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Vulnerabilities spanning multiple functions can be detected more accurately with VulAgentRL, which verifies evidence through a novel Code Property Graph approach.
LLM agents are not just chatbots: they can find hundreds of real-world software vulnerabilities that traditional tools miss.
Multimodal LLMs can reconstruct and analyze cyber-physical system architectures from fragmented documentation, enabling more accurate security assessments.
Security patch detectors trained on standard vulnerability databases are practically useless in the real world, losing up to 90% F1-score when deployed on in-the-wild data.
