Cisco Foundation AI has released Antares, a family of security small language models (SLMs) built for one narrow security task. The task is vulnerability localization. Given a vulnerability description and a repository, find the files containing the flaw.
Two models are open-weight and available now on Hugging Face, Antares-350M and Antares-1B. Both are Apache 2.0. Cisco team also shipped the Vulnerability Localization Benchmark (VLoc Bench), a 500-task agentic evaluation, under the same license.
The main result is not a new state of the art. It is that a 1B model reaches 0.209 File F1. GPT-5.5 reaches 0.229, and a 753B open-weight model reaches 0.186.
The problem Antares is scoped to
Software security depends on connecting external vulnerability knowledge to internal source code. That knowledge lives in public databases, advisories, and Common Weakness Enumerations. The code lives in repositories that are large, modular, and dependency-rich.
