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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
Spatial Variation-Aware Read Disturbance Defenses: Experi...
Abdullah Giray Yağlıkçı, Yahya Can Tuğrul, Geraldo F. Oliveira, · 2024-02-29 · via cs.CR updates on arXiv.org

Read disturbance in modern DRAM chips is a widespread phenomenon and is reliably used for breaking memory isolation, a fundamental building block for building robust systems. RowHammer and RowPress are two examples of read disturbance in DRAM where repeatedly accessing (hammering) or keeping active (pressing) a memory location induces bitflips in other memory locations. Unfortunately, shrinking technology node size exacerbates read disturbance in DRAM chips over generations. As a result, existing defense mechanisms suffer from significant performance and energy overheads, limited effectiveness, or prohibitively high hardware complexity. In this paper, we tackle these shortcomings by leveraging the spatial variation in read disturbance across different memory locations in real DRAM chips. To do so, we 1) present the first rigorous real DRAM chip characterization study of spatial variation of read disturbance and 2) propose Svärd, a new mechanism that dynamically adapts the aggressiveness of existing solutions based on the row-level read disturbance profile. Our experimental characterization on 144 real DDR4 DRAM chips representing 10 chip designs demonstrates a large variation in read disturbance vulnerability across different memory locations: in the part of memory with the worst read disturbance vulnerability, 1) up to 2x the number of bitflips can occur and 2) bitflips can occur at an order of magnitude fewer accesses, compared to the memory locations with the least vulnerability to read disturbance. Svärd leverages this variation to reduce the overheads of five state-of-the-art read disturbance solutions, and thus significantly increases system performance.