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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
ImPress: Securing DRAM Against Data-Disturbance Errors vi...
Moinuddin Qureshi, Anish Saxena, Aamer Jaleel · 2024-07-23 · via cs.CR updates on arXiv.org

DRAM cells are susceptible to Data-Disturbance Errors (DDE), which can be exploited by an attacker to compromise system security. Rowhammer is a well-known DDE vulnerability that occurs when a row is repeatedly activated. Rowhammer can be mitigated by tracking aggressor rows inside DRAM (in-DRAM) or at the Memory Controller (MC). Row-Press (RP) is a new DDE vulnerability that occurs when a row is kept open for a long time. RP significantly reduces the number of activations required to induce an error, thus breaking existing RH solutions. Prior work on Explicit Row-Press mitigation, ExPress, requires the memory controller to limit the maximum row-open-time, and redesign existing Rowhammer solutions with reduced Rowhammer threshold. Unfortunately, ExPress incurs significant performance and storage overheads, and being a memory controller-based solution, it is incompatible with in-DRAM trackers. In this paper, we propose Implicit Row-Press mitigation (ImPress), which does not restrict row-open-time, is compatible with memory controller-based and in-DRAM solutions and does not reduce the tolerated Rowhammer threshold. ImPress treats a row open for a specified time as equivalent to an activation. We design ImPress by developing a Unified Charge-Loss Model, which combines the net effect of both Rowhammer and Row-Press for arbitrary patterns. We analyze both controller-based (Graphene and PARA) and in-DRAM trackers (Mithril and MINT). We show that ImPress makes Rowhammer solutions resilient to Row-Press transparently, without affecting the Rowhammer threshold.