惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

L
LangChain Blog
博客园_首页
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
月光博客
月光博客
S
SegmentFault 最新的问题
量子位
Apple Machine Learning Research
Apple Machine Learning Research
博客园 - 司徒正美
博客园 - Franky
Google DeepMind News
Google DeepMind News
Recent Announcements
Recent Announcements
B
Blog RSS Feed
C
Check Point Blog
The Cloudflare Blog
M
MIT News - Artificial intelligence
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
F
Fortinet All Blogs
Hugging Face - Blog
Hugging Face - Blog
博客园 - 叶小钗
V
Visual Studio Blog
V
V2EX
Microsoft Azure Blog
Microsoft Azure Blog
博客园 - 聂微东
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More

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
RELOAD+REFRESH: Abusing Cache Replacement Policies to Per...
Samira Briongos, Pedro Malagón, José M. Moya, Thomas Eisenbarth · 2019-04-12 · via cs.CR updates on arXiv.org

Caches have become the prime method for unintended information extraction across logical isolation boundaries. Even Spectre and Meltdown rely on the cache side channel, as it provides great resolution and is widely available on all major CPU platforms. As a consequence, several methods to stop cache attacks by detecting them have been proposed. Detection is strongly aided by the fact that observing cache activity of co-resident processes is not possible without altering the cache state and thereby forcing evictions on the observed processes. In this work, we show that this widely held assumption is incorrect. Through clever usage of the cache replacement policy it is possible to track a victims process cache accesses without forcing evictions on the victim's data. Hence, online detection mechanisms that rely on these evictions can be circumvented as they do not detect be the introduced RELOAD+REFRESH attack. The attack requires a profound understanding of the cache replacement policy. We present a methodology to recover the replacement policy and apply it to the last five generations of Intel processors. We further show empirically that the performance of RELOAD+REFRESH on cryptographic implementations is comparable to that of other widely used cache attacks, while its detectability becomes extremely difficult, due to the negligible effect on the victims cache access pattern.