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Google's open-sourced Mantis framework addresses critical limitations in current AI security tools by using agentic workflows and sandboxed reproduction to drastically reduce false positives and hallucinations in vulnerability scanning. By grounding findings in repository history and architectural context rather than relying solely on LLM judgment, the system achieves a significant improvement in true-positive rates for automated code analysis. This development represents a substantive advancement in making AI-driven security auditing reliable enough for production environments.
Sources coveringInfoQ, Databricks
What this isA model's reading of the article and related coverage, written for aiqbench. Not the publisher's words.

Google Mantis: an Agentic Vulnerability Scanning Harness for Reducing False Positives

InfoQ3 min read

Google has open-sourced Mantis, an AI-agent framework designed to automate the software vulnerability lifecycle, from identifying and validating vulnerabilities to reproducing and fixing them. Google says it developed Mantis to address the high rate of false positives and hallucinated vulnerabilities produced by conventional AI-powered code scanning. By Sergio De Simone

Google has open-sourced Mantis, an AI-agent framework designed to automate the software vulnerability lifecycle, from identifying and validating vulnerabilities to reproducing and fixing them. Google says it developed Mantis to address the high rate of false positives and hallucinated vulnerabilities produced by conventional AI-powered code scanning. While sloppiness in AI code scanning frequently leads to hallucinated bugs…

Excerpt shown under the publisher's RSS terms · 57 words · full text and figures are on the publisher's site

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