🛡️ AgentArmor — AI Security Scanner & LLM Red-Teaming Platform

LLM security · AI red teaming · prompt-injection & jailbreak testing · MCP / agent / RAG security · OWASP LLM Top 10

The Armor for AI Security. AgentArmor is an open-source scanner that continuously tests, audits, and red-teams your AI stack — LLM APIs, chatbots, tool-calling agents, MCP servers, and RAG pipelines — for prompt injection, jailbreaks, system-prompt leaks, data exfiltration, and unsafe tool use.

Every scan runs a multi-agent analysis that scores findings on a 0–100 risk model, maps them to the OWASP LLM Top 10, explains in plain English what went wrong and how to fix it, and exports reports for developers and security teams (SARIF, HTML, PDF, CSV).

Latest release: v1.4.3 · Changelog

🔍 Who it's for: AI engineers shipping LLM features, red teamers and pentesters testing GenAI apps, and security teams that need OWASP-mapped, audit-ready reports.

What AgentArmor does

End users on Windows: download the release installer, double-click, and run scans — no Python, Rust, or Node install needed.

Developers / CLI users: need Python 3.10+. Rust is only for compiling the optional native signature engine or building the Tauri GUI yourself.

Quick install

Windows desktop (recommended)

1. Download AgentArmor_1.4.3_x64-setup.exe or .msi from Releases
2. Run the installer
3. Open AgentArmor → choose scan type (API, Local Model, Agent, MCP, RAG, Benchmark)
4. Configure target → run scan → review findings → export reports

Portable mode: place a file named PORTABLE next to the executable; data is stored in ./data/.

CLI (all platforms)

pip install agentarmor

# Optional: offline local model scanning
pip install agentarmor[local]

# One-time detection model download
agentarmor models download

Docker

docker build -f docker/Dockerfile -t agentarmor/agentarmor .
docker run -p 8787:8787 agentarmor/agentarmor

Usage examples

Scan an API endpoint

# OpenAI-compatible chat API (use the POST URL from DevTools, not the HTML page)
agentarmor scan --url http://localhost:8000/v1/chat/completions

# Cloud provider
export OPENAI_API_KEY=sk-...
agentarmor scan --provider openai

# Multi-agent analysis runs on every scan (offline mode was removed in v1.4.0).
# Provide an analysis API key via AGENTARMOR_ANALYSIS_API_KEY or --analysis-api-key.
export AGENTARMOR_ANALYSIS_API_KEY=sk-...
agentarmor scan --url https://api.example.com/v1/chat/completions \
  --self-play-enabled

Scan agents, MCP, and RAG

agentarmor scan --agent crewai --agent-config agent.toml
agentarmor scan --mcp ./filesystem-mcp
agentarmor scan --rag ./corpus --embedder bge

Local models (offline)

pip install agentarmor[local]
agentarmor scan --model llama-3.gguf
agentarmor scan --model ./models/qwen3

Benchmarks

# Model leaderboard
agentarmor benchmark --providers openai,anthropic,gemini --suite owasp

# Tools comparison
agentarmor benchmark tools --suite owasp-llm01 --targets corpus

Ecosystem (v1.2)

agentarmor marketplace list
agentarmor marketplace install roleplay-injection

agentarmor monitor add "Daily API" --url https://api.example.com/v1/chat/completions --cron daily
agentarmor monitor run <schedule-id>

agentarmor dataset export -o research.jsonl --anonymize

Reports and CI gates

agentarmor scan --url http://localhost:8000/v1/chat/completions --format html,sarif,pdf -o ./reports/
agentarmor gate --sarif ./reports/findings.sarif --fail-on HIGH,CRITICAL

Config file

agentarmor scan --config AgentArmor.toml

See AgentArmor.toml for endpoint profiles, L0 attack goals, detection mode, and plugin directories.

Desktop GUI

The Tauri v2 GUI includes:

For GUI development only (not required for end users):

agentarmor serve --port 8787
cd gui && npm install && npm run dev   # http://localhost:1420

See gui/README.md for Tauri build instructions.

API server

agentarmor serve --port 8787

GitHub Action (CI/CD)

name: AI Security Scan
on: [push, pull_request]

jobs:
  agentarmor:
    runs-on: ubuntu-latest
    steps:
      - uses: YashvantHange/AgentArmor/action@v1
        with:
          url: https://api.example.com/v1/chat/completions
          fail-on: HIGH,CRITICAL

See action/action.yml.

Provider API keys

As of v1.4.0 the offline analysis mode was removed — AgentArmor runs multi-agent (cloud) analysis on every scan: capability-aware attack-graph planning, an LLM judge, and confidence scoring. This requires an analysis provider API key. Set AGENTARMOR_ANALYSIS_API_KEY (or a provider key such as OPENAI_API_KEY), or configure it in GUI Settings / AgentArmor.toml. Scans started without a key are rejected with a clear error.

Custom probes

Author probes with the SDK and publish to your local marketplace:

agentarmor marketplace validate probes/custom_probe.py
agentarmor marketplace publish probes/custom_probe.py --id my.probe --name "My Probe"

See probes/custom_probe.py and agentarmor.sdk.probe_sdk.

macOS

No native Mac .app yet (Windows desktop first). Use PyPI + CLI or dev GUI:

pip install agentarmor
agentarmor models download
agentarmor serve --port 8787

See docs/MAC.md.

Development

git clone https://github.com/YashvantHange/AgentArmor.git
cd AgentArmor
pip install -e ".[dev]"
pytest

Optional native L1 engine (requires Rust):

cd native/l1_signatures && pip install maturin && maturin develop --release

Without Rust, the Python L1 fallback is used automatically.

Windows installer build (maintainers):

powershell -File packaging/build-installer.ps1

License

See repository license file.