Rogue

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Two Ways to Harden Your Agent

🎯 Automatic Evaluation Test your agent against business policies and expected behaviors. Define scenarios & expected outcomes Verify compliance with business rules Watch live conversations as Rogue probes your agent Get detailed pass/fail reports with reasoning Best for: Regression testing, behavior validation, policy compliance

🔴 Red Teaming Simulate adversarial attacks to find security vulnerabilities. 75+ vulnerabilities across 12 security categories 20 attack techniques (encoding, social engineering, injection) CVSS-based risk scoring 8 compliance frameworks (OWASP, MITRE, NIST, GDPR, EU AI Act) Best for: Security audits, penetration testing, compliance reporting

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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:

1. Create a Python file with a call_agent function:

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}"

2. 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.

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🔥 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 Evaluation or Red Teaming

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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

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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.

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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"
}

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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

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Documentation

• Quick Reference — One-page cheat sheet
• Red Team Workflow — Technical deep-dive
• Implementation Status — Feature breakdown
• Attack Mapping — Vulnerability coverage

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Contributing

1. Fork the repository
2. Create a branch (git checkout -b feature/amazing-feature)
3. Commit changes (git commit -m 'Add amazing feature')
4. Push (git push origin feature/amazing-feature)
5. Open a Pull Request

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License

Licensed under a proprietary license — see LICENSE.

Free for personal and internal use. Commercial hosting requires licensing. Contact: hello@rogue.security