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      AI Achieves Over 90% Success Rate in Vulnerability Detection, UC Berkeley Study Shows

      Recent results from CyberGym, a benchmark developed by researchers at UC Berkeley, indicate that artificial intelligence (AI) agents have significantly improved their ability to detect software vulnerabilities. The leading AI agent, a Sangfor AI Agent utilizing DeepSeek-V4-Flash, achieved a success rate of 93.2%, a remarkable increase from the 10-30% success rates observed just a year ago.

      CyberGym evaluates AI performance using 1,507 real-world vulnerability instances from 188 open-source software projects, primarily sourced from Google's OSS-Fuzz corpus. The AI agents are tasked with generating proof-of-concept tests that successfully trigger vulnerabilities in unpatched code while ensuring that patched versions remain unaffected. This objective scoring system ensures that only valid detections are credited, with no partial marks awarded.

      In addition to reproducing known vulnerabilities, the AI agents have collectively identified 34 new zero-day vulnerabilities and flagged 18 incomplete patches. These findings highlight the potential of AI not only to enhance security measures but also to expose weaknesses in widely used software that have previously undergone extensive human scrutiny. As AI capabilities advance, the dual-use nature of this technology raises concerns about its potential for exploitation in the wrong hands.

      © 2026 KLEA News. All Rights Reserved. This article is provided for informational purposes only. It is not offered or intended to be used as legal, tax, investment, financial, or other advice.

      Source: KLEA News

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