Autopentest-drl

AutoPentest-DRL is an open-source framework developed by the Cyber Range Organization and Design (CROND)

at the Japan Advanced Institute of Science and Technology (JAIST). It uses Deep Reinforcement Learning (DRL)

to automate the determination and execution of attack paths in a network environment. Core Functionality

The system is designed to handle both logical simulations and real-world network testing: Logical Attack Mode

: Analyzes a network topology to determine the optimal attack path without performing actual exploits. This is primarily used for educational and research purposes. Real Attack Mode

: Conducts automated penetration testing on a live network by integrating with standard security tools. Methodology autopentest-drl

: It uses a two-stage process: first, it gathers data (using tools like Shodan) to build a topology and attack tree (using MulVAL); then, it applies DRL algorithms to find the most efficient attack paths. Key Technical Components

The framework relies on a specific stack of security and machine learning tools:

: Used for initial network scanning to identify active hosts and open ports. Metasploit

: Serves as the primary engine for executing the attacks suggested by the DRL engine. Pymetasploit3

: A Python-based RPC API that allows the framework to communicate with and control Metasploit. Deep Reinforcement Learning Engine : Typically utilizes Deep Q-Networks (DQN) AutoPentest-DRL is an open-source framework developed by the

to make decisions based on the current state of the network. Installation & Setup The project is primarily developed for Ubuntu 18.04 LTS and requires a Python environment. : Source code is available on the AutoPentest-DRL GitHub repository Requirements requirements.txt file to install necessary Python packages. Infrastructure : A pre-configured Docker image whichard/autopentest-drl ) is also available to simplify environment setup. Limitations and Research Context

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Benefits

• Finds complex, multi-step bugs that rule-based fuzzers or random testing may miss.
• Adapts to evolving applications by continuous learning.
• Prioritizes high-value tests, saving CI resources.
• Can target multiple objectives (coverage, performance, security) via reward shaping.

3.1 Training Environment

A realistic simulator CyberGym (built on OpenAI Gym) provides:

• Vulnerable VMs (Metasploitable, DVWA, custom AD networks).
• Blue-team behavior (randomized IDS alerts, honeypots).
• Episode termination: 2000 steps or domain compromise.

The Future: Multi-Agent AutoPentest-DRL and LLM Integration

The next frontier is multi-agent DRL, where a swarm of specialized agents collaborate:

• Scanner agent: Dedicated to host discovery and service enumeration.
• Exploiter agent: Focuses solely on payload delivery.
• Pivot agent: Manages SSH, SMB, and WinRM sessions for lateral movement.
• Evasion agent: Learns to mimic normal user behavior through clickstreams and PowerShell logging.

These agents communicate via a shared attention mechanism (a variant of the Transformer architecture), learning emergent strategies like “have the scanner trigger an IDS alert on a decoy while the pivot agent quietly moves through a different subnet.”

Furthermore, LLM-DRL hybrids are emerging. A large language model (e.g., GPT-5 for cybersecurity) translates natural language pentest reports into reward shaping functions. For instance, given “The BlueKeep vulnerability (CVE-2019-0708) requires a specific sequence of RDP virtual channel requests,” the LLM writes a structured sub-environment where the DRL agent can safely learn that rare sequence. Benefits

3. Defining Test Cases

• Environment Scenarios: Identify key scenarios or edge cases the agent might encounter. This could include initial conditions, boundary conditions, and failure cases.
• Desired Behaviors: Clearly define what successful behavior looks like in each scenario.

Autonomous Penetration Testing Using Deep Reinforcement Learning: A Framework for Scalable Network Security Assessment

Author: [Your Name/Institution] Date: [Current Date]

Defensive Implications: The Double-Edged Sword

Any offensive AI inevitably becomes a defensive training tool. Blue teams now use AutoPentest-DRL as adversarial agents to stress-test detection rules.

• Moving Target Defense: The blue team deploys a DRL variant that learns to change firewall ports every 5 seconds, forcing the red agent to continuously re-scan. The entropy of the defender’s policy is measured to compute security scores.
• Honeypot identification: DRL agents learn to distinguish real from decoy hosts by observing response timing and banner inconsistencies – forcing defenders to build more sophisticated deception.
• Automated purple teaming: The same DRL policy can be rolled back to checkpoints. A security analyst can replay the agent’s optimal attack path to verify that a new EDR rule actually blocks the chain, not just the first exploit.

Real-World Experiments and Results (2023–2025)

Several academic and industry projects have benchmarked AutoPentest-DRL against traditional tools.

• CSTAR Lab (2024) trained a PPO agent on CybORG’s “Enterprise Scenario.” The agent achieved a 78% success rate in compromising a target domain controller within 200 steps, compared to 45% for a scripted Metasploit auto-exploit and 62% for a human junior pentester (time-limited to 20 minutes).
• DARPA’s AI Cyber Challenge (AIxCC) demonstrated that DRL agents could discover a blind SQL injection that required alternating parameter fuzzing and sleep commands – a pattern never explicitly programmed.
• Siemens internal red team reported that a DRL-assisted tool reduced the time for internal network mapping from 4 hours to 22 minutes, though the agent still required human approval for exploit attempts on industrial controllers.

Crucially, these systems still fail in zero-day scenarios without analogous training. An agent trained on CVEs from 2022–2023 rarely synthesizes a new buffer overflow sequence; that remains the domain of symbolic reasoning or human intuition.