Arm Metis with GPT5.5 Cyber scores 98% on firmware vulnerability benchmark
Arm has developed an open-sourced AI security framework called Metis to enhance software vulnerability detection. Metis utilizes advanced analysis techniques and AI workflows to identify complex security issues across large codebases. The framework aims to improve detection quality, reduce false positives, and streamline the software development process.
- ▪Metis is currently running across more than 130 software projects within Arm.
- ▪The framework delivers up to 10x higher true positive rates and approximately 50% fewer false positives compared to leading static analysis tools.
- ▪Metis combines large language models with project-specific knowledge for contextual security analysis.
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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 | Arm Newsroom |
| Canonical URL | https://newsroom.arm.com/blog/arm-metis-agentic-ai-security |
| Publication time | Fri, 29 May 2026 14:37:09 +0000 |
| Retrieval time | 2026-05-29T14:50:01.336Z |
| Last seen | 2026-05-29T14:50:01.336Z |
| 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 | atChjFlNlS0a |
| 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
Arm Newsroom Blog Blog May 28, 2026 Agentic AI-powered Arm Metis advances security vulnerability discovery in software The open-sourced agentic AI security framework, delivers contextual AI-powered security analysis at scale to detect software vulnerabilities earlier and save time and costs By Mark Hambleton, SVP, Software, Arm Share In the era of AI, modern software systems are built across increasingly complex codebases, frameworks, runtimes and libraries. As these systems scale, so does the challenge of identifying security vulnerabilities before products reach customers. To help address this challenge, Arm’s product security team has developed and open-sourced Metis, an agentic AI security framework designed to identify complex security issues across large-scale codebases.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Arm Newsroom.
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