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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
Lookout for Zombies: Mitigating Flush+Reload Attack on Sh...
Gururaj Saileshwar, Moinuddin K. Qureshi · 2019-06-06 · via cs.CR updates on arXiv.org

OS-based page sharing is a commonly used optimization in modern systems to reduce memory footprint. Unfortunately, such sharing can cause Flush+Reload cache attacks, whereby a spy periodically flushes a cache line of shared data (using the clflush instruction) and reloads it to infer the access patterns of a victim application. Current proposals to mitigate Flush+Reload attacks are impractical as they either disable page sharing, or require application rewrite, or require OS support, or incur ISA changes. Ideally, we want to tolerate attacks without requiring any OS or ISA support and while incurring negligible performance and storage overheads. This paper makes the key observation that when a cache line is invalidated due to a Flush-Caused Invalidation (FCI), the tag and data of the invalidated line are still resident in the cache and can be used for detecting Flush-based attacks. We call lines invalidated due to FCI as Zombie lines. Our design explicitly marks such lines as Zombies, preserves the Zombie lines in the cache, and uses the hits and misses to Zombie lines to tolerate the attacks. We propose Zombie-Based Mitigation (ZBM), a simple hardware-based design that successfully guards against attacks by simply treating hits on Zombie-lines as misses to avoid any timing leaks to the attacker. We analyze the robustness of ZBM using three spy programs: attacking AES T-Tables, attacking RSA Square-and-Multiply, and Function Watcher (FW), and show that ZBM successfully defends against these attacks. Our solution requires negligible storage (4-bits per cache line), retains OS-based page sharing, requires no OS/ISA changes, and does not incur slowdown for benign applications.