ModelCypher handles sensitive model weights and potential training data. Security is paramount.

• Never commit API keys. Use .env files.
• ModelCypher respects HF_TOKEN environment variables for Hugging Face authentication.
• Do not hardcode tokens in Python scripts.

• Prefer safetensors: We default to safetensors for all model saving/loading to avoid pickle execution vulnerabilities.
• Pickle Warning: Some workflows (e.g., LoRA adapter merge) may use torch.load for .bin/.pt files, which relies on pickle.
• Untrusted Models: Do not load .bin/.pt weights from untrusted sources.

ModelCypher disables trust_remote_code by default to avoid executing model-supplied code.

export MC_TRUST_REMOTE_CODE=1

Only enable this for models you trust.

• TLS 1.2+: All network connections (HF Hub, MLflow) default to strict TLS.
• No Telemetry: ModelCypher does not phone home. All "training dynamics" data is stored locally or sent only to your configured MLflow server.

If you discover a security vulnerability, please do not open a public issue. Email jason@ethyros.ai.