bawbel-scanner 0.2.0

Agentic AI component security scanner — detects AVE vulnerabilities

Bawbel Scanner

Agentic AI component security scanner — detects AVE vulnerabilities before they reach production.

Bawbel Scanner scans agentic AI components — SKILL.md files, MCP server manifests, system prompts, and agent plugins — for security vulnerabilities mapped to the AVE (Agentic Vulnerability Enumeration) standard.

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Install

pip install bawbel-scanner

With optional engines:

pip install "bawbel-scanner[yara]"      # YARA rules
pip install "bawbel-scanner[semgrep]"   # Semgrep rules
pip install "bawbel-scanner[llm]"       # LLM Stage 2 (any provider via LiteLLM)
pip install "bawbel-scanner[all]"       # everything

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Quick Start

# Check version and active detection engines
bawbel version
bawbel --version

# Scan a SKILL.md file
bawbel scan ./my-skill.md

# Scan a directory
bawbel scan ./skills/ --recursive

# Full report with remediation instructions
bawbel report ./my-skill.md

# Fail CI on high severity
bawbel scan ./skills/ --fail-on-severity high

# Watch for changes and re-scan automatically
bawbel scan ./skills/ --watch

# Output formats
bawbel scan ./skills/ --format json     # JSON for tooling
bawbel scan ./skills/ --format sarif    # SARIF for GitHub Security tab

Example output:

Bawbel Scanner v0.2.0

Scanning:  malicious-skill.md
Type:      skill

FINDINGS
🔴  CRITICAL  AVE-2026-00001  External instruction fetch detected
   Line 7 · pattern engine
   OWASP: ASI01, ASI08

🟠  HIGH      —               Goal override instruction detected
   Line 17 · pattern engine
   OWASP: ASI01, ASI08

SUMMARY
Risk score:   9.4 / 10  CRITICAL
Findings:     2
Scan time:    5ms

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Use as a Library

from scanner import scan

result = scan("/path/to/skill.md")

if result.is_clean:
    print("Clean")
else:
    for finding in result.findings:
        print(f"[{finding.severity.value}] {finding.title}")
    print(f"Risk score: {result.risk_score:.1f} / 10")

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CI/CD Integration

GitHub Actions

- name: Bawbel scan
  run: |
    pip install bawbel-scanner
    bawbel scan ./skills/ --recursive --fail-on-severity high

Pre-commit

Add to your .pre-commit-config.yaml:

repos:
  - repo: local
    hooks:
      - id: bawbel-scan
        name: Bawbel Scanner — agentic AI component security scan
        entry: bawbel scan
        language: system        # uses your venv where bawbel-scanner is installed
        pass_filenames: true
        types: [markdown]       # scans .md files on every commit
        args: ["--fail-on-severity", "high"]

Then install:

pip install bawbel-scanner
pre-commit install

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Detection Stages

Stage	Engine	Requires	Coverage
1a	Pattern matching	Nothing (stdlib)	15 rules, always runs
1b	YARA	yara-python	Binary + text pattern matching
1c	Semgrep	semgrep	Structural pattern matching
2	LLM semantic	pip install "bawbel-scanner[llm]" + API key	Nuanced injection, obfuscated payloads — any LiteLLM provider
3	Behavioral	Docker + eBPF	Runtime behaviour (v1.0)

15 built-in pattern rules cover: goal override, jailbreak, hidden instructions, external fetch, tool call injection, permission escalation, credential exfiltration, PII exfiltration, shell injection, destructive commands, cryptocurrency drain, trust escalation, persistence, MCP tool poisoning, system prompt extraction.

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AVE Standard

Every finding maps to an AVE record — the CVE equivalent for agentic AI components.

• Browse records: github.com/bawbel/bawbel-ave
• Report a new vulnerability: open an issue on bawbel-ave

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Documentation

bawbel.io/docs · Getting Started · API Reference

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License

Apache 2.0 — see LICENSE.

Built by Bawbel · [e-mail]