Antares: Foundation Models for Agentic Vulnerability Localization

Abstract:Vulnerability localization is a fundamental step in software security, requiring models to reason over large codebases and iteratively identify vulnerable implementations. We present Antares, a family of compact language models (350M, 1B, and 3B parameters) for agentic vulnerability localization. Based on IBM Granite base models, Antares is trained through a two-stage pipeline that combines supervised fine-tuning on cybersecurity reasoning and repository exploration data with reinforcement learning from verifiable rewards over vulnerable repositories. Across extensive evaluations, Antares-3B approaches GPT-5.5 while outperforming open-weight models over 200x larger in size. The Antares family further enables fast, low-cost local inference, completing a full 500-task evaluation sweep in approximately 15 minutes on a single H100 GPU, corresponding to an amortized evaluation time of under 2 seconds and less than $0.002 per task.

Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)

arXiv:2608.02407 [cs.CR]

(or arXiv:2608.02407v1 [cs.CR] for this version)

https://doi.org/10.48550/arXiv.2608.02407 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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