fine-tuning-expert
Fail
Audited by Gen Agent Trust Hub on Sep 15, 2026
Risk Level: CRITICALDYNAMIC_EXECUTIONINDIRECT_PROMPT_INJECTIONCOMMAND_EXECUTIONEXTERNAL_DOWNLOADS
Full Analysis
- [DYNAMIC_EXECUTION]: The skill documentation includes examples using
trust_remote_code=Truewhen loading models from Hugging Face (e.g., inreferences/deployment-optimization.md). This allows for the execution of arbitrary code defined in the model's repository. - [INDIRECT_PROMPT_INJECTION]: The skill is designed to ingest and process external datasets (JSONL, Parquet) for fine-tuning. These datasets represent an attack surface for indirect prompt injection if the source data is untrusted.
- Ingestion points: Dataset loading in
SKILL.mdandreferences/dataset-preparation.md. - Boundary markers: The provided examples do not use explicit delimiters or instructions to ignore embedded prompts in the training data.
- Capability inventory: The skill has access to file system writes (checkpoints), network operations (Hugging Face Hub), and subprocess execution.
- Sanitization: A quality filter is provided in
references/dataset-preparation.md, but it focuses on content suitability rather than security sanitization. - [COMMAND_EXECUTION]: The
export_to_gguffunction inreferences/deployment-optimization.mdusessubprocess.runto execute Python scripts and binaries for model quantization, with paths derived from theLLAMA_CPP_PATHenvironment variable. - [EXTERNAL_DOWNLOADS]: The skill demonstrates fetching models and datasets from well-known repositories like the Hugging Face Hub (e.g.,
meta-llama/Llama-3-8B), which is a standard practice in the field.
Recommendations
- CRITICAL: 1 file(s) identified as malware by FileRep - DO NOT USE
- Contains 2 malicious URL(s) - DO NOT USE
Audit Metadata