warden-ai 1.0.4

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. Scans code patterns, MCP configs, infrastructure, secrets, agent architecture, dependencies, and audit compliance. 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

# View the scoring methodology
warden methodology

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

7 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

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

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 score, findings, and D17 warning
• warden_report.html -- self-contained HTML report (no external requests, works air-gapped)
• warden_report.json -- machine-readable with scoring_version field

Example Output

  ____    __              __   ___            __
 / __/__ / /  ___ _____  / /__/ _ \___  __ __/ /____  ____
_\ \/ _ \/ _ \/ _ `/ __/_/  '_/ , _/ _ \/ // / __/ -_)/ __/
/___/_//_/_//_/\_,_/_/ /_/\_\/_/|_|\___/\_,_/\__/\__/_/

Warden v1.0.0 -- AI Agent Governance Scanner
Scanning: /home/user/my-agent-project
--------------------------------------------
  Layer 1: Code Patterns ...... 12 findings
  Layer 2: MCP Servers ........ 3 findings
  Layer 3: Infrastructure ..... 5 findings
  Layer 4: Secrets ............ 2 findings (2 CRITICAL)
  Layer 5: Agent Architecture . 4 findings
  Layer 6: Supply Chain ....... 1 finding
  Layer 7: Audit & Compliance . 6 findings

  Governance tools detected: Pangea (CrowdStrike)
  Competitors in registry: 17
--------------------------------------------
  GOVERNANCE SCORE: 19 / 100 -- UNGOVERNED
--------------------------------------------

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

Test Suite

94 tests covering:

• Scoring model (17 dimensions, normalization math, all 4 score levels)
• All 7 scan layers with fixture-based tests
• D17 trap defense (env var detection, code pattern detection, full defense max score)
• Competitor detection (confidence levels, multi-signal detection)
• JSON/HTML report generation
• Security tests: no network imports, no SharkRouter imports, secrets masking, HTML self-contained

Known Limitations

• Language coverage: v1.0 scans Python and JS/TS code patterns. Go/Rust/Java code analysis is planned for v2.0. Infrastructure, secrets, and dependency scanning apply to all languages.
• 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.

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.