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Introducing Antares: Highly Efficient Open Weight AI Models for Vulnerability Localization - Cisco Blogs

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Cisco Blogs / Artificial Intelligence - AI / Introducing Antares: Highly Efficient Open Weight AI Models for Vulnerability Localization

Artificial Intelligence - AI

Introducing Antares: Highly Efficient Open Weight AI Models for Vulnerability Localization<br>6 min read<br>Amin Karbasi

Collaborator(s): Supriti Vijay, Aman Priyanshu, Didier Chapoteau, Arthur Goldblatt, Kimia Majd, Fraser Burch, Jianliang He, Baturay Saglam, Takahiro Matsumoto, Zhuoran Yang

Today, Cisco is introducing Antares, a family of security small language models (SLMs) purpose-built for one of the hardest, most time-consuming and expensive problems in security: pinpointing where known vulnerabilities exist within a codebase.

We are releasing two of these models—Antares-350M and Antares-1B—as open-weight models now available to the broader community on Hugging Face. Benchmark testing shows that these models outperform many powerful closed- and open-weight models in this critical security task at a fraction of the cost. And they’re compact enough to run locally, heading off the need to send sensitive codebases to the cloud.

By combining open-weight models with Antares, open specifications with Foundry Security Spec, secure coding guidance with CodeGuard, and a new benchmark, we are working to define the types of practical, trustworthy AI tools that will help cybersecurity professionals do their job better, as well as cultivating an ecosystem and standards for further enterprise adoption of AI security tools.

Figure 1. The Antares family of small language model outperforms a dozen larger models tested in the new Vulnerability Localization Benchmark. (Antares-3B is coming soon.)

Small Models for a Costly and Sensitive Security Workflow

Software security depends on connecting external vulnerability knowledge like public databases, advisories, and CWEs to internal code. That work is difficult because repositories are large, security signals are noisy, and the relevant evidence is rarely in one obvious place. Analysts often need to search through unfamiliar code, follow naming conventions, inspect call paths, compare candidate files, and decide whether a weakness is actually present.

Additionally, organizations need security tooling that is not only capable, but practical to deploy. Compact models reduce inference costs, support local or on-premises operations, and help teams keep sensitive source code within their own environment. Antares can unlock the power of AI-assisted security for universities, public sector institutions, and smaller security teams that may have lacked the resources to use token-intensive AI models for critical tasks.

Antares is purpose-built to address both problems.

"Software has always shipped with vulnerabilities, and vast bodies of legacy code still carry latent flaws. AI now pushes on the problem from both sides: agents write more of the code, and are growing capable of exploiting it. That makes cheap, continuous verification a control that has to sit close to the loop, checking code before it lands. Small models are especially compelling here: they run locally, so proprietary code never leaves the machine, and they’re fast enough to gate an agent’s output in real time. Antares-1b is a good example: it recognizes vulnerabilities in code it is shown, across several weakness classes and languages. The provided CLI packages the model’s capabilities into a practical workflow, making it easy to scan codebases, inspect findings, and integrate it into automated pipelines. What I found compelling was seeing a model this small navigate a codebase and identify security issues that would otherwise require larger models or more manual analysis.”

— Reza Shokri, Associate Professor of Computer Science at the National University of Singapore (NUS)

"Security can’t be a luxury good, yet advanced AI-based detection has largely belonged to organizations with frontier-scale budgets. Antares’s 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."

— Amin Saberi, Professor of Management Science and Engineering and Director of Language, Data, and Reasoning Lab at Stanford University

What Makes Antares Different

Traditional static analysis tools are valuable, but they are often rule-heavy and can produce results that require substantial triage....