Research
Securing AI Agents: Lessons From Real-World Deployment
Publication date unknown · Discovered uber.com
Page published
Publication date unknown · First observed: 7 Nov 2026
Coverage timeline
Single-source research — one report is available.
Why it matters
Uber’s framework gives defenders architectural patterns for checking whether authorized agent actions actually match user intent and session context, rather than relying on access controls alone.
Uber describes an agent-security framework combining provisioning-time validation, context-aware runtime enforcement, and continuous assurance. Its runtime approach uses prompt provenance and evidence chaining to connect privileged actions to user requests, helping defend against indirect prompt injection, goal hijacking, and unintended agent behavior.