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Security: dreamfast/ungguf

Security

SECURITY.md

Security Policy

Reporting vulnerabilities

If you discover a security vulnerability in ungguf, please report it by opening a GitHub issue with the security label. For sensitive vulnerabilities, contact the maintainers directly.

Known security considerations

Binary GGUF file parsing

ungguf reads and parses binary GGUF files using the gguf Python library. While the library is well-maintained, parsing untrusted binary files always carries inherent risk. Only convert GGUF files from sources you trust.

trust_remote_code=True in vLLM

The vllm_sanity.py inference checker uses trust_remote_code=True when loading models with vLLM. This is required for many HuggingFace models but allows arbitrary code execution during model loading. Only run sanity checks against models from trusted sources.

Docker GPU access

All conversion and inference runs inside Docker containers with NVIDIA GPU access (--runtime=nvidia). Ensure your Docker installation is properly configured and that GPU access is restricted to trusted users.

No cryptographic verification of GGUF files

ungguf does not verify the integrity or authenticity of GGUF input files. A corrupted or maliciously modified GGUF file could produce incorrect safetensors output. Always verify conversion results using the built-in bit-exact verifier.

There aren't any published security advisories