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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
Understanding Concurrency Vulnerabilities in Linux Kernel
Zunchen Huang, Shengjian Guo, Meng Wu, Chao Wang · 2022-12-11 · via cs.SE updates on arXiv.org

While there is a large body of work on analyzing concurrency related software bugs and developing techniques for detecting and patching them, little attention has been given to concurrency related security vulnerabilities. The two are different in that not all bugs are vulnerabilities: for a bug to be exploitable, there needs be a way for attackers to trigger its execution and cause damage, e.g., by revealing sensitive data or running malicious code. To fill the gap, we conduct the first empirical study of concurrency vulnerabilities reported in the Linux operating system in the past ten years. We focus on analyzing the confirmed vulnerabilities archived in the Common Vulnerabilities and Exposures (CVE) database, which are then categorized into different groups based on bug types, exploit patterns, and patch strategies adopted by developers. We use code snippets to illustrate individual vulnerability types and patch strategies. We also use statistics to illustrate the entire landscape, including the percentage of each vulnerability type. We hope to shed some light on the problem, e.g., concurrency vulnerabilities continue to pose a serious threat to system security, and it is difficult even for kernel developers to analyze and patch them. Therefore, more efforts are needed to develop tools and techniques for analyzing and patching these vulnerabilities.