Cisco has revealed a family of open-weight AI models called Antares that, it said, can help security teams isolate potentially vulnerable parts of a software repository before deeper investigation begins.
Rather than detecting a specific CVE or generating a patch, these models search a codebase using only a Common Weakness Enumeration (CWE) description and return the files most likely to contain that class of vulnerability.
“Its purpose is to reduce a large codebase to a focused set of files that a security professional or a downstream security workflow should investigate,” Cisco’s AI researcher Supriti Vijay said via email. “The goal is not to replace a security engineer’s judgement or send them on a wild-goose chase, but to reduce fatigue and workload by helping them triage an issue earlier and focus their investigation on the most relevant parts of the codebase.”
The Antares family consists of models with 350 million, 1 billion, and 3 billion parameters trained specifically for repository-scale vulnerability localization.
The company said its largest model approaches the performance of GPT-5.5 on its internal vulnerability localization (Vloc) benchmark while remaining small enough for low-cost local deployment.
