Security · 2h ago
LLMs Struggle to Prioritize Vulnerabilities, Raising False Positives
Large language models show high false-positive rates in vulnerability detection and fail to consider scan context, according to a new analysis. This creates extra work for application security professionals who must manually verify alerts. The findings highlight current limitations of AI in security tooling.
Meridian48 take
The hype around LLMs for security often overshadows their practical shortcomings; this report grounds expectations in real-world performance.
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Using LLMs to Find and Prioritize Vulnerabilities Is No Easy Task →
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