Unauthenticated or billable AI endpoint

Check method

Confirms a stranger can call the AI endpoint, and whether the call costs money.

Possible evidence type

Live observation when the check is compatible and authorized. This description does not mean your platform has been tested.

Access and limitations

Website address. Actual results and checks not run appear in your report. A check with no finding does not guarantee the absence of risk.

Concrete scenario

An anonymous caller can invoke a route that spends the platform’s model budget.

What to inspect

Confirm the endpoint actually performs model work, what identity is required and whether authorized limits apply.

Remediation approach

Authenticate billable calls and enforce quotas on the server; a browser token or client counter alone is insufficient.

Separate instructions from untrusted content

An LLM may process user text, retrieved documents, tool output and application instructions in the same context. Content that looks like an instruction can therefore influence an answer. A suspicious answer is not automatically a data leak or an action: record exactly what changed and whether sensitive data or tools were involved.

1. List input sources: user messages, documents, retrieval, history and tool results.
2. Identify which instructions and secrets are placed in the model context.
3. Distinguish a changed answer from a demonstrated confidential disclosure or unauthorized tool call.

Build layered protections

A stronger prompt is not a complete security boundary. Enforce tool permissions and data access outside the model, minimize what it can see, and validate its output before using it as executable code, HTML or a trusted decision.

1. Remove credentials and unnecessary confidential material from context.
2. Apply identity-scoped retrieval and allowlisted tool operations.
3. Require approval for consequential actions and retain regression tests for known bypasses.

References

OWASP LLM10 · OWASP ASI02