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brucemao3/safeskillscan-readyclaw

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11 stars 품질 54 트렌드 8MIT

Multi-stage security scanner for AI agent skill files — regex, AST, semantic embeddings, and LLM audit in one pipeline.

개요

Multi-stage security scanner for AI Agent skill files (SKILL.md). Designed to detect malicious patterns in community-contributed skill packages before they are installed into AI coding assistants (Claude Code, OpenClaw, etc.). Each stage filters sequentially. Skills rejected at an earlier stage are not re-evaluated by later stages. Place skill files in data/files/ with one directory per skill: Or provide a SQLite database at data/skills.db with a skill_content table containing slug and skill_md columns. For large-scale scans (10k+ skills), use the batch runners: Each job gets its own database and report under jobs/{job_id}/. Detection rules are defined in YAML under skill_scanner/rules/:

README

원문 README의 일부입니다. 원문 보기.

Developed by https://x.com/SlimeVerse_

Preconfigured OpenClaw website:https://www.readyclaw.org/en

SafeSkillScan

Multi-stage security scanner for AI Agent skill files (SKILL.md).

Designed to detect malicious patterns in community-contributed skill packages before they are installed into AI coding assistants (Claude Code, OpenClaw, etc.).

Pipeline Overview

Stage 0: Intake       →  Normalize, dedup, encoding/size checks
Stage 1: Rules + AST  →  Regex rules, IOC domain matching, AST analysis
Stage 2: Semantic      →  Embedding-based coherence, structural entropy
Stage 3: LLM Audit    →  Multi-model voting (Claude, GPT, Gemini)

Each stage filters sequentially. Skills rejected at an earlier stage are not re-evaluated by later stages.

Threat Classes

Class Name
1 Credential Theft
2 Data Exfiltration
3 Home Directory Destruction
4 Download & Execute
5 Prompt Injection / Agent Hijack
6 Crypto Wallet Fraud
7 Surveillance / Keylogging

Quick Start

1. Install

python -m venv .venv
source .venv/bin/activate
pip install -r requirements-scanner.txt

2. Configure

cp .env.example .env
# Edit .env and add your API keys (required for Stage 3 only)

3. Prepare Data

Place skill files in data/files/ with one directory per skill:

data/
└── files/
    ├── my-skill/
    │   └── SKILL.md
    ├── another-skill/
    │   └── SKILL.md
    └── ...

Or provide a SQLite database at data/skills.db with a skill_content table containing slug and skill_md columns.

4. Run

Single skill scan:

python -m skill_scanner scan --slug my-skill

Full scan (Stage 0-2, no API keys needed):

python -m skill_scanner scan --all --stage 2

Full pipeline with LLM audit:

python -m skill_scanner scan --all

View results:

python -m skill_scanner report --summary
python -m skill_scanner report --findings --severity CRITICAL
python -m skill_scanner report --export-csv results.csv

Batch Scanning

For large-scale scans (10k+ skills), use the batch runners:

# Stage 0-2 (deterministic + semantic)
python batch_scan.py

# Stage 3 (LLM audit, requires API keys)
python batch_scan_s3.py

Job System

Scan ad-hoc skill batches in isolation:

# Scan a .tar.gz batch
python job_scan.py input.tar.gz --name mybatch --stages 0,1,2,3 --concurrency 5

# List all jobs
python job_report.py --list

# Regenerate report
python job_report.py --job-id mybatch-abc123

Each job gets its own database and report under jobs/{job_id}/.

Rule Files

Detection rules are defined in YAML under skill_scanner/rules/:

File Coverage
credential_access.yaml SSH keys, API tokens, browser credentials
data_exfiltration.yaml External POST requests, webhook exfiltration
GitHub에서 전체 README 보기

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