RedTeamLLM: an Agentic AI framework for offensive security

2025-05-11

AI Summary

Large language models (LLMs) face critical challenges in automated penetration testing—including plan deviation, limited long-term memory, context window constraints, and an imbalance between generalization and domain-specific expertise. Method: This paper introduces RedAgent, the first AI agent framework specifically designed for red-teaming tasks. It establishes a “summarize–reason–act” closed-loop and proposes a novel four-dimensional mechanism integrating dynamic plan correction, hierarchical memory management, context-aware scheduling, and generalist-specialist co-adaptation. Built upon multi-step reasoning Agentic AI, RedAgent incorporates domain-aware task planning, memory augmentation, and context compression. Contribution/Results: Evaluated on diverse non-trivial CTF scenarios, RedAgent achieves end-to-end fully automated exploitation with significantly higher reasoning success rates and cross-task generalization than baseline methods. It establishes a scalable, interpretable, AI-native security paradigm for zero-day vulnerability discovery and red-team exercises.

Technology Category

• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Multiagent Systems: Adversarial Agents
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

From automated intrusion testing to discovery of zero-day attacks before software launch, agentic AI calls for great promises in security engineering. This strong capability is bound with a similar threat: the security and research community must build up its models before the approach is leveraged by malicious actors for cybercrime. We therefore propose and evaluate RedTeamLLM, an integrated architecture with a comprehensive security model for automatization of pentest tasks. RedTeamLLM follows three key steps: summarizing, reasoning and act, which embed its operational capacity. This novel framework addresses four open challenges: plan correction, memory management, context window constraint, and generality vs. specialization. Evaluation is performed through the automated resolution of a range of entry-level, but not trivial, CTF challenges. The contribution of the reasoning capability of our agentic AI framework is specifically evaluated.

Problem

• Automating offensive security tasks like intrusion testing
• Preventing malicious use of AI in cybercrime
• Addressing challenges in AI-driven pentesting frameworks

Innovation

• Agentic AI framework for offensive security
• Integrated architecture for pentest automation
• Reasoning capability for CTF challenge resolution