arXiv Machine Learning June 18, 2026 By Nahum Korda, Gadi Evron

OpenAnt: LLM-Powered Vulnerability Discovery Through Code Decomposition, Adversarial Verification, and Dynamic Testing

Read the original on arXiv Machine Learning →

arXiv:2606. 19149v1 Announce Type: cross Abstract: Automated vulnerability discovery in large codebases remains challenging: traditional static analysis produces high false-positive rates, while dynamic approaches such as fuzzing require substantial infrastructure and often target narrow classes of bugs.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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