SkillScan Detector v4
A fine-tuned
Qwen2.5-1.5B-Instruct model for detecting security threats in AI agent skill files.
What it does
Analyzes AI agent skill files (.md) and outputs structured JSON with:
- Verdict: benign or malicious
- Labels: specific attack types detected
- Confidence: 0-1 score
- Reasoning: human-readable explanation citing evidence from the text
Attack types detected
| Class | F1 | Precision | Recall |
|---|
| path_traversal | 0.857 | 0.882 | 0.833 |
| social_engineering | 0.857 | 0.750 | 1.000 |
| prompt_injection | 0.474 | 0.941 | 0.317 |
| code_injection | 0.424 | 0.438 | 0.412 |
| supply_chain | 0.340 | 0.258 | 0.500 |
| evasion | 0.308 | 0.182 | 1.000 |
| data_exfiltration | 0.148 | 0.080 | 1.000 |
Macro F1: 0.487 | Verdict accuracy: 85.2% | Parse failures: 1.2%
Usage with llama-cpp-python
Requirement already satisfied: llama-cpp-python in /opt/homebrew/lib/python3.11/site-packages (0.3.20)
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Download the GGUF model:
[33m⚠️ Warning: 'huggingface-cli download' is deprecated. Use 'hf download' instead.[0m
Model details
- Base model: Qwen/Qwen2.5-1.5B-Instruct (Apache 2.0)
- Fine-tuning: QLoRA (r=32, alpha=64) on 20,035 teacher-distilled examples
- Teachers: Claude Sonnet + GPT-4o (structured security analysis)
- Quantization: GGUF Q4_K_M (935 MB)
- Inference: CPU-only via llama.cpp, ~2-4s per file
- License: Apache 2.0
Files
| File | Size | Description |
|---|
| 935 MB | Quantized model for CPU inference |
| 3.1 GB | Full FP16 weights |
Part of SkillScan
This model is used by
skillscan-security, the open-source CLI scanner for AI agent skill files.
Requirement already satisfied: skillscan-security[ml] in /opt/homebrew/lib/python3.11/site-packages (0.8.0)
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intel refresh updated=9 skipped=0 errors=0
Scan failed: Target does not exist: path/to/skill
