{"slug": "antares-1b-cisco-s-tiny-model-that-hunts-code-vulnerabilities-locally", "title": "Antares-1B: Cisco's Tiny Model That Hunts Code Vulnerabilities Locally", "summary": "Cisco's Foundation AI team released Antares-1B, a 1 billion parameter open-weight model that localizes software vulnerabilities in code repositories and runs on a single GPU, consuming under 5GB of VRAM in testing. Built on IBM's Granite 4.0 1B backbone and released under the Apache 2.0 license, Antares-1B outperforms larger models like GLM, Gemini 3 Pro, and GPT-5 mini on Cisco's internal vulnerability localization benchmark, according to the model's release notes.", "body_md": "# Antares-1B: Cisco's Tiny Model That Hunts Code Vulnerabilities Locally\n\nCisco's 1B-parameter Antares model localizes vulnerabilities in code repos and runs on a single GPU. Here's how it works and how to deploy it.\n\n## What is Antares-1B?\n\nAntares-1B is a 1 billion parameter open weight model from Cisco’s Foundation AI team, built specifically for vulnerability localization. Instead of generating chat responses or writing code, it takes a code repository and a specific weakness type (a CWE, or Common Weakness Enumeration ID) and acts like a security analyst, running terminal commands such as grep, find, and cat to trace the flaw down to the exact file and line. It’s fine-tuned on IBM’s Granite 4.0 1B backbone, released under the Apache 2.0 license, and available on Hugging Face under the fdtn-ai organization.\n\nThe name is a nod to the brightest star in Scorpius, the constellation’s answer to Ares (Mars). The pitch behind the name matches the model’s actual goal: punch above its weight class. At 1B parameters, Antares is small enough to run comfortably on a single consumer or workstation GPU, yet Cisco reports it outperforming much larger general-purpose models on its own vulnerability localization benchmark.\n\n## TL;DR\n\n**Antares-1B** is a specialized security model from Cisco Foundation AI that finds where a known vulnerability class lives in a codebase, rather than writing or reviewing code generally.- It’s built on\n**IBM’s Granite 4.0 1B** architecture and released under the**Apache 2.0** license, so weights and derivative use are unrestricted for commercial and research work. - On Cisco’s internal vulnerability localization benchmark, Antares reportedly beats much larger models including\n**GLM, Gemini 3 Pro, and GPT-5 mini**, despite having a fraction of their parameters. - The model behaves like an\n**agentic terminal tool**: it issues real filesystem commands (grep, find, cat) against a cloned repo to hunt down the files tied to a specific CWE. - Local deployment through\n**vLLM** consumed just under**5GB of VRAM** in testing, making it practical on a single GPU with room to spare. - Cisco ships a dedicated\n**Antares CLI** alongside the model weights, plus smaller 3B and sub-1B variants and GGUF quantized files for different hardware budgets. - In a live test on a deliberately vulnerable sample application, Antares correctly localized a\n**server-side request forgery (SSRF)** flaw to the exact line of code responsible.\n\n## How does Antares-1B actually find vulnerabilities?\n\nAntares doesn’t just read code and guess. It’s trained to behave like an agent operating in a terminal environment. Given a repository path and a target CWE, it issues a sequence of standard shell commands (grep for pattern matches, find for file discovery, cat to inspect contents) and reasons over the output to narrow down candidate files. This mirrors how a human security analyst actually works through an unfamiliar codebase: search broadly, inspect promising files, and correlate what you find with the known signature of the vulnerability class.\n\nBecause the model is trained on this specific task rather than general code generation or chat, it doesn’t need to be huge. Vulnerability localization is a narrower, more structured problem than “write me a web app,” and Cisco’s benchmark results suggest that specialization beats raw scale here. The model was reportedly evaluated against and outperformed several larger models on Cisco’s own vulnerability localization benchmark, according to the model’s release notes.\n\n## What hardware do you need to run it locally?\n\nVery little, by modern LLM standards. In a local deployment test using an Nvidia RTX 6000 with 48GB of VRAM, serving Antares-1B through vLLM consumed just under 5GB of VRAM. That leaves enormous headroom on a card like the RTX 6000, meaning the model would likely run fine on much smaller consumer GPUs, especially with the smaller sub-1B variant or GGUF quantized files that Cisco also published.\n\nThe practical requirements for a local setup are straightforward:\n\n- A Python environment (the demonstrated setup used\n`uv`\n\nfor virtual environment and dependency management) - A Hugging Face account with a free access token, since the model is gated and requires accepting terms before download\n- vLLM installed to serve the model behind an OpenAI-compatible API endpoint\n- The Antares CLI, which Cisco bundles as an assets file alongside the model weights on Hugging Face\n\nOnce vLLM is serving the model locally, you point the CLI at that local endpoint using a small config file specifying the model name, backend, and endpoint URL. No external API key is required since everything runs on your own hardware.\n\n## Is Antares-1B worth using over a bigger general model?\n\nFor the specific task it’s built for, the case is compelling. Vulnerability localization is a well-defined problem: find where a specific weakness type lives in a repo. General-purpose frontier models can do this too, but they’re expensive to run at scale, slower, and not optimized for the agentic grep-find-cat workflow that actually solves the problem efficiently. A 1B model that fits in under 5GB of VRAM and matches or beats those larger models on this narrow benchmark is a meaningful efficiency win, especially for teams wanting to run vulnerability scans locally, offline, or embedded into CI pipelines without sending code to a third-party API.\n\n## Remy doesn't build the plumbing. It inherits it.\n\nOther agents wire up auth, databases, models, and integrations from scratch every time you ask them to build something.\n\nRemy ships with all of it from MindStudio — so every cycle goes into the app you actually want.\n\nThat said, the model is not a general-purpose security auditor. It’s designed to localize a known CWE within a codebase, not to independently discover novel vulnerability classes or replace a full security review process. Testing on a real repository found it correctly identified an SSRF vulnerability, pinpointing the exact file and line where a user-supplied URL was passed unvalidated into a request call. It also surfaced a related test file exercising that same flaw as a secondary result, showing some ability to rank and contextualize findings rather than just returning a single guess. But the model’s own documentation and hands-on testing suggest rough edges remain, and Cisco doesn’t position it as production-ready without validation.\n\n## How do you set it up with the CLI and vLLM?\n\nThe workflow demonstrated for local deployment breaks down into a few concrete steps:\n\n- Create a Python virtual environment and download the Antares-1B weights from Hugging Face (requires accepting the gated model terms and using a free access token).\n- Install the accompanying CLI tooling, which Cisco distributes as a zipped assets package alongside the model.\n- Install vLLM and serve the local model weights, which exposes an OpenAI-compatible API endpoint.\n- Register the local vLLM endpoint with the Antares CLI by creating a small config file that specifies the model name, backend type, and endpoint URL. No API key is needed since it’s a fully local connection.\n- Confirm the setup with a model list command in the CLI, which should show the locally served Antares model as available.\n- Clone the target repository you want to scan, then run the CLI’s vulnerability-hunting command, specifying the repo path and the CWE ID you’re checking for.\n\nFrom there, Antares works through the codebase autonomously and returns ranked file and line-level findings tied to that vulnerability class.\n\n## Frequently Asked Questions\n\n### What is Antares-1B used for?\n\nIt’s used to localize specific known vulnerability types (identified by CWE ID) within a codebase, pointing to the exact files and lines responsible rather than performing general code review or generation.\n\n### How many parameters does Antares-1B have, and what’s it built on?\n\nIt has 1 billion parameters and is fine-tuned from IBM’s Granite 4.0 1B model architecture, released by Cisco’s Foundation AI team under the Apache 2.0 license.\n\n### Can Antares-1B run on a single GPU?\n\nYes. Served through vLLM, it used just under 5GB of VRAM in testing, which fits comfortably on most modern GPUs, including consumer-grade cards, with plenty of headroom left over.\n\n### Does Antares-1B replace larger models like GPT-5 mini or Gemini for security tasks?\n\nNot entirely. It reportedly outperforms those larger models specifically on Cisco’s vulnerability localization benchmark, but it’s a narrow specialist tool for that one task, not a general-purpose coding or reasoning model.\n\n### Is Antares-1B free to use commercially?\n\nYes, the model weights are released under the Apache 2.0 license, which permits commercial use, modification, and redistribution, though the model itself is gated on Hugging Face and requires accepting terms before download.", "url": "https://wpnews.pro/news/antares-1b-cisco-s-tiny-model-that-hunts-code-vulnerabilities-locally", "canonical_source": "https://www.mindstudio.ai/blog/antares-1b-vulnerability-localization-local/", "published_at": "2026-08-18 00:00:00+00:00", "updated_at": "2026-08-18 21:43:03.339970+00:00", "lang": "en", "topics": ["artificial-intelligence", "ai-products", "ai-agents", "ai-research"], "entities": ["Cisco", "IBM", "Antares-1B", "Granite 4.0", "Hugging Face", "vLLM", "Nvidia RTX 6000", "GLM"], "alternates": {"html": "https://wpnews.pro/news/antares-1b-cisco-s-tiny-model-that-hunts-code-vulnerabilities-locally", "markdown": "https://wpnews.pro/news/antares-1b-cisco-s-tiny-model-that-hunts-code-vulnerabilities-locally.md", "text": "https://wpnews.pro/news/antares-1b-cisco-s-tiny-model-that-hunts-code-vulnerabilities-locally.txt", "jsonld": "https://wpnews.pro/news/antares-1b-cisco-s-tiny-model-that-hunts-code-vulnerabilities-locally.jsonld"}}