Cisco Launches Low-Cost AI Models for Source Code Security

Cisco Foundation AI has announced Antares, a small language model (SLM) designed to assist security teams in identifying known vulnerabilities within codebases. Antares is an open-weight model, aiming to provide a cost-effective and data-sovereign solution compared to expensive large language models and offering a significant improvement in efficiency and accuracy over existing open-weight and closed LLM models. Cisco describes Antares as “purpose-built for one of the hardest, most time-consuming and expensive problems in security: pinpointing where known vulnerabilities exist within a codebase.”

Currently, security teams often face a dilemma: subscribe to and use a major closed large language (LLM) AI frontier model, which can rapidly become very expensive, or download smaller ‘open-weight’ general language models (GLM)s. Open weight is not quite open source, but the ‘weights’ (effectively the brain of the model that defines how it ‘thinks’) can be controlled by the user. This approach is particularly useful where data sovereignty is important or regulated: the repository being analyzed by an open weight GLM is not required to leave the company’s possession.

However, open-weight GLMs often generate a higher level of false positives, forcing companies to choose between high cost with lower false positives and low cost with higher false positives. Antares is designed to be open-weight, but SLM-designed, specifically to find known vulnerabilities in a codebase – in other words, low cost with low false positives while maintaining data sovereignty; improving on both GLM open weight models and large scale closed LLM frontier models.

Its processing follows a search pattern resembling the way a human investigator works through a repository. “Each model starts from a vulnerability description, searches for relevant code patterns, reads candidate files, incorporates new evidence, changes direction when a path is unhelpful, and narrows toward the files most likely to matter.” The goal, says Cisco, is not to replace expert judgment, but rather to assist in the first stages of source-code vulnerability triage.

Antares is primarily targeted at those organizations with budget-restrained security resources: “Universities, research institutions, nonprofits, and public-sector teams that still maintain important software.” To solve this last problem, Cisco is also releasing a new benchmark, the Vulnerability Localization Benchmark. It is described as “a 500-entry benchmark requiring 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). The output is a ranked list of source files likely to contain a relevant vulnerability. Security teams can use this list to start their triage process.

“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 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.”