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

J
Java Code Geeks
S
SegmentFault 最新的问题
V
Visual Studio Blog
人人都是产品经理
人人都是产品经理
阮一峰的网络日志
阮一峰的网络日志
腾讯CDC
Stack Overflow Blog
Stack Overflow Blog
博客园 - 【当耐特】
Recent Announcements
Recent Announcements
I
InfoQ
U
Unit 42
博客园_首页
GbyAI
GbyAI
Hugging Face - Blog
Hugging Face - Blog
罗磊的独立博客
博客园 - 叶小钗
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
D
DataBreaches.Net
aimingoo的专栏
aimingoo的专栏
月光博客
月光博客
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
博客园 - 聂微东
T
Tailwind CSS Blog
量子位

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
A Large-scale Study of Security Vulnerability Support on ...
Triet H. M. Le, Roland Croft, David Hin, M. Ali Babar · 2020-08-10 · via cs.CR updates on arXiv.org

Context: Security Vulnerabilities (SVs) pose many serious threats to software systems. Developers usually seek solutions to addressing these SVs on developer Question and Answer (Q&A) websites. However, there is still little known about on-going SV-specific discussions on different developer Q&A sites. Objective: We present a large-scale empirical study to understand developers' SV discussions and how these discussions are being supported by Q&A sites. Method: We first curate 71,329 SV posts from two large Q&A sites, namely Stack Overflow (SO) and Security StackExchange (SSE). We then use topic modeling to uncover the topics of SV-related discussions and analyze the popularity, difficulty, and level of expertise for each topic. We also perform a qualitative analysis to identify the types of solutions to SV-related questions. Results: We identify 13 main SV discussion topics on Q&A sites. Many topics do not follow the distributions and trends in expert-based security sources such as Common Weakness Enumeration (CWE) and Open Web Application Security Project (OWASP). We also discover that SV discussions attract more experts to answer than many other domains, but some difficult SV topics (e.g., Vulnerability Scanning Tools) still receive quite limited support from experts. Moreover, we identify seven key types of answers given to SV questions on Q&A sites, in which SO often provides code and instructions, while SSE usually gives experience-based advice and explanations. Conclusion: Our findings provide support for researchers and practitioners to effectively acquire, share and leverage SV knowledge on Q&A sites.