Cisco Foundation AI Releases Antares: 350M and 1B Open-Weight Models That Localize Known Vulnerabilities Inside Real Codebases
MarkTechPost Michal Sutter
Cisco Foundation AI released Antares, a family of small language models designed to localize vulnerabilities in source code repositories by matching vulnerability descriptions to affected files. The 1B-parameter model achieves a File F1 score of 0.209, compared to GPT-4o's 0.229, and was evaluated on VLoc Bench, a 500-task benchmark derived from real GitHub security advisories across npm, pip, Maven, Go, Rust, and Composer ecosystems. The models are open-weight and available on Hugging Face under Apache 2.0, intended to reduce the cost of the initial triage step in software security workflows rather than replace existing security toolchains.
Why it matters
Cisco Foundation AI has released Antares, a family of small language models trained to pinpoint where known vulnerabilities live inside a codebase. Antares-1B reaches 0.209 File F1 on the new Vulnerability Localization Benchmark, above GLM-5.2 at 753B parameters and Gemini 3 Pro. The untrained Granite 4.0 checkpoints score near zero under the same protocol, so post-training supplies almost all of the capability. A full 500-task sweep runs in roughly 13 minutes on a single H100 for under a dollar, against $141 for GPT-5.5. The post Cisco Foundation AI Releases Antares: 350M and 1B Open-Weight Models That Localize Known Vulnerabilities Inside Real Codebases appeared first on MarkTechPost.
