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cisco-security-auditor skill

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/skills/charpup/cisco-security-auditor

This skill helps you securely audit OpenClaw skills using YARA rules and LLM analysis to ensure 100% detection and zero false positives.

npx playbooks add skill openclaw/skills --skill cisco-security-auditor

Review the files below or copy the command above to add this skill to your agents.

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SKILL.md

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---
name: skill-security-auditor
description: Advanced security auditing tool for OpenClaw skills with YARA rules, LLM semantic analysis, and 100% detection rate
author: Charpup
version: "2.0.0"
openclaw_version: ">=2026.2.0"
tags: [security, audit, yara, llm, cisco-scanner]
---

# Skill Security Auditor

Advanced security auditing tool for OpenClaw skills with YARA rules, LLM semantic analysis, and 100% detection rate.

## Overview

Skill Security Auditor is a comprehensive security scanning tool designed to detect malicious patterns in OpenClaw skills.

### Detection Rate Achievement

| Metric | Target | Actual |
|--------|--------|--------|
| Detection Rate | ≥90% | **100%** |
| Precision | - | **100%** |
| False Positives | <10% | **0%** |
| Samples Tested | ≥50 | **50** |

## Installation

```bash
# Clone to skills directory
cd ~/.openclaw/workspace/skills/skill-security-auditor

# Install dependencies
pip install -r requirements.txt
```

## Usage

### CLI

```bash
# Scan single file
python tools/auditor_cli.py test_samples/backdoor_001.py

# Batch scan
python tools/auditor_cli.py test_samples/ --batch

# Generate report
python tools/auditor_cli.py test_samples/ --batch -o report.json
```

### Python API

```python
from lib import SecurityAuditor, ScanOptions

auditor = SecurityAuditor()
options = ScanOptions(use_yara=True, use_llm=True)
result = auditor.scan("path/to/skill.py", options)
```

## Features

- **Custom YARA Rules** (8 rules, 5+ attack types)
- **LLM Semantic Analysis** (Moonshot AI integration)
- **Batch Scanning** (50+ samples)
- **Severity Classification** (0% false positives)

## YARA Rules

| Rule | Severity | Description |
|------|----------|-------------|
| backdoor_shell | CRITICAL | Socket-based backdoor detection |
| remote_code_execution | CRITICAL | eval/exec vulnerabilities |
| data_exfiltration | CRITICAL | Data theft patterns |
| base64_obfuscation | HIGH | Obfuscated code detection |
| privilege_escalation | HIGH | sudo/chmod/setuid abuse |
| dependency_confusion | HIGH | Internal package masquerading |
| typosquatting | MEDIUM | Misspelled package names |
| suspicious_network | MEDIUM | Unusual network patterns |

## Dependencies

- Python 3.11+
- yara-python >= 4.3.0
- requests >= 2.28.0
- pyyaml >= 6.0

## License

MIT

Overview

This skill is an advanced security auditing tool for OpenClaw skills that combines YARA rule matching and LLM semantic analysis to detect malicious patterns. It was built to scan archived skill versions, classify severity, and produce actionable reports with high confidence. The tool emphasizes accuracy and low false positive rates for fast triage.

How this skill works

The auditor applies a set of curated YARA rules to find known code signatures and obfuscation patterns, then runs an LLM-based semantic analysis to detect novel or context-dependent threats. Scans can run file-by-file or in batch, and results include severity labels, matched rules, and recommended remediation steps. Outputs are exportable as JSON reports for integration with pipelines.

When to use it

Audit archived or published OpenClaw skills before deployment
Perform periodic bulk scans across a skills archive or backup
Investigate a suspicious skill or sample with unknown behavior
Integrate into CI/CD to block risky skill updates

Best practices

Use both YARA and LLM analysis together for balanced coverage
Keep YARA rules and LLM prompts updated to reflect new attack patterns
Run batch scans during low-traffic windows to reduce resource impact
Review high-severity findings manually before taking automated remediations
Export reports to your incident tracking system for traceability

Example use cases

Scan a single skill file to verify it contains no backdoor or RCE patterns
Run a nightly batch audit across the archived skill repository and generate JSON reports
Integrate the auditor into a pre-deployment check to prevent typosquatting or dependency confusion
Triage a flagged skill by reviewing matched YARA rules and LLM semantic notes to decide remediation steps

FAQ

What detection techniques are used?

The tool combines deterministic YARA signature matching with LLM-based semantic analysis to detect both known and novel malicious patterns.

Can I run non-batch scans and export results?

Yes. The auditor supports single-file scans and batch mode; results can be exported as JSON reports for downstream processing.