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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
Randomized Line-to-Row Mapping for Low-Overhead Rowhammer...
Anish Saxena, Saurav Mathur, Moinuddin Qureshi · 2023-08-29 · via cs.CR updates on arXiv.org

Modern systems mitigate Rowhammer using victim refresh, which refreshes the two neighbours of an aggressor row when it encounters a specified number of activations. Unfortunately, complex attack patterns like Half-Double break victim-refresh, rendering current systems vulnerable. Instead, recently proposed secure Rowhammer mitigations rely on performing mitigative action on the aggressor rather than the victims. Such schemes employ mitigative actions such as row-migration or access-control and include AQUA, SRS, and Blockhammer. While these schemes incur only modest slowdowns at Rowhammer thresholds of few thousand, they incur prohibitive slowdowns (15%-600%) for lower thresholds that are likely in the near future. The goal of our paper is to make secure Rowhammer mitigations practical at such low thresholds. Our paper provides the key insights that benign application encounter thousands of hot rows (receiving more activations than the threshold) due to the memory mapping, which places spatially proximate lines in the same row to maximize row-buffer hitrate. Unfortunately, this causes row to receive activations for many frequently used lines. We propose Rubix, which breaks the spatial correlation in the line-to-row mapping by using an encrypted address to access the memory, reducing the likelihood of hot rows by 2 to 3 orders of magnitude. To aid row-buffer hits, Rubix randomizes a group of 1-4 lines. We also propose Rubix-D, which dynamically changes the line-to-row mapping. Rubix-D minimizes hot-rows and makes it much harder for an adversary to learn the spatial neighbourhood of a row. Rubix reduces the slowdown of AQUA (from 15% to 1%), SRS (from 60% to 2%), and Blockhammer (from 600% to 3%) while incurring a storage of less than 1 Kilobyte.