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

H
Help Net Security
爱范儿
爱范儿
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
博客园 - 三生石上(FineUI控件)
大猫的无限游戏
大猫的无限游戏
Hugging Face - Blog
Hugging Face - Blog
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
Vercel News
Vercel News
人人都是产品经理
人人都是产品经理
G
Google Developers Blog
WordPress大学
WordPress大学
S
SegmentFault 最新的问题
雷峰网
雷峰网
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
Jina AI
Jina AI
博客园 - 叶小钗
D
DataBreaches.Net
D
Docker
月光博客
月光博客
博客园 - 司徒正美
Last Week in AI
Last Week in AI
有赞技术团队
有赞技术团队
腾讯CDC
酷 壳 – CoolShell
酷 壳 – CoolShell

cs.SE updates on arXiv.org

VLA Foundry: A Unified Framework for Training Vision-Language-Action Models Evaluating LLM-Generated Obfuscated XSS Payloads for Machine Learning-Based Detection Do Agents Dream of Root Shells? Partial-Credit Evaluation of LLM Agents in Capture the Flag Challenges Refute-or-Promote: An Adversarial Stage-Gated Multi-Agent Review Methodology for High-Precision LLM-Assisted Defect Discovery From Particles to Perils: SVGD-Based Hazardous Scenario Generation for Autonomous Driving Systems Testing Choose Your Own Adventure: Non-Linear AI-Assisted Programming with EvoGraph Human-Machine Co-Boosted Bug Report Identification with Mutualistic Neural Active Learning LLMSniffer: Detecting LLM-Generated Code via GraphCodeBERT and Supervised Contrastive Learning Neurosymbolic Repo-level Code Localization CodeMMR: Bridging Natural Language, Code, and Image for Unified Retrieval Symbolic Guardrails for Domain-Specific Agents: Stronger Safety and Security Guarantees Without Sacrificing Utility Verification Modulo Tested Library Contracts The Semi-Executable Stack: Agentic Software Engineering and the Expanding Scope of SE Scaling Test-Time Compute for Agentic Coding AI-Assisted Requirements Engineering: An Empirical Evaluation Relative to Expert Judgment From Procedural Skills to Strategy Genes: Towards Experience-Driven Test-Time Evolution Atropos: Improving Cost-Benefit Trade-off of LLM-based Agents under Self-Consistency with Early Termination and Model Hotswap Vibe-Coding: Feedback-Based Automated Verification with no Human Code Inspection, a Feasibility Study Benchmarks for Trajectory Safety Evaluation and Diagnosis in OpenClaw and Codex: ATBench-Claw and ATBench-Codex Bounded Autonomy for Enterprise AI: Typed Action Contracts and Consumer-Side Execution AIPC: Agent-Based Automation for AI Model Deployment with Qualcomm AI Runtime Analyzing Chain of Thought (CoT) Approaches in Control Flow Code Deobfuscation Tasks Asking What Matters: Reward-Driven Clarification for Software Engineering Tasks Prompt-Driven Code Summarization: A Systematic Literature Review LinuxArena: A Control Setting for AI Agents in Live Production Software Environments LLMs taking shortcuts in test generation: A study with SAP HANA and LevelDB Large Language Models to Enhance Business Process Modeling: Past, Present, and Future Trends CollabCoder: Plan-Code Co-Evolution via Collaborative Decision-Making for Efficient Code Generation Sentiment analysis for software engineering: How far can zero-shot learning (ZSL) go? Learning from Change: Predictive Models for Incident Prevention in a Regulated IT Environment
A Grounded Theory Based Approach to Characterize Software...
Sara Moshtari, Ahmet Okutan, Mehdi Mirakhorli · 2021-12-03 · via cs.SE updates on arXiv.org

The notion of Attack Surface refers to the critical points on the boundary of a software system which are accessible from outside or contain valuable content for attackers. The ability to identify attack surface components of software system has a significant role in effectiveness of vulnerability analysis approaches. Most prior works focus on vulnerability techniques that use an approximation of attack surfaces and there has not been many attempt to create a comprehensive list of attack surface components. Although limited number of studies have focused on attack surface analysis, they defined attack surface components based on project specific hypotheses to evaluate security risk of specific types of software applications. In this study, we leverage a qualitative analysis approach to empirically identify an extensive list of attack surface components. To this end, we conduct a Grounded Theory (GT) analysis on 1444 previously published vulnerability reports and weaknesses with a team of three software developers and security experts. We extract vulnerability information from two publicly available repositories: 1) Common Vulnerabilities and Exposures, and 2) Common Weakness Enumeration. We ask three key questions: where the attacks come from, what they target, and how they emerge, and to help answer these questions we define three core categories for attack surface components: Entry points, Targets, and Mechanisms. We extract attack surface concepts related to each category from collected vulnerability information using the GT analysis and provide a comprehensive categorization that represents attack surface components of software systems from various perspectives. The comparison of the proposed attack surface model with the literature shows in the best case previous works cover only 50% of the attack surface components at network level and only 6.7% of the components at code level.