What this error means

LiteLLM rate limit error message body leaks full SHA-256 token hash on 429 responses is a LiteLLM failure pattern reported for developers trying to fix litellm 429 error response leaking token hash in rate limit error message. Based on the imported evidence, treat this as a tool-specific troubleshooting page rather than a generic API error.

Why this happens

GitHub issue #27884 reports that when the parallel request limiter returns a 429, the full SHA-256 hash of the API token is included in the error message body. This leaks sensitive token information to logs, monitoring, and potentially end users.

Common causes

  • When LiteLLM's parallel request limiter returns a 429 response, the error message body contains the full SHA-256 hash of the API token. This is a credential leak that exposes sensitive token information in logs, error outputs, and monitoring systems. Enterprise users relying on LiteLLM proxy for key management are affected.
  • GitHub issue #27884 reports that when the parallel request limiter returns a 429, the full SHA-256 hash of the API token is included in the error message body. This leaks sensitive token information to logs, monitoring, and potentially end users.

Quick fixes

  1. Confirm the exact error signature matches LiteLLM rate limit error message body leaks full SHA-256 token hash on 429 responses.
  2. Check the LiteLLM account, local tool state, and provider configuration involved in the failing workflow.
  3. Reduce request pressure, check quota or plan limits, and retry with backoff instead of immediate repeated requests.

Platform/tool-specific checks

  • Verify the command, editor, extension, or API client that produced the error.
  • Compare local settings with CI, deployment, or editor-level settings when the error appears in only one environment.
  • Avoid deleting credentials, local model data, or project settings until the failing scope is clear.

Step-by-step troubleshooting

  1. Capture the exact error message and the command, editor action, or request that triggered it.
  2. Check whether the failure is account/auth, quota/rate, model/provider, local runtime, or deployment configuration.
  3. Review the source evidence below and compare it with your environment.
  4. Apply one change at a time and rerun the smallest failing action.
  5. Keep the working fix documented for the team or deployment environment.

How to prevent it

  • Keep provider/tool configuration documented.
  • Record non-secret diagnostics such as tool version, provider name, model name, and command path.
  • Add a lightweight check before CI or production workflows depend on the tool.