What this error means

malicious litellm_init.pth in litellm 1.82.8 — credential stealer is a LiteLLM failure pattern reported for developers trying to fix or investigate malicious code execution from litellm package on python startup. Based on the imported evidence, treat this as a tool-specific troubleshooting page rather than a generic API error.

Why this happens

The litellm_init.pth file (34,628 bytes) uses double base64-encoded payload to collect system info, environment variables, SSH keys, git credentials, AWS/GCP/Azure/K8s/Docker credentials, crypto wallets, and SSL/TLS private keys. Auto-executes on Python startup — no 'import litellm' needed. Reported 2026-03-24, 487 comments, issue closed.

Common causes

  • The litellm==1.82.8 PyPI package contains a malicious .pth file that auto-executes a credential-stealing script on every Python interpreter start. Developers who installed this version had their API keys, SSH keys, AWS/GCP/Azure credentials, Kubernetes configs, and crypto wallets exfiltrated. Massive developer impact with 487+ comments on the tracking issue.
  • The litellm_init.pth file (34,628 bytes) uses double base64-encoded payload to collect system info, environment variables, SSH keys, git credentials, AWS/GCP/Azure/K8s/Docker credentials, crypto wallets, and SSL/TLS private keys. Auto-executes on Python startup — no 'import litellm' needed. Reported 2026-03-24, 487 comments, issue closed.

Quick fixes

  1. Confirm the exact error signature matches malicious litellm_init.pth in litellm 1.82.8 — credential stealer.
  2. Check the LiteLLM account, local tool state, and provider configuration involved in the failing workflow.
  3. Compare the failing environment with a known working setup, then change one configuration value at a time.

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.