AI Can Find Bugs, But Human Knowledge Still Proves Them
–>
Home What attacker-controlled input, identity, or state was required? What security boundary was crossed, such as authentication, authorization, tenancy, trust, privilege, or memory safety? What exact steps reproduce the behavior in the target environment? What is the demonstrated impact, not just the theoretical worst case? What evidence shows that the issue is reachable and relevant in the deployed configuration? What would a fix need to change, and how can the team confirm that the fix works? This kind of checklist helps keep AI in the right role. It can help produce candidates, suggest test ideas, and speed up reproduction. It should not be allowed to skip the step where a human verifies the claim against reality. One of the underappreciated realities of AI security platforms is that human validation remains deeply important behind the scenes. That should not be surprising. Offensive security has always required judgment, and judgment is especially important when findings become consequential. The person reviewing the evidence has to decide whether the exploit path is realistic, whether the environment matters, whether the issue is isolated or chainable, and whether the severity claim is justified.
Published: July 16, 2026 10:10 am
Source: The Hacker News — Read original