Cisco releases Antares, compact AI models for finding code vulnerabilities

Vulnerability localization has long been a bottleneck in security operations—time-intensive work that demands expertise and often requires organizations to move sensitive code outside their environment. General-purpose AI models, while capable at broad code reasoning, were not designed for the specific task of finding known vulnerabilities within a repository. Antares addresses this gap by building on research from Cisco's Foundation AI team showing that compact models can learn useful search strategies through iterative exploration rather than relying solely on model scale.

The release of Antares alongside a new Vulnerability Localization Benchmark signals a shift toward specialized, measurable AI security tools. By publishing open-weight models and a benchmark, Cisco creates a shared standard against which future work can be evaluated—moving away from proprietary, closed-loop AI security products toward an ecosystem smaller teams and resource-constrained institutions can adopt. This is particularly significant for universities, nonprofits, and public-sector organizations that have historically lacked the budget for token-intensive AI services but maintain critical software that requires ongoing vulnerability management.

Cisco frames Antares as one piece of a broader effort to mature AI-assisted security. Foundry Security Spec provides an open blueprint for building agentic security systems with clear governance; CodeGuard contributes reusable secure coding rules; and Antares adds the compact models and benchmark needed to make vulnerability localization practical and measurable. The connective theme is shifting AI in security from one-off demos toward deployable systems practitioners can evaluate, govern, and improve.