Cisco Launches Antares AI Models for Source Code Security
Cisco Foundation AI has announced Antares, a small language model (SLM) designed to find known vulnerabilities in codebases. The model is named after the brightest star in the Scorpius constellation and comprises two models: Antares-350M and Antares-1B.
According to Cisco, Antares is "purpose-built for one of the hardest, most time-consuming and expensive problems in security: pinpointing where known vulnerabilities exist within a codebase." The model is designed to assist security teams in the first stages of source-code vulnerability triage, rather than replacing expert judgment.
Challenges in Source Code Security
When a vulnerability is discovered, security teams must investigate whether their company code includes that vulnerability, and if so, where it is. This is not easy and is currently best assisted by AI. However, the two primary AI options have their own problems. One option is to subscribe to and use a major closed large language (LLM) AI frontier model, which can rapidly become very expensive. The other option is to download smaller 'open-weight' general language models (GLM)s, which can generate a far higher level of false positives.
Cisco's Antares is designed to be open-weight, but SLM-designed, specifically to find known vulnerabilities in a codebase. The model follows a search pattern resembling the way a human investigator works through a repository, starting from a vulnerability description, searching for relevant code patterns, reading candidate files, incorporating new evidence, and narrowing toward the files most likely to matter.
Benchmarking Antares
To solve the problem of proof, Cisco is also releasing a new benchmark, the Vulnerability Localization Benchmark. The benchmark requires models to simultaneously navigate unfamiliar codebases efficiently and recognize vulnerability patterns associated with specific CWE categories.
Using this benchmark, Cisco tested Antares against a top performing closed LLM frontier model (OpenAI's GPT-5.5) and a top performing open weight model (Z.ai's GLM-5.2) and found that it completes faster at lower cost than its rivals (172x cheaper than the LLM and 15.2x cheaper than the GLM).
"Security can't be a luxury good, yet advanced AI-based detection has largely belonged to organizations with frontier-scale budgets," comments Amin Saberi, professor of management science and engineering at Stanford University. "Antares' results change that equation: near-frontier accuracy on secure code reasoning at a fraction of the cost, fast enough to run on every commit. At a moment when attackers are already using AI, that kind of efficiency isn't a nice-to-have; it's what makes always-on security scanning possible for every team."
Cisco's Goal
Cisco defines its forays into AI as designed to "help the community build a more durable foundation for AI-assisted defense: open specifications, reusable security knowledge, compact deployable models, and benchmarks that make progress measurable. The goal is to build toward a system where all security practitioners, regardless of on-prem or resource constraints, can effectively incorporate AI in everyday security operations."
Cisco's Antares is primarily targeted at those organizations with budget-restrained security resources, such as universities, research institutions, nonprofits, and public-sector teams that still maintain important software.
Source: SecurityWeek