Rogue — AI Agent Evaluator & Red Team Platform
Two Ways to Harden Your Agent
Architecture
Rogue operates on a client-server architecture with multiple interfaces:
| Component | Description |
|---|---|
| Server | Core evaluation & red team logic |
| TUI | Modern terminal interface (Go + Bubble Tea) |
| CLI | Non-interactive mode for CI/CD pipelines |
https://github.com/user-attachments/assets/b5c04772-6916-4aab-825b-6a7476d77787
Supported Protocols
| Protocol | Transport | Description |
|---|---|---|
| A2A | HTTP | Google's Agent-to-Agent protocol |
| MCP | SSE, STREAMABLE_HTTP | Model Context Protocol via send_message tool |
| Python | — | Direct Python function calls (no network protocol) |
See examples in examples/ for reference implementations.
Python Entrypoint
For agents implemented as Python functions without A2A or MCP:
- Create a Python file with a
call_agentfunction:
def call_agent(messages: list[dict]) -> str:
"""
Process conversation and return response.
Args:
messages: List of {"role": "user"|"assistant", "content": "..."}
Returns:
Agent's response as a string
"""
# Your agent logic here
latest = messages[-1]["content"]
return f"Response to: {latest}"
- Run Rogue with Python protocol:
uvx rogue-ai cli \
--protocol python \
--python-entrypoint-file ./my_agent.py \
--judge-llm openai/gpt-4o-mini
Or via TUI: select "Python" as the protocol and enter the file path.
See examples/python_entrypoint_stub.py for a complete example.
🔥 Quick Start
Prerequisites
uvx— Install uv- Python 3.10+
- LLM API key (OpenAI, Anthropic, or Google)
Installation
# TUI (recommended)
uvx rogue-ai
# CLI / CI/CD
uvx rogue-ai cli
Try It With the Example Agent
# All-in-one: starts both Rogue and a sample T-shirt store agent
uvx rogue-ai --example=tshirt_store
Configure in the UI:
- Agent URL:
http://localhost:10001 - Mode: Choose
Automatic EvaluationorRed Teaming
Running Modes
| Mode | Command | Description |
|---|---|---|
| Default | uvx rogue-ai |
Server + TUI |
| Server | uvx rogue-ai server |
Backend only |
| TUI | uvx rogue-ai tui |
Terminal client |
| CLI | uvx rogue-ai cli |
Non-interactive (CI/CD) |
Server Options
uvx rogue-ai server --host 0.0.0.0 --port 8000 --debug
CLI Options
uvx rogue-ai cli \
--evaluated-agent-url http://localhost:10001 \
--judge-llm openai/gpt-4o-mini \
--business-context-file ./.rogue/business_context.md
| Option | Description |
|---|---|
--config-file |
Path to config JSON |
--evaluated-agent-url |
Agent endpoint (required) |
--judge-llm |
LLM for evaluation (required) |
--business-context |
Context string or --business-context-file |
--input-scenarios-file |
Scenarios JSON |
--output-report-file |
Report output path |
Red Teaming
Scan Types
| Type | Vulnerabilities | Attacks | Time |
|---|---|---|---|
| Basic | 5 curated | 6 | ~2-3 min |
| Full | 75+ | 40+ | ~30-45 min |
| Custom | User-selected | User-selected | Varies |
Compliance Frameworks
- OWASP LLM Top 10 — Prompt injection, sensitive data exposure, excessive agency
- MITRE ATLAS — Adversarial threat landscape for AI systems
- NIST AI RMF — AI risk management framework
- ISO/IEC 42001 — AI management system standard
- EU AI Act — European AI regulation compliance
- GDPR — Data protection requirements
- OWASP API Top 10 — API security best practices
Attack Categories
| Category | Examples |
|---|---|
| Encoding | Base64, ROT13, Leetspeak |
| Social Engineering | Roleplay, trust building |
| Injection | Prompt injection, SQL injection |
| Semantic | Goal redirection, context poisoning |
| Technical | Gray-box probing, permission escalation |
Risk Scoring (CVSS-based)
Each vulnerability receives a 0-10 risk score based on:
- Impact — Severity if exploited
- Exploitability — Success rate likelihood
- Human Factor — Manual exploitation potential
- Complexity — Attack difficulty
Reproducible Scans
# Use random seeds for reproducible results
uvx rogue-ai cli --random-seed 42
Perfect for regression testing and validating security fixes.
Configuration
Environment Variables
OPENAI_API_KEY="sk-..."
ANTHROPIC_API_KEY="sk-..."
GOOGLE_API_KEY="..."
Config File (.rogue/user_config.json)
{
"evaluated_agent_url": "http://localhost:10001",
"judge_llm": "openai/gpt-4o-mini"
}
Key Features
| Feature | Description |
|---|---|
| 🔄 Dynamic Scenarios | Auto-generate tests from business context |
| 👀 Live Monitoring | Watch agent conversations in real-time |
| 📊 Comprehensive Reports | Markdown, CSV, JSON exports |
| 🔍 Multi-Faceted Testing | Policy compliance + security vulnerabilities |
| 🤖 Model Support | OpenAI, Anthropic, Google (via LiteLLM) |
| 🛡️ CVSS Scoring | Industry-standard risk assessment |
| 🔁 Reproducible | Deterministic scans with random seeds |
Documentation
- Quick Reference — One-page cheat sheet
- Red Team Workflow — Technical deep-dive
- Implementation Status — Feature breakdown
- Attack Mapping — Vulnerability coverage
Contributing
- Fork the repository
- Create a branch (
git checkout -b feature/amazing-feature) - Commit changes (
git commit -m 'Add amazing feature') - Push (
git push origin feature/amazing-feature) - Open a Pull Request
License
Licensed under a proprietary license — see LICENSE.
Free for personal and internal use. Commercial hosting requires licensing.
Contact: hello@rogue.security
1 maintainer
Metadata
1 maintainer
Release files for rogue-ai 0.6.4
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