicize MineShark for broader use.

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ABOUT NDSS
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The Network and Distributed System Security Symposium (NDSS) fosters information exchange among researchers and practitioners of network and distributed system security. The target audience includes those interested in practical aspects of network and distributed system security, with a focus on actual system design and implementation. A major goal is to encourage and enable the Internet community to apply, deploy, and advance the state of available security technologies.

-----

Our thanks to the **[Network and Distributed System Security (NDSS) Symposium][1]** for publishing their Creators, Authors and Presenter’s superb **[NDSS Symposium 2025 Conference][2]** content on the **[organization’s’][1]** **[YouTube][3]** channel.

Permalink

The post NDSS 2025 – MineShark: Cryptomining Traffic Detection At Scale appeared first on Security Boulevard.

The intrusion a year ago into Conduent Business Solutions' systems, likely by the SafePay ransomware group, that affected more than 10.5 individuals will likely cost the company more than $50 million in related expenses and millions more to settle the lawsuits that are piling up.

The post Conduent Faces Financial Hit, Lawsuits from Breach Affecting 10.5 Million appeared first on Security Boulevard.