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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
Advanced Vulnerability Scanning for Open Source Software:...
Victor Wen, Zedong Peng · 2026-01-01 · via cs.SE updates on arXiv.org

Automated detection of software vulnerabilities remains a critical challenge in software security. Log4j is an industrial-grade Java logging framework listed as one of the top 100 critical open source projects. On Dec. 10, 2021 a severe vulnerability Log4Shell was disclosed before being fully patched with Log4j2 version 2.17.0 on Dec. 18, 2021. However, to this day about 4.1 million, or 33 percent of all Log4j downloads in the last 7 days contain vulnerable packages. Many Log4Shell scanners have since been created to detect if a user's installed Log4j version is vulnerable. Current detection tools primarily focus on identifying the version of Log4j installed, leading to numerous false positives, as they do not check if the software scanned is really vulnerable to malicious actors. This research aims to develop an advanced Log4j scanning tool that can evaluate the real-world exploitability of the software, thereby reducing false positives. Our approach first identifies vulnerabilities and then provides targeted recommendations for mitigating these detected vulnerabilities, along with instant feedback to users. By leveraging GitHub Actions, our tool offers automated and continuous scanning capabilities, ensuring timely identification of vulnerabilities as code changes occur. This integration into existing development workflows enables real-time monitoring and quicker responses to potential threats. We demonstrate the effectiveness of our approach by evaluating 28 open-source software projects across different releases, achieving an accuracy rate of 91.4% from a sample of 140 scans. Our GitHub action implementation is available at the GitHub marketplace and can be accessed by anyone interested in improving their software security and for future studies. This tool provides a dependable way to detect and mitigate vulnerabilities in open-source projects.