Autonomous Agents
Autonomous AI agents can plan, execute, and adapt security research tasks with minimal human intervention. They break down complex objectives into actionable steps and self-correct based on results.
High Risk
AutoGPT
AutoGPT
github.com/Significant-Gravitas/AutoGPT
An experimental open-source application showcasing GPT-4's capabilities as an autonomous agent. It chains together LLM "thoughts" to accomplish user-defined goals.
Installation
# Clone repository git clone https://github.com/Significant-Gravitas/AutoGPT.git cd AutoGPT # Set up environment cp .env.template .env # Edit .env with your API keys # Install dependencies pip install -r requirements.txt # Run AutoGPT python -m autogpt
Security Research Example
Name: SecurityResearcher Role: An AI security researcher that performs authorized vulnerability assessments Goals: 1. Enumerate subdomains of authorized-target.com 2. Identify open ports and services on discovered hosts 3. Check for common web vulnerabilities using nuclei 4. Generate a detailed findings report 5. Suggest remediation steps for each finding AutoGPT will: - Break down goals into tasks - Execute reconnaissance tools - Analyze results and adapt approach - Document findings automatically
Key Features for Security
Internet Access
Browse web, search for exploits, access security databases.
Code Execution
Write and execute Python scripts for custom tools.
File Operations
Read/write files for data persistence and reporting.
Memory
Long-term memory for context across sessions.
AgentGPT
AgentGPT
agentgpt.reworkd.ai
A web-based autonomous AI agent platform. Deploy agents directly in your browser without local installation.
Advantages
- No local setup required - runs in browser
- Visual task execution feedback
- Easy to share and collaborate
- Rate-limited for safety
BabyAGI
BabyAGI
github.com/yoheinakajima/babyagi
A simplified autonomous agent focused on task management. Creates, prioritizes, and executes tasks based on previous results.
Task Loop Architecture
┌──────────────────────────────────────────────────────────────┐ │ BABYAGI LOOP │ ├──────────────────────────────────────────────────────────────┤ │ │ │ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │ │ │ Task │────▶│ Execution │────▶│ Result │ │ │ │ Queue │ │ Agent │ │ Storage │ │ │ └─────────────┘ └─────────────┘ └─────────────┘ │ │ ▲ │ │ │ │ ▼ │ │ ┌─────────────┐ ┌─────────────┐ │ │ │ Task │◀───────────────────────│ Task │ │ │ │ Prioritizer │ │ Creator │ │ │ └─────────────┘ └─────────────┘ │ │ │ └──────────────────────────────────────────────────────────────┘
Security-Focused Agents
WhiteRabbitNeo
Uncensored Security LLM
A fine-tuned LLM specifically designed for cybersecurity without the typical safety restrictions. Available for local deployment.
# Run with Ollama ollama pull whiterabbitneo ollama run whiterabbitneo # Or use with LM Studio # Download GGUF model from HuggingFace
Custom Security Agents
Build custom autonomous agents for specific security tasks:
from langchain.agents import initialize_agent, Tool
from langchain.llms import OpenAI
# Define security tools
tools = [
Tool(
name="NmapScan",
func=run_nmap,
description="Scan ports on a target"
),
Tool(
name="NucleiScan",
func=run_nuclei,
description="Run vulnerability templates"
),
Tool(
name="SubdomainEnum",
func=run_subfinder,
description="Enumerate subdomains"
)
]
# Initialize agent
agent = initialize_agent(
tools,
llm=OpenAI(temperature=0),
agent="zero-shot-react-description",
verbose=True
)
# Run autonomous assessment
agent.run("""
Perform a security assessment on authorized-target.com:
1. Find all subdomains
2. Scan for open ports
3. Check for vulnerabilities
4. Report findings
""") Safety Controls
🛑 Kill Switch
Always implement a way to immediately stop agent execution.
⚠️ Approval Workflow
Require human approval for destructive or sensitive actions.
🔒 Sandboxing
Run in isolated VMs or containers with limited network access.
📊 Logging
Log all agent actions for audit and review.
Critical Warning