NäckenSec Scan

Swedish AI agent security scanner. Fork av Cisco skill-scanner med svensk PII-detektion, Fortnox-medvetenhet och EU AI Act compliance.

# Installera
pip install git+https://github.com/opensverige/nackensec-scan.git

# Skanna en agent (svenska fynd på svenska)
nackensec-scan scan /path/to/agent --lang sv

Vad NäckenSec lägger till

Licensiering

NäckenSec-tilläggen licensieras under AGPL-3.0. Se LICENSE för den dubbla licensstrukturen. Kommersiell licensiering: licensing@opensverige.se

Built on top of Cisco skill-scanner (Apache 2.0) — se THIRD_PARTY.md för fullständig attribution.

Skill Scanner (upstream)

A best-effort security scanner for AI Agent Skills that detects prompt injection, data exfiltration, and malicious code patterns. Combines pattern-based detection (YAML + YARA), LLM-as-a-judge, and behavioral dataflow analysis to maximize detection coverage of probable threats while minimizing false positives.

Important: This scanner provides best-effort detection, not comprehensive or complete coverage. A scan that returns no findings does not guarantee that a skill is free of all threats. See Scope and Limitations below.

Supports OpenAI Codex Skills and Cursor Agent Skills formats following the Agent Skills specification.

Highlights

• Multi-Engine Detection - Static analysis, behavioral dataflow, LLM semantic analysis, and cloud-based scanning for layered, best-effort coverage
• False Positive Filtering - Meta-analyzer significantly reduces noise while preserving detection capability
• CI/CD Ready - SARIF output for GitHub Code Scanning, reusable GitHub Actions workflow, exit codes for build failures
• Pre-commit Hook - Standard pre-commit framework integration to scan skills before every commit
• Extensible - Plugin architecture for custom analyzers

Join the Cisco AI Discord to discuss, share feedback, or connect with the team.

Scope and Limitations

Skill Scanner is a detection tool. It identifies known and probable risk patterns, but it does not certify security.

Key limitations:

• No findings ≠ no risk. A scan that returns "No findings" indicates that no known threat patterns were detected. It does not guarantee that a skill is secure, benign, or free of vulnerabilities.
• Coverage is inherently incomplete. The scanner combines signature-based detection, LLM-based semantic analysis, behavioral dataflow analysis, optional cloud services, and configurable rule packs. While this approach improve coverage, no automated tool can detect every technique, especially novel or zero-day attacks.
• False positives and false negatives can occur. Consensus modes and meta-analysis reduce noise, but no configuration eliminates all incorrect classifications. Tune the scan policy to your risk tolerance.
• Human review remains essential. Automated scanning is one component of a defense-in-depth strategy. High-risk or production deployments should pair scanner results with manual code review and/or threat modeling.

Documentation

Installation

Prerequisites: Python 3.10+ and uv (recommended) or pip

# Using uv (recommended)
uv pip install cisco-ai-skill-scanner

# Using pip
pip install cisco-ai-skill-scanner

# AWS Bedrock support
pip install cisco-ai-skill-scanner[bedrock]

# Google Vertex AI support
pip install cisco-ai-skill-scanner[vertex]

# Azure OpenAI support
pip install cisco-ai-skill-scanner[azure]

# All cloud providers
pip install cisco-ai-skill-scanner[all]

Quick Start

Environment Setup (Optional)

# For LLM analyzer and Meta-analyzer
export SKILL_SCANNER_LLM_API_KEY="your_api_key"
export SKILL_SCANNER_LLM_MODEL="claude-3-5-sonnet-20241022"

# For VirusTotal binary scanning
export VIRUSTOTAL_API_KEY="your_virustotal_api_key"

# For Cisco AI Defense
export AI_DEFENSE_API_KEY="your_aidefense_api_key"

Interactive Wizard

Not sure which flags to use? Run skill-scanner with no arguments to launch the interactive wizard:

skill-scanner

The wizard walks you through selecting a scan target, analyzers, policy, and output format, then shows the assembled command before running it. Great for learning the CLI.

CLI Usage

# Scan a single skill (core analyzers: static + bytecode + pipeline)
skill-scanner scan /path/to/skill

# Scan with behavioral analyzer (dataflow analysis)
skill-scanner scan /path/to/skill --use-behavioral

# Scan with all engines
skill-scanner scan /path/to/skill --use-behavioral --use-llm --use-aidefense

# Scan with meta-analyzer for false positive filtering
skill-scanner scan /path/to/skill --use-llm --enable-meta

# Scan with trigger analyzer for vague description checks
skill-scanner scan /path/to/skill --use-trigger

# Run LLM analyzer multiple times and keep majority-agreed findings
skill-scanner scan /path/to/skill --use-llm --llm-consensus-runs 3

# Scan multiple skills recursively
skill-scanner scan-all /path/to/skills --recursive --use-behavioral

# Scan multiple skills with cross-skill overlap detection
skill-scanner scan-all /path/to/skills --recursive --check-overlap

# Lenient mode: tolerate malformed skills instead of failing
skill-scanner scan /path/to/skill --lenient
skill-scanner scan-all /path/to/skills --recursive --lenient

# CI/CD: Fail build if threats found
skill-scanner scan-all ./skills --fail-on-severity high --format sarif --output results.sarif

# Generate interactive HTML report with attack correlation groups
skill-scanner scan /path/to/skill --use-llm --enable-meta --format html --output report.html

# Use custom YARA rules
skill-scanner scan /path/to/skill --custom-rules /path/to/my-rules/

# Use custom taxonomy + threat mapping profiles (JSON/YAML)
skill-scanner scan /path/to/skill --taxonomy /path/to/taxonomy.json --threat-mapping /path/to/threat_mapping.json

# VirusTotal hash scan with optional unknown-file uploads
skill-scanner scan /path/to/skill --use-virustotal --vt-upload-files

# Use a scan policy preset (strict, balanced, permissive)
skill-scanner scan /path/to/skill --policy strict

# Use a custom org policy file
skill-scanner scan /path/to/skill --policy my_org_policy.yaml

# Generate a policy file to customise
skill-scanner generate-policy -o my_org_policy.yaml

# Interactive policy configurator (TUI)
skill-scanner configure-policy

LLM provider note: --llm-provider currently accepts anthropic or openai. For Bedrock, Vertex, Azure, Gemini, and other LiteLLM backends, set provider-specific model strings and environment variables (see LLM Analyzer docs).

Python SDK

from skill_scanner import SkillScanner
from skill_scanner.core.analyzers import BehavioralAnalyzer

# Create scanner with analyzers
scanner = SkillScanner(analyzers=[
    BehavioralAnalyzer(),
])

# Scan a skill
result = scanner.scan_skill("/path/to/skill")

print(f"Findings: {len(result.findings)}")
print(f"Max severity: {result.max_severity}")

# Note: is_safe indicates no HIGH/CRITICAL findings were detected.
# It does not guarantee the skill is free of all risk.
if not result.is_safe:
    print("Issues detected -- review findings before deployment")

Security Analyzers

CLI Options

Example Output

$ skill-scanner scan ./my-skill --use-behavioral

============================================================
Skill: my-skill
============================================================
Status: [OK] No findings
Max Severity: NONE
Total Findings: 0
Scan Duration: 0.15s

Note: "No findings" means the scanner did not detect any known threat patterns -- it is not a guarantee that the skill is free of all risk. See Scope and Limitations.

GitHub Actions

Scan skills automatically on every push or PR using the reusable workflow:

# .github/workflows/scan-skills.yml
name: Scan Skills
on:
  pull_request:
    paths: [".cursor/skills/**"]
jobs:
  scan:
    uses: cisco-ai-defense/skill-scanner/.github/workflows/scan-skills.yml@main
    with:
      skill_path: .cursor/skills
    permissions:
      security-events: write
      contents: read

Results appear as inline annotations in PRs via GitHub Code Scanning. See the full guide for LLM integration, secret configuration, and branch protection setup.

Pre-commit Hook

Scan skills before every commit using the pre-commit framework:

# .pre-commit-config.yaml
repos:
  - repo: https://github.com/cisco-ai-defense/skill-scanner
    rev: v1.0.0  # use the latest release tag
    hooks:
      - id: skill-scanner

Or install the built-in hook directly:

skill-scanner-pre-commit install

The hook automatically detects which skill directories have staged changes and only scans those, keeping commit times fast. Use --all to scan everything.

Contributing

We welcome contributions! Please see CONTRIBUTING.md for guidelines.

License

Apache 2.0 - See LICENSE for details.

Copyright 2026 Cisco Systems, Inc. and its affiliates

GitHub • Discord • PyPI