D-CIPHER: Dynamic Collaborative Intelligent Multi-Agent System with Planner and Heterogeneous Executors for Offensive Security (2502.10931v2)

Abstract: LLMs have been used in cybersecurity such as autonomous security analysis or penetration testing. Capture the Flag (CTF) challenges serve as benchmarks to assess automated task-planning abilities of LLM agents for cybersecurity. Early attempts to apply LLMs for solving CTF challenges used single-agent systems, where feedback was restricted to a single reasoning-action loop. This approach was inadequate for complex CTF tasks. Inspired by real-world CTF competitions, where teams of experts collaborate, we introduce the D-CIPHER LLM multi-agent framework for collaborative CTF solving. D-CIPHER integrates agents with distinct roles with dynamic feedback loops to enhance reasoning on complex tasks. It introduces the Planner-Executor agent system, consisting of a Planner agent for overall problem-solving along with multiple heterogeneous Executor agents for individual tasks, facilitating efficient allocation of responsibilities among the agents. Additionally, D-CIPHER incorporates an Auto-prompter agent to improve problem-solving by auto-generating a highly relevant initial prompt. We evaluate D-CIPHER on multiple CTF benchmarks and LLM models via comprehensive studies to highlight the impact of our enhancements. Additionally, we manually map the CTFs in NYU CTF Bench to MITRE ATT&CK techniques that apply for a comprehensive evaluation of D-CIPHER's offensive security capability. D-CIPHER achieves state-of-the-art performance on three benchmarks: 22.0% on NYU CTF Bench, 22.5% on Cybench, and 44.0% on HackTheBox, which is 2.5% to 8.5% better than previous work. D-CIPHER solves 65% more ATT&CK techniques compared to previous work, demonstrating stronger offensive capability.

Summary and Analysis of "D-CIPHER: Dynamic Collaborative Intelligent Agents with Planning and Heterogeneous Execution for Enhanced Reasoning in Offensive Security"

"D-CIPHER: Dynamic Collaborative Intelligent Agents with Planning and Heterogeneous Execution for Enhanced Reasoning in Offensive Security" addresses the critical challenge of using LLMs in complex cybersecurity tasks, specifically, Capture the Flag (CTF) challenges requiring collaboration across multiple domains such as cryptography, digital forensics, and reverse engineering.

The paper critiques the inadequacies of single-agent systems in handling the complexity of CTF scenarios due to limited dynamic feedback capabilities and self-contained reasoning-action loops. As a solution, it proposes D-CIPHER, a multi-agent framework that divides roles among specialized LLM agents, thereby facilitating improved collaborative problem-solving.

Key Components and Design

1. Multi-Agent Architecture: The framework introduces distinct roles for each agent:
   • Planner Agent: Responsible for formulating and managing an overall problem-solving strategy, while delegating execution tasks to specialized Executor agents.
   • Executor Agents: Tasked with completing specific assignments designated by the Planner, maintaining focus on individual problem components.
   • Auto-prompter Agent: Enhances task initiation through environmental exploration and prompt generation, using dynamic input over static hard-coded prompts.
2. Planner-Executor System: Divides problem-solving responsibilities, allowing detailed task execution and reducing information overload typical in long task sequences, commonly seen with single-agent frameworks.
3. Efficiency and Focus: By streamlining command and function calls, the framework enhances computational efficiency. Each agent operates independently within its task context, avoiding the need for extensive historical input re-analysis, and thus maintaining streamlined focus and resource utilization.

Performance and Evaluation

In terms of empirical results, D-CIPHER excels in the benchmarks tested, achieving state-of-the-art performance on the NYU CTF Bench, Cybench, and HackTheBox, reaching percentages of 22.0%, 22.5%, and 44.0% success in challenges solved, respectively. The critical advancement lies in its ability to outperform existing single-agent frameworks significantly, maintaining lower average costs per solved challenge, reflecting efficient resource utilization across agents.

Implications

The multi-agent system of D-CIPHER shifts the paradigm in LLM utilization for cybersecurity applications by highlighting the potential for heterogeneous execution strategies and dynamic role assignment. By demonstrating improved performance and efficiency, this approach could inspire new models of collaborative AI systems capable of tackling intricate problems in other domains beyond cybersecurity, suggesting broad implications for future AI development trajectories.

Limitations and Future Directions

While the multi-agent approach demonstrates marked improvement, the paper acknowledges certain failures, such as communication bottlenecks when task information is not fully integrated across agents. Future enhancements could explore more intricate agent communication protocols and integration with advanced interactive tools. Additionally, orchestrating different capability tiers within agents could further optimize cost-efficiency in resource-constrained environments.

The advent of D-CIPHER underscores the importance of team dynamics within AI systems, proposing a robust framework for ongoing research in collaborative multi-agent problem-solving strategies across various digital threat landscapes. As AI continues to evolve, the theoretical and practical insights derived from this paper could catalyze further exploration of dynamic task-solving systems in complex environments.