Cisco Foundation AI Releases Antares: 350M and 1B Open-Weight Models That Localize Known Vulnerabilities Inside Real Codebases
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 lic
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Cisco Foundation AI has released Antares, a family of small language models designed to identify the specific files in a codebase that contain known security vulnerabilities. The models are trained to receive a vulnerability description and search through a repository to locate the affected files, automating a task that currently requires developers to manually search unfamiliar code and trace dependencies. The smaller 1 billion parameter model reaches performance comparable to much larger systems, demonstrating that task-specific training on security data is more effective than raw model size. Antares is positioned as a first triage step to reduce the cost of connecting external vulnerability information to internal source code, though it does not replace existing security tools like dependency scanners or dynamic testing.
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