AI can be remarkably effective at discovering a broad range of vulnerabilities—but the real challenge isn’t always detection, It’s precision. Automation has long struggled with false positives, and nowhere is this more evident than in vulnerability scanning. Tools that flag dozens of irrelevant issues often create more work than they save. When AI enters the equation, the stakes grow even higher: models can generalize well, but verifying technical edge cases is a different game entirely.
To ensure accuracy, we developed the concept of validators, automated peer reviewers that confirm each vulnerability XBOW uncovers. Sometimes this process leverages a large language model; in other cases, we build custom programmatic checks. For example, to validate Cross-Site Scripting findings, a headless browser visits the target site to verify that the JavaScript payload was truly executed. (don’t miss Brendan Dolan-Gavitt’s BlackHat presentation on AI agents for Offsec)