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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
TRRespass: Exploiting the Many Sides of Target Row Refresh
2020-04-04 · via cs.CR updates on arXiv.org

After a plethora of high-profile RowHammer attacks, CPU and DRAM vendors scrambled to deliver what was meant to be the definitive hardware solution against the RowHammer problem: Target Row Refresh (TRR). A common belief among practitioners is that, for the latest generation of DDR4 systems that are protected by TRR, RowHammer is no longer an issue in practice. However, in reality, very little is known about TRR. In this paper, we demystify the inner workings of TRR and debunk its security guarantees. We show that what is advertised as a single mitigation mechanism is actually a series of different solutions coalesced under the umbrella term TRR. We inspect and disclose, via a deep analysis, different existing TRR solutions and demonstrate that modern implementations operate entirely inside DRAM chips. Despite the difficulties of analyzing in-DRAM mitigations, we describe novel techniques for gaining insights into the operation of these mitigation mechanisms. These insights allow us to build TRRespass, a scalable black-box RowHammer fuzzer. TRRespass shows that even the latest generation DDR4 chips with in-DRAM TRR, immune to all known RowHammer attacks, are often still vulnerable to new TRR-aware variants of RowHammer that we develop. In particular, TRRespass finds that, on modern DDR4 modules, RowHammer is still possible when many aggressor rows are used (as many as 19 in some cases), with a method we generally refer to as Many-sided RowHammer. Overall, our analysis shows that 13 out of the 42 modules from all three major DRAM vendors are vulnerable to our TRR-aware RowHammer access patterns, and thus one can still mount existing state-of-the-art RowHammer attacks. In addition to DDR4, we also experiment with LPDDR4 chips and show that they are susceptible to RowHammer bit flips too. Our results provide concrete evidence that the pursuit of better RowHammer mitigations must continue.