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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
Obfuscating the Interconnects: Low-Cost and Resilient Ful...
Satwik Patnaik, Mohammed Ashraf, Ozgur Sinanoglu, Johann Knechte · 2020-03-21 · via cs.CR updates on arXiv.org

Layout camouflaging can protect the intellectual property of modern circuits. Most prior art, however, incurs excessive layout overheads and necessitates customization of active-device manufacturing processes, i.e., the front-end-of-line (FEOL). As a result, camouflaging has typically been applied selectively, which can ultimately undermine its resilience. Here, we propose a low-cost and generic scheme---full-chip camouflaging can be finally realized without reservations. Our scheme is based on obfuscating the interconnects, i.e., the back-end-of-line (BEOL), through design-time handling for real and dummy wires and vias. To that end, we implement custom, BEOL-centric obfuscation cells, and develop a CAD flow using industrial tools. Our scheme can be applied to any design and technology node without FEOL-level modifications. Considering its BEOL-centric nature, we advocate applying our scheme in conjunction with split manufacturing, to furthermore protect against untrusted fabs. We evaluate our scheme for various designs at the physical, DRC-clean layout level. Our scheme incurs a significantly lower cost than most of the prior art. Notably, for fully camouflaged layouts, we observe average power, performance, and area overheads of 24.96%, 19.06%, and 32.55%, respectively. We conduct a thorough security study addressing the threats (attacks) related to untrustworthy FEOL fabs (proximity attacks) and malicious end-users (SAT-based attacks). An empirical key finding is that only large-scale camouflaging schemes like ours are practically secure against powerful SAT-based attacks. Another key finding is that our scheme hinders both placement- and routing-centric proximity attacks; correct connections are reduced by 7.47X, and complexity is increased by 24.15X, respectively, for such attacks.