warden-ai 1.2.1

AI Agent Governance Scanner — local-only CLI that scores governance posture across 17 dimensions

Warden — AI Agent Governance Scanner

Open-source, local-only CLI scanner that evaluates AI agent governance posture across 12 scan layers and 17 dimensions. Scans code patterns, MCP configs, infrastructure, secrets, agent architecture, dependencies, audit compliance, CI/CD pipelines, IaC security, framework-specific governance, multi-language code, and cloud AI services. No data leaves the machine.

Quick Start

# With pip
pip install warden-ai
warden scan /path/to/your-agent-project

# With uv (zero setup, one-shot)
uvx --from warden-ai warden scan /path/to/your-agent-project

From zero to governance score in under 60 seconds.

What It Does

Warden scores your AI agent project across 17 governance dimensions (out of 235 raw, normalized to /100):

Group	Dimensions
Core Governance (100 pts)	Tool Inventory, Risk Detection, Policy Coverage, Credential Management, Log Hygiene, Framework Coverage
Advanced Controls (50 pts)	Human-in-the-Loop, Agent Identity, Threat Detection
Ecosystem (55 pts)	Prompt Security, Cloud/Platform, LLM Observability, Data Recovery, Compliance Maturity
Unique Capabilities (30 pts)	Post-Exec Verification, Data Flow Governance, Adversarial Resilience

Score Levels

Score	Level	Meaning
>= 80	GOVERNED	Comprehensive agent governance in place
>= 60	PARTIAL	Significant coverage with material gaps
>= 33	AT_RISK	Some controls exist but major blind spots
< 33	UNGOVERNED	Minimal or no agent governance

CLI Commands

# Scan a project (generates HTML + JSON reports)
warden scan .
warden scan /path/to/project --format json
warden scan /path/to/project --output-dir /path/to/reports

# Skip specific layers
warden scan . --skip secrets,deps

# Run only specific layers
warden scan . --only code,mcp,cloud

# View the scoring methodology
warden methodology

# See the market leaderboard (17 vendors x 17 dimensions)
warden leaderboard

Layer Keys for --skip / --only

Key	Layer
code	Code Patterns (Python AST + JS/TS regex)
mcp	MCP Server Configs
infra	Infrastructure (Docker, K8s)
secrets	Secrets & Credentials
agent	Agent Architecture
deps	Supply Chain / Dependencies
audit	Audit & Compliance
cicd	CI/CD Governance
iac	IaC Security (Terraform, Pulumi, CloudFormation)
frameworks	Framework-Specific Governance
multilang	Multi-Language Governance (Go, Rust, Java)
cloud	Cloud AI Governance (AWS, Azure, GCP)

12 Scan Layers

1. Code Patterns — AST-based Python + regex JS/TS analysis (unprotected LLM calls, agent loops, unrestricted tool access)
2. MCP Servers — Config file analysis (write tools without auth, missing schemas, non-TLS transport)
3. Infrastructure — Dockerfile, docker-compose, K8s manifests (root containers, exposed secrets, missing healthchecks)
4. Secrets — 15+ credential patterns with value masking (OpenAI, Anthropic, AWS, GitHub, Stripe, etc.)
5. Agent Architecture — Agent class analysis (no permissions, no cost tracking, unlimited sub-agent spawning)
6. Supply Chain — Dependency analysis (unpinned AI packages, typosquat detection via Levenshtein distance)
7. Audit & Compliance — Audit logging, structured logging, retention policies, compliance framework mapping
8. CI/CD Governance — GitHub Actions analysis (missing approvals, exposed secrets, no branch protection, CODEOWNERS)
9. IaC Security — Terraform, Pulumi, and CloudFormation analysis (unencrypted storage, open security groups, IAM wildcards, missing remote backend)
10. Framework Governance — LangChain callbacks, CrewAI guardrails, AutoGen sandboxing, LlamaIndex limits
11. Multi-Language Governance — Go (context timeouts, unsafe exec), Rust (unsafe blocks, .unwrap() on API calls), Java (Spring AI @Tool auth, audit logging)
12. Cloud AI Governance — AWS Bedrock guardrails, Azure AI Content Safety, GCP Vertex AI safety settings, managed identity vs hardcoded keys

Plus D17: Adversarial Resilience — 8 sub-checks based on Google DeepMind's "AI Agent Traps" paper (Franklin et al., March 2026).

Language Support

Language	Code Patterns	Secrets	Dependencies	Framework-Specific	Cloud AI
Python	AST	Yes	pip/poetry/uv	LangChain, CrewAI, AutoGen, LlamaIndex	Bedrock, Azure AI, Vertex AI
JavaScript/TypeScript	Regex	Yes	npm/yarn/pnpm	—	—
Go	Regex	Yes	go.mod	context, exec, rate limiting	—
Rust	Regex	Yes	Cargo.toml	tracing, tokio, unsafe blocks	—
Java	Regex	Yes	Maven/Gradle	Spring AI, Spring Security	—
Terraform	HCL regex	—	Provider versions	—	—
Pulumi	Via TS/PY	—	—	—	—
CloudFormation	YAML/JSON regex	—	—	—	—

Competitor Detection

Warden detects 17 governance and security tools across 5 signal layers (env vars, processes, MCP configs, packages, Docker containers). Detection requires 2+ signals from different layers to prevent false positives.

Output Formats

• CLI summary — colorized terminal output with per-layer elapsed time, progress bars, and D17 warning
• warden_report.html — self-contained dark-theme report with SVG score ring, expandable findings, benchmark bars, and market comparison (no external requests, works air-gapped)
• warden_report.json — machine-readable with scoring_version field

Architecture Constraints

1. Zero network access — Scanners never import httpx/requests/urllib. CI-enforced.
2. Zero SharkRouter imports — Standalone package with no internal dependencies. CI-enforced.
3. Secrets never stored — Only file, line, pattern name, and masked preview (first 3 + last 4 chars).
4. HTML report self-contained — No CDN, no Google Fonts. Works in air-gapped environments.

Development

# With uv (recommended)
uv sync --extra dev
uv run pytest tests/ -v

# With pip
python -m venv .venv
source .venv/bin/activate  # or .venv\Scripts\activate on Windows
pip install -e ".[dev]"
pytest tests/ -v

Known Limitations

• Framework vocabulary: Scoring is optimized for recognized AI frameworks. Custom frameworks may score lower despite equivalent governance.
• Static analysis: Warden detects governance patterns, not enforcement. High score = controls present, not proven correct.
• IaC depth: Terraform has the deepest analysis. Pulumi and CloudFormation checks are regex-based heuristics.
• Multi-language AST: Go/Rust/Java analysis uses regex, not AST parsing. Fewer patterns detected than Python.

See SCORING.md for full details.

Methodology

Full scoring methodology: SCORING.md

Run warden methodology to see it in your terminal.

License

MIT

Research Citation

Adversarial resilience dimension (D17) cites:

Franklin, Tomasev, Jacobs, Leibo, Osindero. "AI Agent Traps." Google DeepMind, March 2026.

Every D17 finding maps to EU AI Act articles, OWASP LLM Top 10, and MITRE ATLAS techniques.