SOURCE-LINKED INTELLIGENCE
Athena: Vulnerability-Affected Library Identification via Knowledge Graph Completion
A single vulnerability in a widely used library can cascade through millions of dependent applications, yet more than half of vulnerability database entries contain missing or incorrect affected-library information. Existing automated approaches neglect the relational structure of vulnerability databases, treating identification as an isolated text retrieval problem. In this paper, we propose Athena, the first graph-based approach for vulnerability affected library identification. Athena models vulnerability databases as a knowledge graph and reformulates the identification problem as knowledg
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-01T13:01:45.000Z
First collected: 2026-09-21T06:01:56.170Z. This is not the publication date.