Antares: Foundation Models for Agentic Vulnerability Localization
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 learn
Record details
Published: 3 August 2026
Source: arXiv cs.AI
Category: Research
Topics: Agents & autonomy
Retrieved: 4 August 2026
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ethics.ai (3 August 2026), “Antares: Foundation Models for Agentic Vulnerability Localization,” evidence record 16098, https://ethics.ai/record/16098 (originally published by arXiv cs.AI).
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