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cs.CR updates on arXiv.org

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
The Dangers of Computational Law and Cybersecurity; Persp...
Kaspar Rosager Ludvigsen, Shishir Nagaraja, Angela Daly · 2022-07-01 · via cs.CR updates on arXiv.org

Computational Law has begun taking the role in society which has been predicted for some time. Automated decision-making and systems which assist users are now used in various jurisdictions, but with this maturity come certain caveats. Computational Law exists on the platforms which enable it, in this case digital systems, which means that it inherits the same flaws. Cybersecurity addresses these potential weaknesses. In this paper we go through known issues and discuss them in the various levels, from design to the physical realm. We also look at machine-learning specific adversarial problems. Additionally, we make certain considerations regarding computational law and existing and future legislation. Finally, we present three recommendations which are necessary for computational law to function globally, and which follow ideas in safety and security engineering. As indicated, we find that computational law must seriously consider that not only does it face the same risks as other types of software and computer systems, but that failures within it may cause financial or physical damage, as well as injustice. Consequences of Computational Legal systems failing are greater than if they were merely software and hardware. If the system employs machine-learning, it must take note of the very specific dangers which this brings, of which data poisoning is the classic example. Computational law must also be explicitly legislated for, which we show is not the case currently in the EU, and this is also true for the cybersecurity aspects that will be relevant to it. But there is great hope in EU's proposed AI Act, which makes an important attempt at taking the specific problems which Computational Law bring into the legal sphere. Our recommendations for Computational Law and Cybersecurity are: Accommodation of threats, adequate use, and that humans remain in the centre of their deployment.