Credential sharing detection for AI platforms
Credential sharing can be approved collaboration, a shared service account, a relay, or misuse. This guide describes how to detect behavior worth reviewing without collapsing those different situations into a misleading fraud label.
What signals suggest changed credential use?
New infrastructure, unfamiliar operating hours, changed model mix, cross-key source reuse, and altered concurrency can be useful together. None is conclusive alone.
What should an operator do next?
Preserve the relevant time window, attach customer and deployment context, and make the smallest reversible response that contains credible risk. Record why a case opened so another investigator can reproduce the decision.
Where does this approach fail?
Behavioral metadata cannot identify intent or permission by itself. Legitimate launches, failover, automation, and registered relays can resemble misuse. Treat the output as a ranked investigation queue, then resolve authorization with stronger identity and reconciliation evidence.
Frequently asked questions
How do I reduce false positives?
Capture registered gateways, declared deployments, expected regions, and known shared-operation patterns before an alert fires.
Does InferTrail read prompts or responses?
No. The investigation design uses provider-visible metadata and customer context, with content collection governed separately if a customer requires it.
What is the right first action?
Open a verification case, preserve evidence, and check credential state before making a destructive enforcement decision.
Related guides
- LLM API key abuse detection
- AI gateway credential misuse
- Investigating unexplained AI usage spikes
- LiteLLM credential exposure response
- LLM token resale risk
- AI gateway provenance and delegation
- AI spend anomaly detection
- Authorized versus unauthorized AI usage
- LLM gateway security logging
- Credential sharing detection for AI platforms