Introducing Antares: Highly Efficient Open Weight AI Models for Vulnerability Localization
Cisco has released Antares, a family of open-weight small language models (SLMs) purpose-built to pinpoint where known vulnerabilities exist within a codebase. The initial models, Antares-350M and Antares-1B, are designed to be compact and efficient enough to run locally, addressing the high cost, sensitivity, and complexity of vulnerability triage by outperforming many larger models on the new Vulnerability Localization Benchmark. These models employ an iterative search pattern that mimics a human investigator, navigating codebases to rank files likely to contain specific vulnerabilities based on descriptions like CWE categories. By offering these models openly and highlighting their low operational cost and speed, Cisco aims to make advanced AI-assisted security analysis accessible to organizations with constrained resources, such as universities and public-sector teams, while also fostering a broader ecosystem of practical AI security tools through initiatives like the Foundry Security Spec and CodeGuard.
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