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Agentic Vulnerability Reasoning on Windows COM Binaries From Beats to Breaches:How Offensive AI Infers Sensitive User Information from Playlists Undetectable Backdoors in Model Parameters: Hiding Sparse Secrets in High Dimensions When Embedding-Based Defenses Fail: Rethinking Safety in LLM-Based Multi-Agent Systems Token-Efficient Change Detection in LLM APIs Selfie-Capture Dynamics as an Auxiliary Signal Against Deepfakes and Injection Attacks for Mobile Identity Verification Trident: Improving Malware Detection with LLMs and Behavioral Features When Alignment Isn't Enough: Response-Path Attacks on LLM Agents RefusalGuard: Geometry-Preserving Fine-Tuning for Safety in LLMs Checkerboard: A Simple, Effective, Efficient and Learning-free Clean Label Backdoor Attack with Low Poisoning Budget Block-wise Codeword Embedding for Reliable Multi-bit Text Watermarking Secret Stealing Attacks on Local LLM Fine-Tuning through Supply-Chain Model Code Backdoors Enhancing Linux Privilege Escalation Attack Capabilities of Local LLM Agents Defusing the Trigger: Plug-and-Play Defense for Backdoored LLMs via Tail-Risk Intrinsic Geometric Smoothing Evaluating Jailbreaking Vulnerabilities in LLMs Deployed as Assistants for Smart Grid Operations: A Benchmark Against NERC Standards Behavioral Canaries: Auditing Private Retrieved Context Usage in RL Fine-Tuning FlexServe: A Fast and Secure LLM Serving System for Mobile Devices with Flexible Resource Isolation Breaking MCP with Function Hijacking Attacks: Novel Threats for Function Calling and Agentic Models Text Steganography with Dynamic Codebook and Multimodal Large Language Model An AI Agent Execution Environment to Safeguard User Data TwoHamsters: Benchmarking Multi-Concept Compositional Unsafety in Text-to-Image Models Fundamental Limitations of Favorable Privacy-Utility Guarantees for DP-SGD Symbolic Guardrails for Domain-Specific Agents: Stronger Safety and Security Guarantees Without Sacrificing Utility Hardening x402: PII-Safe Agentic Payments via Pre-Execution Metadata Filtering QShield: Securing Neural Networks Against Adversarial Attacks using Quantum Circuits Hijacking Text Heritage: Hiding the Human Signature through Homoglyphic Substitution Like a Hammer, It Can Build, It Can Break: Large Language Model Uses, Perceptions, and Adoption in Cybersecurity Operations on Reddit Private Seeds, Public LLMs: Realistic and Privacy-Preserving Synthetic Data Generation One Word at a Time: Incremental Completion Decomposition Breaks LLM Safety Measuring and Exploiting Contextual Bias in LLM-Assisted Security Code Review
YASM (Yet Another Surveillance Mechanism)
Kaspar Rosager Ludvigsen, Shishir Nagaraja, Angela Daly · 2022-05-29 · via cs.CR updates on arXiv.org

Client-Side Scanning (CSS) see in the Child Sexual Abuse Material Detection (CSAMD) represent ubiquitous mass scanning. Apple proposed to scan their systems for such imagery. CSAMD was since pushed back, but the European Union decided to propose forced CSS to combat and prevent child sexual abuse and weaken encryption. CSS is mass surveillance of personal property, pictures and text, without considerations of privacy and cybersecurity and the law. We first argue why CSS should be limited or not used and discuss issues with the way pictures cryptographically are handled and how the CSAMD preserves privacy. In the second part, we analyse the possible human rights violations which CSS in general can cause within the regime of the European Convention on Human Rights. The focus is the harm which the system may cause to individuals, and we also comment on the proposed Child Abuse Regulation. We find that CSS is problematic because they can rarely fulfil their purposes, as seen with antivirus software. The costs for attempting to solve issues such as CSAM outweigh the benefits and is not likely to change. The CSAMD as proposed is not likely to preserve the privacy or security in the way of which it is described source materials. We also find that CSS in general would likely violate the Right to a Fair Trial, Right to Privacy and Freedom of Expression. Pictures could have been obtained in a way that could make any trial against a legitimate perpetrator inadmissible or violate their right for a fair trial, the lack of any safeguards to protect privacy on national legal level, which would violate the Right for Privacy, and it is unclear if the kind of scanning could pass the legal test which Freedom of Expression requires. Finally, we find significant issues with the proposed Regulation, as it relies on techno-solutionist arguments and disregards knowledge on cybersecurity.