peft-fine-tuning
Pass
Audited by Gen Agent Trust Hub on Sep 15, 2026
Risk Level: SAFEINDIRECT_PROMPT_INJECTION
Full Analysis
- [SAFE]: The skill provides legitimate machine learning development instructions and code snippets using well-known open-source libraries from official registries. No malicious patterns, obfuscation, or unauthorized data access were detected.
- [INDIRECT_PROMPT_INJECTION]: The skill includes functionality to ingest external datasets from Hugging Face for model fine-tuning. While this constitutes a potential surface for indirect prompt injection, it is the intended primary purpose of the skill and is documented using standard industry practices.
- Ingestion points: Training data is loaded from external repositories using the
load_datasetfunction inSKILL.md. - Boundary markers: The
tokenizefunction inSKILL.mdinterpolates dataset content into prompt templates without explicit boundary delimiters or instructions to ignore embedded commands. - Capability inventory: The skill facilitates local model saving via
model.save_pretrainedand remote model uploading to Hugging Face viamerged_model.push_to_hub. - Sanitization: External data is processed without explicit sanitization or filtering before tokenization.
Audit Metadata