---
license: apache-2.0
language:
- en
tags:
- security
- skillscan
- prompt-injection
- skill-file-analysis
- llama-cpp
- gguf
base_model: Qwen/Qwen2.5-1.5B-Instruct
pipeline_tag: text-generation
---

# SkillScan Detector v4

A fine-tuned [Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/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

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Download the GGUF model:
⚠️  Warning: 'huggingface-cli download' is deprecated. Use 'hf download' instead.



## 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](https://github.com/kurtpayne/skillscan-security), the open-source CLI scanner for AI agent skill files.

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intel refresh updated=9 skipped=0 errors=0
Scan failed: Target does not exist: path/to/skill