# Google Open-Sources Mantis: A Modular Skills Toolkit That Lets Coding Agents Find, Reproduce and Patch Vulnerabilities > Source: > Published: 2026-09-09 22:58:29+00:00 Google has open-sourced [Mantis](https://github.com/google/mantis), a stack-agnostic toolkit of security review skills that lets an AI coding agent run the whole vulnerability lifecycle. It finds a suspected flaw, strips the false positives, reproduces the bug inside a sandbox, writes a minimal patch, re-attacks that patch, and scores the residual risk. Mantis is not a scanner you aim at a repository and walk away from. It is a set of slash commands your existing coding agent loads, plus a strict set of rules about where that agent is allowed to execute code. **Is it deployable?** Yes for local and internal evaluation, not yet for production. You can clone it today and run it with [Gemini CLI](https://github.com/google-gemini/gemini-cli), Antigravity CLI, the Google ADK, or any comparable agent framework. ## **The pipeline** Mantis publishes each stage as a separate skill directory, invoked as a slash command and chained sequentially. A supervisor skill, `/mantis-meta-agent`, can drive the whole loop in a long-lived session. The early stages learn the target: `/mantis-history` mines version control history for past security fixes, `/mantis-summarize` writes the directory maps, `/mantis-architecture` builds a Markdown knowledge base, `/mantis-threat-model` derives trust boundaries, and `/mantis-plan` produces a targeted roadmap. The middle stages find and filter: `/mantis-researcher` sweeps files against the plan, then `/mantis-dedupe`, `/mantis-review` and `/mantis-critic` collapse duplicates, apply negative rules, and drop issues that cannot occur in a release build. The late stages prove and fix: `/mantis-reproduce` executes payloads in gVisor or a VM with networking disabled, `/mantis-chain` assembles multi-step exploit chains from individually confirmed findings, `/mantis-patch` applies and verifies the fix, `/mantis-calibrate` assigns a risk score from 1 to 10, `/mantis-reflect` writes learnings back for the next pass, and `/mantis-report` produces the human-readable review packet. A newer skill, [`/mantis-advise`](https://github.com/google/mantis/blob/main/mantis-advise/SKILL.md), inverts the flow. It queries the accumulated threat model, past bug lineages and verified patch patterns before you write code, so the same class of bug does not land twice. ## **But why?** Most agentic security tooling stops at generating findings. Mantis is interesting because it treats the reproducer and the re-attack as the trust boundary, and because it publishes the inter-stage contracts so teams can wrap the skills in a deterministic harness instead of trusting an LLM to orchestrate shell commands. ## **Key Takeaways** - Mantis is a modular skills toolkit for coding agents, not a standalone scanner or a supported Google product. - Its differentiator is grounding: sandboxed reproduction and patch re-attack, not model confidence. - A hierarchical summary tree cuts token overhead by over 85 percent, per Google. - Google cites sub-7 percent true-positive rates for naive AI code scanning as the problem Mantis targets. - Deployable locally under Apache 2.0 but not recommended yet for production. Check out the [**google/mantis on GitHub**](https://github.com/google/mantis), [** Agent Reference Guide**](https://github.com/google/mantis/blob/main/README_AGENTS.md), [** Cloud CISO Perspectives**,](https://cloud.google.com/blog/products/identity-security/cloud-ciso-perspectives-how-google-cloud-security-uses-ai-internally) and [**Getting started with Mantis**](https://cloud.google.com/blog/products/identity-security/getting-started-with-the-mantis-harness-to-find-and-fix-bugs). Also, feel free to follow us on **[Twitter](https://x.com/intent/follow?screen_name=marktechpost)** and don’t forget to join our **[150k+ML SubReddit](https://www.reddit.com/r/machinelearningnews/)** and Subscribe to **[our Newsletter](https://magic.beehiiv.com/v1/f5e63dd4-5653-4f09-83e2-321a8b1ba526?email={{email}})**. Wait! are you on telegram? [now you can join us on telegram as well.](https://t.me/machinelearningresearchnews) Need to partner with us for promoting your GitHub Repo OR Hugging Face Page OR Product Release OR Webinar etc.? [Connect with us](https://forms.gle/wbash1wF6efRj8G58) Michal Sutter is a data science professional with a Master of Science in Data Science from the University of Padova. With a solid foundation in statistical analysis, machine learning, and data engineering, Michal excels at transforming complex datasets into actionable insights.