We give AI a simple job: try to break Didit

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Didit describes its AI red-teaming program as a continuous staging-environment process in which AI agents with source-code access attempt to defeat identity-verification workflows using forged documents, artificial camera feeds, and automated user journeys. The company stresses that agent-reported bypasses are not treated as confirmed vulnerabilities until engineers reproduce and investigate the underlying evidence, implement fixes where needed, rerun tests, and assess effects on legitimate users. These exercises also provide examples of automation behavior, such as timing, retries, sequences, and device context, that can inform bot detection without treating any single unusual signal as proof of fraud. Didit argues that the same behavioral analysis can reveal usability obstacles including unclear instructions, permission problems, poor lighting, and damaged documents, supporting a balance between stronger fraud controls and accessible verification. Emphasizing the sensitivity of identity data and the possibility that any provider can be breached, the company calls on industry technical and security leaders to assign ownership, resources, and ongoing attention to security across verification flows, data systems, access controls, and incident response.

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