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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
New Attacks and Defenses for Randomized Caches
Kartik Ramkrishnan, Antonia Zhai, Stephen McCamant, Pen Chung Ye · 2019-09-26 · via cs.CR updates on arXiv.org

The last level cache is vulnerable to timing based side channel attacks because it is shared by the attacker and the victim processes even if they are located on different cores. These timing attacks evict the victim cache lines using small conflict groups(SCG), and monitor the cache to observe when the victim uses these cache lines again. A conflict group is a collection of cache lines which will evict the target cache line. Randomization is often used by defenses to prevent creation of SCGs. We introduce new attacks to demonstrate that the current randomization schemes require an extremely high refresh rate to be secure, on average a 15\% performance overhead, and upto 50\% in the worst case. Next, we propose a new randomization strategy using an indirection table, which mitigates this issue. Addresses of cache lines are encrypted and used to lookup the indirection table entry. Each indirection table entry stores a mapping to a randomly chosen cache set. The cache line is placed into this randomly chosen set. The encryption key changes upto 50x faster than CEASER's default rate, by using evictions to trigger the re-randomization. Instead of moving cache lines, this mechanism re-randomizes one iTable entry at a time, whenever the cache lines corresponding to the iTable entry are naturally evicted. Thus, the miss rate is not much worse than the baseline. We quantitatively show that our scheme does almost as well as a fully associative cache to defend against these attacks. We also demonstrate new attacks that target the iTable by oversubscribing its entries, and quantitatively show that our scheme is resilient against new attacks for trillions of years. We estimate low area ( < 7\%) and power overhead compared to a baseline inclusive last-level cache. Lastly, we evaluate a low performance overhead (<4%) using the SPECrate 2017 and PARSEC 3.0 benchmarks.