Error handling in an AI application carries a specific risk conventional error handling guidance addresses but AI-specific implementations sometimes miss: error messages flowing back into a model's context, or forward to an end user, can leak internal information a well-designed error handling approach should keep contained, compounding a functional error into a separate information disclosure issue.
Never surface raw internal error detail to the model or the end user
A raw exception message, stack trace, or internal error detail should never flow directly into a model's context for a subsequent turn, nor directly to an end user, since these often carry internal system detail, file paths, internal service names, sometimes fragments of data being processed, that hands useful reconnaissance information to an attacker probing the system, or simply confuses a legitimate user with detail irrelevant to their actual situation. Translate internal errors into a generic, safe message for anything reaching the model or the user, while preserving the full detail separately for internal logging.
Fail safely for any error affecting a security-relevant decision
When an error occurs during a security-relevant operation, an authorization check, an approval workflow, a rate limit evaluation, the system should fail toward denying or holding the action, not toward allowing it by default because the check that would have denied it hit an error instead. This connects directly to the fail-safe design running throughout this content series, and it needs to be implemented explicitly in error handling code, since the default behavior of many programming patterns, an unhandled exception that simply lets execution continue past a failed check, can silently produce the less safe outcome if nothing specifically guards against it.
Distinguish user-facing errors from security-relevant errors in logging
Log errors with enough context to distinguish a routine, expected error condition, a user submitting invalid input, from a security-relevant one, a failed authorization check, an attempted action outside policy. This distinction matters for monitoring, covered in abuse detection guidance elsewhere, since a spike in security-relevant errors specifically deserves different attention than a spike in routine user input errors, and mixing both categories into one undifferentiated error log makes that distinction harder to draw during monitoring or investigation.
Avoid error messages that reveal what a security check specifically evaluated
Even a sanitized, generic error message can leak useful information to an attacker if it's specific enough to reveal what a security check evaluated: an error message saying "amount exceeds your approval limit of exactly this number" tells an attacker precisely what threshold to stay under on a subsequent attempt. Prefer more general messages, "this action requires additional approval," that communicate the outcome without revealing the specific threshold or logic an attacker could use to refine a future attempt.
Handle model provider errors and timeouts explicitly
Beyond your own application's error conditions, handle errors and timeouts from the model provider itself explicitly: a timeout, a rate limit response from the provider, an unexpected response format should each have defined handling that fails safely, rather than letting an unhandled provider-side error propagate in a way that might bypass validation or authorization logic that assumed a normal, successful model response.
Test error paths deliberately, not only happy-path scenarios
Following testing guidance covered elsewhere, deliberately test error conditions: simulate a security check throwing an unexpected exception, simulate a model provider timeout, and confirm the system's actual behavior in each case matches the intended fail-safe design. Error paths are disproportionately likely to hide unintended security gaps precisely because they get less routine testing attention than the primary success path.
Avoid retry logic that silently bypasses a failed security check
When implementing retry logic for transient failures, make sure retries re-execute the full security-relevant check rather than caching or assuming a previous check's result still holds, since retrying only the underlying operation while skipping a previously-attempted but inconclusive security check can let an action execute without ever having actually passed that check successfully.
Where TELEON fits
TELEON's policy enforcement, applied through the gateway or supported middleware, is designed to fail toward denying an action when a policy decision cannot be reached, providing the fail-safe backstop for the specific enforcement point it covers, which application-level error handling should complement rather than assume covers every error scenario in the surrounding system.
Designing the application's own error message sanitization, logging categorization, and error path testing remains implementation work specific to each feature's own error handling code.
A short checklist
- Never surface raw internal error detail to the model or end user.
- Fail toward denying or holding any security-relevant action when an
error occurs.
- Distinguish routine errors from security-relevant errors in logging.
- Avoid error messages specific enough to reveal exact security check
thresholds.
- Handle model provider errors and timeouts with explicit, safe fallback
behavior.
- Deliberately test error paths, not only happy-path scenarios.
- Ensure retry logic re-executes security checks rather than bypassing
them.
Secure error handling in AI applications requires the same discipline applied to any application's error handling, extended specifically to avoid leaking internal detail through model-facing or user-facing error messages and to ensure failures default toward the safer outcome for any security-relevant decision.