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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
X-Volt: Joint Tuning of Driver Strengths and Supply Volta...
Saideep Sreekumar, Mohammed Ashraf, Mohammed Nabeel, Ozgur Sinan · 2022-11-15 · via cs.CR updates on arXiv.org

Power side-channel (PSC) attacks are well-known threats to sensitive hardware like advanced encryption standard (AES) crypto cores. Given the significant impact of supply voltages (VCCs) on power profiles, various countermeasures based on VCC tuning have been proposed, among other defense strategies. Driver strengths of cells, however, have been largely overlooked, despite having direct and significant impact on power profiles as well. For the first time, we thoroughly explore the prospects of jointly tuning driver strengths and VCCs as novel working principle for PSC-attack countermeasures. Toward this end, we take the following steps: 1) we develop a simple circuit-level scheme for tuning; 2) we implement a CAD flow for design-time evaluation of ASICs, enabling security assessment of ICs before tape-out; 3) we implement a correlation power analysis (CPA) framework for thorough and comparative security analysis; 4) we conduct an extensive experimental study of a regular AES design, implemented in ASIC as well as FPGA fabrics, under various tuning scenarios; 5) we summarize design guidelines for secure and efficient joint tuning. In our experiments, we observe that runtime tuning is more effective than static tuning, for both ASIC and FPGA implementations. For the latter, the AES core is rendered >11.8x (i.e., at least 11.8 times) as resilient as the untuned baseline design. Layout overheads can be considered acceptable, with, e.g., around +10% critical-path delay for the most resilient tuning scenario in FPGA. We will release source codes for our methodology, as well as artifacts from the experimental study, post peer-review.