Threat
Workflow Identity Hijacking: The Silent Backdoor in AI Workflows
Publication date not yet evaluated · Added noma.security
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Why it matters
Workflow identity hijacking shows that agentic AI pipelines can be tricked into executing privileged actions on behalf of unauthenticated outsiders, exposing enterprise data through everyday input channels that defenders rarely treat as attack surface.
Researchers at Noma Labs disclosed 'workflow identity hijacking,' an authorization design flaw in enterprise AI pipelines that lets attackers bypass standard security controls by sending a benign-looking request through an unauthenticated entry point such as a support inbox, GitHub issue, web form, or shared document. The flaw stems from AI pipelines decoupling the identity and permissions of the user who triggers a workflow from the elevated permissions used to execute it, so an unauthorized requester's input is read, interpreted, and acted on as designed.