Antares: Highly Efficient Open Weight AI Models for Vulnerability Localization
Cisco has released Antares, a family of open-weight AI models designed for vulnerability research, which can help cybersecurity professionals pinpoint known vulnerabilities within a codebase. The models, Antares-350M and Antares-1B, are available on Hugging Face and have been shown to outperform other models in benchmark testing. By providing compact and efficient models, Antares aims to make AI-assisted security more accessible to smaller security teams and organizations.
- ▪Antares is a family of security small language models purpose-built for vulnerability localization.
- ▪The models, Antares-350M and Antares-1B, are open-weight models available on Hugging Face.
- ▪Benchmark testing shows that these models outperform many powerful closed- and open-weight models at a fraction of the cost.
Hacker News (Newest) files mainly under programming. We currently carry 5,306 of its stories.
Story provenance
Source · retrieval · rights · ranking — open for full record
inspect →
Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | Cisco Blogs |
| Canonical URL | https://blogs.cisco.com/ai/introducing-antares-the-most-efficient-open-weight-ai-models-for-vulnerability-localization |
| Publication time | Mon, 27 Jul 2026 05:13:21 +0000 |
| Retrieval time | 2026-07-21T23:06:07.482Z |
| Last seen | 2026-07-21T23:06:07.482Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | JTIxoy58PBm7 |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
July 21, 2026 Leave a Comment Artificial Intelligence - AI Introducing Antares: Highly Efficient Open Weight AI Models for Vulnerability Localization6 min read 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.
…
Excerpt limited to ~120 words for fair-use compliance. The full article is at Cisco Blogs.
Discussion
0 commentsMore from Cisco Blogs
