Descriptions:
Fahd Mirza demonstrates Antares 1B, a 1-billion parameter model released by Cisco’s Foundation AI team specifically for vulnerability localization in code repositories. Named after the brightest star in Scorpius, Antares is built on IBM’s Granite 4 1B backbone, released under the Apache 2.0 license, and available as a gated model on Hugging Face. On Cisco’s own vulnerability localization benchmark, it reportedly outscores much larger models including GLM 5.2, Gemini 3 Pro, and GPT-5 Mini.
The video walks through the complete local setup: creating a Python virtual environment with UV, downloading the model from Hugging Face, serving it via vLLM for an OpenAI-compatible API endpoint (consuming just under 5GB of VRAM), and configuring the Antares CLI. Mirza then clones a deliberately vulnerable restaurant web application and directs Antares to hunt for a CWE-918 Server-Side Request Forgery weakness. The model successfully pinpoints the vulnerability to `menu/utils.py`, identifying the exact line where a user-supplied `image_url` parameter is passed to `requests.get()` without validation — and ranks the test file exercising that flaw as the second most relevant result.
Mirza notes the model has rough edges and recommends testing before production use. Antares also ships in 3B and sub-1B variants, as well as GGUF quantized files for further hardware flexibility. For security engineers exploring AI-assisted code review on local hardware, this is a practical introduction to a focused, open-weight specialist model.
📺 Source: Fahd Mirza · Published August 17, 2026
🏷️ Format: Hands On Build
