Cisco Foundation AI Releases Antares: 350M and 1B Open-Weight Models That Localize Known Vulnerabilities Inside Real Codebases

Cisco Foundation AI released Antares, a family of small language models (350M and 1B parameters) that localize known vulnerabilities inside real codebases. Antares-1B achieves 0.209 File F1 on the Vulnerability Localization Benchmark, outperforming GLM-5.2 (753B parameters) and Gemini 3 Pro, while a full 500-task sweep runs in about 13 minutes on a single H100 for under $1, compared to $141 for GPT-5.5.

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 https://www.marktechpost.com/2026/07/21/cisco-foundation-ai-releases-antares-350m-and-1b-open-weight-models-that-localize-known-vulnerabilities-inside-real-codebases/ appeared first on MarkTechPost https://www.marktechpost.com .