Google Open-Sources Mantis: A Modular Skills Toolkit That Lets Coding Agents Find, Reproduce and Patch Vulnerabilities
Google has open-sourced 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
Google has open-sourced Mantis, a modular toolkit that helps AI coding agents automatically find, test, and fix security vulnerabilities in software. Unlike typical security scanners, Mantis works as a series of slash commands that chain together sequentially, starting with learning the target codebase, then searching for vulnerabilities, filtering out false positives, reproducing bugs in a sandboxed environment, creating patches, and re-testing those patches to measure remaining risk. The toolkit is currently available for local and internal evaluation but not recommended for production use. Mantis addresses a key limitation in automated code security review: most AI tools stop after generating potential findings, whereas Mantis verifies findings through actual sandboxed execution and re-attack, treating reproducibility as the true measure of trust rather than relying solely on model confidence.
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Anthropic releases new Mythos and Fable models amid RSI concerns
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Also reported by The Verge, SiliconANGLE and MarkTechPost
Runway’s Praxis-1 world action model for robotics is now available
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Gemini 4 Carbon is now available
The latest version aims to deliver improved performance while reducing the computational resources and energy required for AI operations.
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Also reported by SiliconANGLE
Introducing AlphaProof Code: a breakthrough in verifying complex software at depth
AlphaProof Code agents wrote three million lines of proofs in Lean to verify Signal’s core encryption.