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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
On the Prevalence and Usage of Commit Signing on GitHub: ...
Anupam Sharma, Sreyashi Karmakar, Gayatri Priyadarsini Kancherla · 2025-04-27 · via cs.SE updates on arXiv.org

GitHub is one of the most widely used public code development platform. However, the code hosted publicly on the platform is vulnerable to commit spoofing that allows an adversary to introduce malicious code or commits into the repository by spoofing the commit metadata to indicate that the code was added by a legitimate user. The only defense that GitHub employs is the process of commit signing, which indicates whether a commit is from a valid source or not based on the keys registered by the users. In this work, we perform an empirical analysis of how prevalent is the use of commit signing in commonly used GitHub repositories. To this end, we build a framework that allows us to extract the metadata of all prior commits of a GitHub repository, and identify what commits in the repository are verified. We analyzed 60 open-source repositories belonging to four different domains -- web development, databases, machine learning and security -- using our framework and study the presence of verified commits in each repositories over five years. Our analysis shows that only ~10% of all the commits in these 60 repositories are verified. Developers committing code to security-related repositories are much more vigilant when it comes to signing commits by users. We also analyzed different Git clients for the ease of commit signing, and found that GitKraken provides the most convenient way of commit signing whereas GitHub Web provides the most accessible way for verifying commits. During our analysis, we also identified an unexpected behavior in how GitHub handles unverified emails in user accounts preventing legitimate owner to use the email address. We believe that the low number of verified commits may be due to lack of awareness, difficulty in setup and key management. Finally, we propose ways to identify commit ownership based on GitHub's Events API addressing the issue of commit spoofing.