aiqbench analysis
Cisco's Antares models demonstrate that specialized small language models can effectively localize known software vulnerabilities within large codebases at a fraction of the cost of general-purpose giants like GPT-5.5 or massive open-weight counterparts, validating the utility of narrow security SLMs for automated triage workflows. While these tools do not replace existing application security pipelines, they offer a significant efficiency gain by automating the expensive step of connecting external vulnerability data to internal source code repositories.
Cisco Foundation AI Releases Antares: 350M and 1B Open-Weight Models That Localize Known Vulnerabilities Inside Real Codebases
Cisco Foundation AI has released Antares, a family of small language models trained to pinpoint where known vulnerabilities live inside a codebase. Antares-1B reaches 0.209 File F1 on the new Vulnerability Localization Benchmark, above GLM-5.2 at 753B parameters and Gemini 3 Pro. The untrained Granite 4.0 checkpoints score near zero under the same protocol, so post-training supplies almost all of the capability. A full 500-task sweep runs in roughly 13 minutes on a single H100 for under a dollar, against $141 for GPT-5.5. The post Cisco Foundation AI Releases Antares: 350M and 1B Open-Weight Models That Localize Known Vulnerabilities Inside Real Codebases appeared first on MarkTechPost.
Cisco Foundation AI has released Antares, a family of security small language models (SLMs) built for one narrow security task. The task is vulnerability localization. Given a vulnerability description and a repository, find the files containing the flaw. Two models are open-weight and available now on Hugging Face, Antares-350M and Antares-1B. Both are Apache 2.0. Cisco team also shipped the Vulnerability Localization Benchmark (VLoc Bench), a 500-task agentic evaluation, under the same license. The main result is not a new state of the art. It is that…
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