Autonomous Redteam Pentesing Using Agentic AI

A C Arul Prasanth, Kamalesh T G K, Manoj Babu P, Dr. K N Ambili, Dr. Sunanda Das

Abstract: Traditional penetration testing relies heavily on manual expertise, rule-based tools, and linear workflows that limit scalability, adaptability, and continuous learning. Existing offensive security systems- including standard vulnerability scanners, automated recon tools, and semi-autonomous frameworks- operate in isolation, lack long-term memory, and require constant human supervision. These tools function on predefined signatures, static exploit databases, and deterministic logic, making them less effective against evolving attack surfaces, zero-day vulnerabilities, and dynamic enterprise environments. Although some AI-assisted tools and script-based automation exist, they do not integrate deep reasoning, agent collaboration, or reinforcement-learning capabilities. Likewise, blue-team defensive systems mainly focus on detection rather than intelligent exploit simulation, leaving a gap in adversarial modelling. The proposed system introduces a fully autonomous, agentic AI-driven penetration testing architecture that overcomes these limitations by integrating a centralized custom-tuned LLM with multi-agent collaboration, reinforcement learning, long-term memory storage, and tool-augmented reasoning. Each agent performs a specialized task such as reconnaissance, scanning, exploitation, vulnerability triage, and post-exploitation analysis, with decisions validated through an internal agent-to-agent verification pipeline. The system continuously learns from previous scans, real-world datasets, exploit outputs, and user customization, enabling adaptive threat reasoning and persistent target understanding. By combining offline RL-based training, internet-assisted research, dynamic tool invocation, and storage of historical attack patterns, the framework delivers more accurate vulnerability detection, faster exploitation workflows, and enhanced ability to uncover high-impact bugs compared to traditional methods.

Keywords: Autonomous AI Agents, Offensive Security Automation, Agentic Pentesting, Tool-Driven Exploitation, LLM-Integrated Workflow, Cyber Reconnaissance AI, Automated Vulnerability Discovery, Multi-Agent Coordination, Adaptive Pentest Intelligence, AI-Enabled Exploit Analysis, Workflow Orchestration, Memory-Augmented AI

How to Cite?: A C Arul Prasanth, Kamalesh T G K, Manoj Babu P, Dr. K N Ambili, Dr. Sunanda Das, "Autonomous Redteam Pentesing Using Agentic AI", Volume 15 Issue 5, May 2026, International Journal of Science and Research (IJSR), Pages: 271-288, https://www.ijsr.net/getabstract.php?paperid=SR26325143700, DOI: https://dx.doi.org/10.21275/SR26325143700

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