LLM token resale risk
Token resale turns a provider account, gateway route, or purchased allowance into downstream metered access. This guide explains the observable risk signals, the limits of those signals, and why pricing or shared infrastructure alone cannot establish the source of supply.
What can a provider observe about resale?
It may observe changed rate, timing, model mix, and infrastructure reuse. A relay can collapse many downstream users into one upstream source, reducing attribution.
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
Does a low price prove stolen credentials?
No. Retail pricing alone cannot establish upstream provenance, authorization, or fraud.
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