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

让小产品的独立变现更简单 - ezindie.com
让小产品的独立变现更简单 - ezindie.com
B
Blog
Jina AI
Jina AI
N
Netflix TechBlog - Medium
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
博客园_首页
Hugging Face - Blog
Hugging Face - Blog
博客园 - 聂微东
美团技术团队
Google DeepMind News
Google DeepMind News
WordPress大学
WordPress大学
阮一峰的网络日志
阮一峰的网络日志
U
Unit 42
The Cloudflare Blog
V
V2EX
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
小众软件
小众软件
罗磊的独立博客
Microsoft Security Blog
Microsoft Security Blog
Apple Machine Learning Research
Apple Machine Learning Research
I
InfoQ
GbyAI
GbyAI
腾讯CDC
MongoDB | Blog
MongoDB | Blog

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
EPA-RIMM: A Framework for Dynamic SMM-based Runtime Integ...
Brian Delgado, Karen L. Karavanic · 2018-05-10 · via cs.CR updates on arXiv.org

Runtime integrity measurements identify unexpected changes in operating systems and hypervisors during operation, enabling early detection of persistent threats. System Management Mode, a privileged x86 CPU mode, has the potential to effectively perform such rootkit detection. Previously proposed SMM-based approaches demonstrated effective detection capabilities, but at a cost of performance degradation and software side effects. In this paper we introduce our solution to these problems, an SMM-based Extensible, Performance Aware Runtime Integrity Measurement Mechanism called EPA-RIMM. The EPA-RIMM architecture features a performance-sensitive design that decomposes large integrity measurements and schedules them to control perturbation and side effects. EPA-RIMM's decomposition of long-running measurements into shorter tasks, extensibility, and use of SMM complicates the efforts of malicious code to detect or avoid the integrity measurements. Using a Minnowboard-based prototype, we demonstrate its detection capabilities and performance impacts. Early results are promising, and suggest that EPA-RIMM will meet production-level performance constraints while continuously monitoring key OS and hypervisor data structures for signs of attack.