bawbel-scanner 1.0.1

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]"      # Stage 1b — YARA rules (15 rules)
pip install "bawbel-scanner[semgrep]"   # Stage 1c — Semgrep rules (15 rules)
pip install "bawbel-scanner[llm]"       # Stage 2  — LLM semantic analysis
pip install "bawbel-scanner[magika]"    # Stage 0  — file type verification (Google Magika)
pip install "bawbel-scanner[watch]"     # Watch mode — re-scan on file change
pip install "bawbel-scanner[all]"       # Everything: yara + semgrep + llm + magika + watch

Stage 3 (behavioral sandbox) requires Docker — see Stage 3.

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Quick Start

cp .env.example .env   # copy env template, fill in your keys
source .env

bawbel version                                    # show version + active engines
bawbel scan ./my-skill.md                         # scan a file
bawbel scan ./skills/ --recursive                 # scan a directory
bawbel report ./my-skill.md                       # full remediation report
bawbel scan ./skills/ --fail-on-severity high     # exit 2 on HIGH+
bawbel scan ./skills/ --watch                     # re-scan on every change
bawbel scan ./skills/ --format json               # JSON for tooling
bawbel scan ./skills/ --format sarif              # SARIF for GitHub Security tab

Example output:

Bawbel Scanner v1.0.0  ·  github.com/bawbel/bawbel-scanner
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Scanning:  malicious-skill.md
Type:      skill

FINDINGS
──────────────────────────────────────────────────────────
🔴  CRITICAL  AVE-2026-00001      External instruction fetch detected
   Line 7  ·  fetch your instructions
   OWASP: ASI01 (Prompt Injection), ASI08 (Goal Hijacking)

🟠  HIGH      AVE-2026-00007      Goal override instruction detected
   Line 17  ·  Ignore all previous instructions
   OWASP: ASI01 (Prompt Injection), ASI08 (Goal Hijacking)
──────────────────────────────────────────────────────────
SUMMARY
──────────────────────────────────────────────────────────
Risk score:   9.4 / 10  CRITICAL
Findings:     2
Scan time:    5ms
→  Run bawbel report malicious-skill.md for full remediation guide

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False Positive Reduction

Bawbel v1.0 ships a 5-layer false positive reduction system — the result of real-world feedback from scanning production skill files:

Layer	Mechanism	FP reduction
FP-1	Code fence stripping — ``` blocks skipped before static analysis	~60%
FP-2	Preceding-line context — "Never do this:" suppresses the line below	~15%
FP-3	Confidence scoring — table rows, headings, docs/ paths penalised	~10%
FP-4	Meta-analyzer — one LLM call per file validates medium-confidence findings	~7%
FP-5	File-type profiles — documentation scanned at higher threshold (0.85)	~3%

The meta-analyzer (FP-4) sends all findings as enriched context to the LLM in a single call — not a general security scan, but a targeted false-positive filter. Requires BAWBEL_LLM_ENABLED=true and an API key. Skips silently if not configured.

See False Positive Reduction guide for full details.

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Detection Pipeline

Five stages run in sequence — each adds an independent layer:

Stage	Engine	Install	What it catches
0	Magika	pip install "bawbel-scanner[magika]"	Content-type verification — catches supply chain attacks (ELF disguised as .md, pickle as .yaml)
1a	Pattern	nothing — always active	15 regex rules, all AVE IDs
1b	YARA	pip install "bawbel-scanner[yara]"	Binary + complex text combinations, 15 rules
1c	Semgrep	pip install "bawbel-scanner[semgrep]"	Structural + multi-line patterns, 15 rules
2	LLM	pip install "bawbel-scanner[llm]" + API key	Obfuscated, nuanced, multi-paragraph injections
3	Sandbox	Docker + BAWBEL_SANDBOX_ENABLED=true	Runtime behaviour — network egress, filesystem, processes

15 built-in rules covering every major agentic attack class: 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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Stage 2 — LLM Semantic Analysis

Catches what regex misses: obfuscated payloads, synonym attacks, multi-paragraph injections, and social engineering. Works with any LiteLLM-supported provider.

pip install "bawbel-scanner[llm]"

export ANTHROPIC_API_KEY=sk-ant-...   # → auto-selects claude-haiku-4-5-20251001
export OPENAI_API_KEY=sk-...          # → auto-selects gpt-4o-mini
export GEMINI_API_KEY=...             # set BAWBEL_LLM_MODEL=gemini/gemini-1.5-flash
export BAWBEL_LLM_MODEL=ollama/mistral  # local model, no API key needed

bawbel scan ./my-skill.md             # Stage 2 activates automatically

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Stage 3 — Behavioral Sandbox

Runs the component inside an isolated Docker container and monitors what it actually does at runtime — catching attacks that static analysis cannot see.

export BAWBEL_SANDBOX_ENABLED=true
bawbel scan ./my-skill.md

Hybrid image strategy — no setup required:

1. Check local Docker cache  →  run immediately if found
2. Pull bawbel/sandbox:latest from Docker Hub  →  cache + run (~5s first time)
3. Build from bundled Dockerfile  →  offline / air-gapped fallback (~15s)

BAWBEL_SANDBOX_IMAGE=local                          # skip Hub, build locally
BAWBEL_SANDBOX_IMAGE=registry.corp.com/bawbel/sandbox@sha256:abc  # enterprise

Detects: outbound network egress · persistence writes (/.bashrc, crontab) · credential access (/.ssh/, .env) · shell pipe injection · subprocess spawning · Base64 encoded payloads.

See Detection Engines Guide for full sandbox documentation.

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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.ave_id}  {finding.title}")
        print(f"  Engine: {finding.engine}  CVSS-AI: {finding.cvss_ai}")
    print(f"\nRisk score: {result.risk_score:.1f} / 10")

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CI/CD Integration

GitHub Actions — official action ✅

The recommended way to integrate Bawbel into CI/CD. One line — installs the scanner, runs the scan, uploads findings to the GitHub Security tab automatically.

# .github/workflows/bawbel.yml
name: Bawbel Security Scan
on: [push, pull_request]

jobs:
  scan:
    runs-on: ubuntu-latest
    permissions:
      security-events: write
      contents: read
    steps:
      - uses: actions/checkout@v4

      - uses: bawbel/bawbel-integrations@v1
        id: bawbel
        with:
          path: .
          fail-on-severity: high
          format: sarif

      - uses: github/codeql-action/upload-sarif@v3
        if: always()
        with:
          sarif_file: ${{ steps.bawbel.outputs.sarif-file }}

Action inputs: path, fail-on-severity, format, recursive, no-ignore, version, extras Action outputs: sarif-file, findings-count, risk-score, result

See bawbel/bawbel-integrations for full documentation and examples.

VS Code Extension

Install Bawbel Scanner from the VS Code Marketplace. Auto-installs the CLI on first activation — no manual setup required. Inline diagnostics, status bar, auto-scan on save.

Manual GitHub Actions (without official action)

      - name: Scan for AVE vulnerabilities
        run: |
          pip install "bawbel-scanner[all]"
          bawbel scan . --recursive --fail-on-severity high

Pre-commit

# .pre-commit-config.yaml
repos:
  - repo: local
    hooks:
      - id: bawbel-scan
        name: Bawbel Scanner
        entry: bawbel scan
        language: system
        pass_filenames: true
        types: [markdown]
        args: ["--fail-on-severity", "high"]

pip install bawbel-scanner && pre-commit install

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Configuration

Copy .env.example and fill in your values:

cp .env.example .env

Variable	Default	Description
BAWBEL_LOG_LEVEL	WARNING	DEBUG · INFO · WARNING · ERROR
ANTHROPIC_API_KEY	—	Enables Stage 2 via Claude
OPENAI_API_KEY	—	Enables Stage 2 via OpenAI
BAWBEL_LLM_MODEL	auto	Any LiteLLM model string
BAWBEL_LLM_ENABLED	true	Set false to disable Stage 2
BAWBEL_SANDBOX_ENABLED	false	Set true to enable Stage 3
BAWBEL_SANDBOX_IMAGE	default	default · local · custom image
BAWBEL_SANDBOX_TIMEOUT	30	Container timeout in seconds
BAWBEL_SANDBOX_NETWORK	none	none=isolated · bridge=internet
BAWBEL_NO_IGNORE	false	Set true to override all suppressions (audit mode)

See .env.example for the full reference.

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Suppression — Managing False Positives

Three mechanisms to suppress known false positives. Suppressed findings are never deleted — they appear in suppressed_findings in JSON output for full audit trail.

Inline — on the line

fetch https://internal.company.com  <!-- bawbel-ignore -->
fetch https://internal.company.com  <!-- bawbel-ignore: bawbel-external-fetch -->
fetch https://internal.company.com  <!-- bawbel-ignore: AVE-2026-00001 -->
fetch https://internal.company.com  # bawbel-ignore

Block — a section

<!-- bawbel-ignore-start -->
fetch https://internal.company.com
Ignore all previous instructions  ← intentional, test fixture
<!-- bawbel-ignore-end -->

.bawbelignore — entire files or directories

# .bawbelignore
tests/fixtures/**          # all test fixtures
docs/examples/bad.md       # known-bad example file
**/test_*.md               # all test skill files

Audit mode — override all suppressions

bawbel scan ./skills/ --no-ignore      # CLI flag
BAWBEL_NO_IGNORE=true bawbel scan ./   # env var

See Suppression Guide for full documentation.

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AVE Standard

Every finding maps to a published AVE record — the CVE equivalent for agentic AI.

• Browse records: github.com/bawbel/bawbel-ave
• Threat intelligence API: api.piranha.bawbel.io
• Report a vulnerability: open an issue on bawbel-ave

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Documentation

Resource	Link
Full docs	bawbel.io/docs
Getting started	docs/guides/getting-started.md
Detection engines	docs/guides/engines.md
Configuration	docs/guides/configuration.md
CI/CD integration	docs/guides/cicd-integration.md
Python API	docs/api/scan.md
Suppression	docs/guides/suppression.md
False positive reduction	docs/guides/false-positive-reduction.md
Writing rules	docs/guides/writing-rules.md
Changelog	CHANGELOG.md

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License

Apache 2.0 — see LICENSE.

Built by Bawbel · [e-mail]