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

T
The Blog of Author Tim Ferriss
Hugging Face - Blog
Hugging Face - Blog
F
Fortinet All Blogs
B
Blog
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
Microsoft Security Blog
Microsoft Security Blog
Blog — PlanetScale
Blog — PlanetScale
月光博客
月光博客
腾讯CDC
小众软件
小众软件
G
Google Developers Blog
V
Visual Studio Blog
罗磊的独立博客
GbyAI
GbyAI
V
V2EX
大猫的无限游戏
大猫的无限游戏
H
Help Net Security
L
LangChain Blog
Engineering at Meta
Engineering at Meta
量子位
The GitHub Blog
The GitHub Blog
博客园 - 司徒正美
WordPress大学
WordPress大学
B
Blog RSS Feed

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
Path-wise Vulnerability Mitigation
Zhen Huang, Hristina Dokic · 2024-05-26 · via cs.CR updates on arXiv.org

Software vulnerabilities are prevalent but fixing software vulnerabilities is not trivial. Studies have shown that a considerable prepatch window exists because it often takes weeks or months for software vendors to fix a vulnerability. Existing approaches aim to reduce the pre-patch window by generating and applying mitigation patches that prevent adversaries from exploiting vulnerabilities rather than fix vulnerabilities. Because mitigation patches typically terminate the execution of vulnerability-triggering program paths at the level of functions, they can have significant side-effects. This paper describes an approach called PAVER that generates and inserts mitigation patches at the level of program paths, i.e. path-wise vulnerability mitigation patches, in order to reduce their side-effects. PAVER generates a program path graph that includes the paths leading to vulnerabilities and the control dependencies on these paths, then identifies candidate patch locations based on the program path graph. For each candidate patch location, PAVER generates and inserts a mitigation patch, and tests the patched program to assess the side-effect of the patch. It ranks the patches by the extent of their side-effects. We evaluates the prototype of PAVER on real world vulnerabilities and the evaluation shows that our path-wise vulnerability mitigation patches can achieve minimum side-effects.