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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
Internal Vulnerabilities, External Threats: A Grounded Fr...
Wenhao Yang, Minghui Zhou, Daniel Izquierdo Cortázar, Yehui Wang · 2025-10-30 · via cs.SE updates on arXiv.org

Enterprise engagement with open source has evolved from tactical adoption to strategic deep integration, exposing them to a complex risk landscape far beyond mere code. However, traditional risk management, narrowly focused on technical tools, is structurally inadequate for systemic threats like upstream "silent fixes", community conflicts, or sudden license changes, creating a dangerous governance blind spot. To address this governance vacuum and enable the necessary shift from tactical risk management to holistic risk governance, we conducted a grounded theory study with 15 practitioners to develop a holistic risk governance framework. Our study formalizes an analytical framework built on a foundational risk principle: an uncontrollable External Threat (e.g., a sudden license change in a key dependency) only becomes a critical risk when it exploits a controllable Internal Vulnerability (e.g., an undefined risk appetite for single-vendor projects), which then amplifies the impact. The framework operationalizes this principle through a clear logical chain: "Objectives -> Threats -> Vulnerabilities -> Mitigation" (OTVM). This provides a holistic decision model that transcends mere technical checklists. Based on this logic, our contributions are: (1) a "Strategic Objectives Matrix" to clarify goals; (2) a systematic dual taxonomy of External Threats (Ex-Tech, Ex-Comm, Ex-Eco) and Internal Vulnerabilities (In-Strat, In-Ops, In-Tech); and (3) an actionable mitigation framework mapping capability-building to these vulnerabilities. The framework's analytical utility was validated by three industry experts through retrospective case studies on real-world incidents. This work provides a novel diagnostic lens and a systematic path for enterprises to shift from reactive "firefighting" to proactively building an organizational "immune system".