LibVulnWatch: A Deep Assessment Agent System and Leaderboard for Uncovering Hidden Vulnerabilities in Open-Source AI Libraries (2505.08842v2)

Abstract: Open-source AI libraries are foundational to modern AI systems, yet they present significant, underexamined risks spanning security, licensing, maintenance, supply chain integrity, and regulatory compliance. We introduce LibVulnWatch, a system that leverages recent advances in LLMs and agentic workflows to perform deep, evidence-based evaluations of these libraries. Built on a graph-based orchestration of specialized agents, the framework extracts, verifies, and quantifies risk using information from repositories, documentation, and vulnerability databases. LibVulnWatch produces reproducible, governance-aligned scores across five critical domains, publishing results to a public leaderboard for ongoing ecosystem monitoring. Applied to 20 widely used libraries, including ML frameworks, LLM inference engines, and agent orchestration tools, our approach covers up to 88% of OpenSSF Scorecard checks while surfacing up to 19 additional risks per library, such as critical RCE vulnerabilities, missing SBOMs, and regulatory gaps. By integrating advanced language technologies with the practical demands of software risk assessment, this work demonstrates a scalable, transparent mechanism for continuous supply chain evaluation and informed library selection.

LibVulnWatch: Assessing Vulnerabilities in AI Libraries

The paper introduces LibVulnWatch, a sophisticated graph-based assessment framework dedicated to identifying and quantifying risks associated with open-source AI libraries. This framework is engineered as a coordinated system of specialized agents that perform evaluations grounded in evidence from repositories, documentation, and databases, generating comprehensive governance-aligned risk scores. The focus is on five domains crucial to Technical AI Governance: licensing, security, maintenance, dependency management, and regulatory compliance.

Overview

LibVulnWatch uses LangGraph to organize a directed acyclic graph of agents that systematically extract, verify, and analyze risks in AI libraries, publishing findings in a public leaderboard. This system was evaluated on 20 widely used libraries, including popular ML frameworks like TensorFlow and PyTorch, and inference engines and orchestration tools. By covering up to 88% of OpenSSF Scorecard checks while uncovering up to 19 additional risks per library, LibVulnWatch provides a more nuanced and exhaustive assessment compared to existing tools.

Numerical Results

The framework's application showed significant effectiveness in revealing hidden software vulnerabilities. For instance, libraries exhibited up to 88% alignment with existing security checks while identifying substantial additional risks—such as Remote Code Execution vulnerabilities and missing SBOMs. These findings underscore the urgency for thorough and systematic risk management in AI software supply chains, especially given the legal, security, and operational risks inherent in open-source components.

Implications for Technical AI Governance

From a theoretical perspective, LibVulnWatch advances the dialogue in Technical AI Governance by translating broad governance principles into practical, quantifiable metrics. This paper emphasizes the need for robust frameworks capable of continuous and transparent evaluation of AI systems, addressing gaps between policy intent and technical implementation. Practically, LibVulnWatch provides a scalable solution to monitor ecosystem health, supporting informed decision-making around library use based on security, compliance, and performance data.

Future Directions

The implications of LibVulnWatch extend into future AI development, where automated, verification-driven risk assessment systems will likely play a critical role. As AI technologies proliferate, the demand for scalable governance frameworks that ensure safe and ethical deployment will increase. Continuous refinement of agentic and graph-based approaches, combined with improved integration with existing vulnerability databases, could provide even more detailed and timely assessments, fostering greater public trust in AI systems.

Overall, LibVulnWatch serves as a pivotal tool in evolving the landscape of AI governance and operational risk management, highlighting the importance of proactive, transparent monitoring in safeguarding AI ecosystems.