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

• URL: http://arxiv.org/abs/2502.10931v1
• Date: Sat, 15 Feb 2025 23:43:18 GMT
• Title: D-CIPHER: Dynamic Collaborative Intelligent Agents with Planning and Heterogeneous Execution for Enhanced Reasoning in Offensive Security
• Authors: Meet Udeshi, Minghao Shao, Haoran Xi, Nanda Rani, Kimberly Milner, Venkata Sai Charan Putrevu, Brendan Dolan-Gavitt, Sandeep Kumar Shukla, Prashanth Krishnamurthy, Farshad Khorrami, Ramesh Karri, Muhammad Shafique,
• Abstract summary: Large Language Models (LLMs) have been used in cybersecurity in many ways. Capture the Flag (CTF) challenges serve as benchmarks for assessing the automated task-planning abilities of LLM agents. We introduce the D-CIPHER multi-agent LLM framework for collaborative CTF challenge solving.
• Score: 22.86304661035188
• License:
• Abstract: Large Language Models (LLMs) have been used in cybersecurity in many ways, including their recent use as intelligent agent systems for autonomous security analysis. Capture the Flag (CTF) challenges serve as benchmarks for assessing the automated task-planning abilities of LLM agents across various cybersecurity skill sets. Early attempts to apply LLMs for solving CTF challenges relied on single-agent systems, where feedback was restricted to a single reasoning-action loop. This approach proved inadequate for handling complex CTF tasks. Drawing inspiration from real-world CTF competitions, where teams of experts collaborate, we introduce the D-CIPHER multi-agent LLM framework for collaborative CTF challenge solving. D-CIPHER integrates agents with distinct roles, enabling dynamic feedback loops to enhance reasoning on CTF challenges. 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 LLMs. Additionally, D-CIPHER incorporates an Auto-prompter agent, which improves problem-solving by exploring the challenge environment and generating a highly relevant initial prompt. We evaluate D-CIPHER on CTF benchmarks using multiple LLM models and conduct comprehensive studies to highlight the impact of our enhancements. Our results demonstrate that the multi-agent D-CIPHER system achieves a significant improvement in challenges solved, setting a state-of-the-art performance on three benchmarks: 22.0% on NYU CTF Bench, 22.5% on Cybench, and 44.0% on HackTheBox. D-CIPHER is available at https://github.com/NYU-LLM-CTF/nyuctf_agents as the nyuctf_multiagent package.

Related papers

• Agent-as-a-Judge: Evaluate Agents with Agents [61.33974108405561]
  We introduce the Agent-as-a-Judge framework, wherein agentic systems are used to evaluate agentic systems. This is an organic extension of the LLM-as-a-Judge framework, incorporating agentic features that enable intermediate feedback for the entire task-solving process. We present DevAI, a new benchmark of 55 realistic automated AI development tasks.
  arXiv  Detail & Related papers  (2024-10-14T17:57:02Z)
• Agent-Oriented Planning in Multi-Agent Systems [54.429028104022066]
  We propose a novel framework for agent-oriented planning in multi-agent systems, leveraging a fast task decomposition and allocation process. We integrate a feedback loop into the proposed framework to further enhance the effectiveness and robustness of such a problem-solving process.
  arXiv  Detail & Related papers  (2024-10-03T04:07:51Z)
• Textualized Agent-Style Reasoning for Complex Tasks by Multiple Round LLM Generation [49.27250832754313]
  We present AgentCOT, a llm-based autonomous agent framework. At each step, AgentCOT selects an action and executes it to yield an intermediate result with supporting evidence. We introduce two new strategies to enhance the performance of AgentCOT.
  arXiv  Detail & Related papers  (2024-09-19T02:20:06Z)
• Agent Q: Advanced Reasoning and Learning for Autonomous AI Agents [44.34340798542]
  Large Language Models (LLMs) have shown remarkable capabilities in natural language tasks requiring complex reasoning. Traditional supervised pre-training on static datasets falls short in enabling autonomous agent capabilities. We propose a framework that combines guided Monte Carlo Tree Search (MCTS) search with a self-critique mechanism and iterative fine-tuning on agent interactions.
  arXiv  Detail & Related papers  (2024-08-13T20:52:13Z)
• On the Resilience of LLM-Based Multi-Agent Collaboration with Faulty Agents [58.79302663733703]
  Large language model-based multi-agent systems have shown great abilities across various tasks due to the collaboration of expert agents. However, the impact of clumsy or even malicious agents, on the overall performance of the system remains underexplored. This paper investigates what is the resilience of various system structures under faulty agents.
  arXiv  Detail & Related papers  (2024-08-02T03:25:20Z)
• Adaptive In-conversation Team Building for Language Model Agents [33.03550687362213]
  Leveraging multiple large language model (LLM) agents has shown to be a promising approach for tackling complex tasks. Our new adaptive team-building paradigm offers a flexible solution, realized through a novel agent design named Captain Agent. A comprehensive evaluation across six real-world scenarios demonstrates that Captain Agent significantly outperforms existing multi-agent methods.
  arXiv  Detail & Related papers  (2024-05-29T18:08:37Z)
• Smurfs: Leveraging Multiple Proficiency Agents with Context-Efficiency for Tool Planning [14.635361844362794]
  Smurfs' is a cutting-edge multi-agent framework designed to revolutionize the application of large language models. Smurfs can enhance the model's ability to solve complex tasks at no additional cost.
  arXiv  Detail & Related papers  (2024-05-09T17:49:04Z)
• A Dynamic LLM-Powered Agent Network for Task-Oriented Agent Collaboration [55.35849138235116]
  We propose automatically selecting a team of agents from candidates to collaborate in a dynamic communication structure toward different tasks and domains. Specifically, we build a framework named Dynamic LLM-Powered Agent Network ($textDyLAN$) for LLM-powered agent collaboration. We demonstrate that DyLAN outperforms strong baselines in code generation, decision-making, general reasoning, and arithmetic reasoning tasks with moderate computational cost.
  arXiv  Detail & Related papers  (2023-10-03T16:05:48Z)
• Learning to Solve Complex Tasks by Talking to Agents [39.08818632689814]
  Humans often solve complex problems by interacting with existing agents, such as AI assistants, that can solve simpler sub-tasks. Common NLP benchmarks aim for the development of self-sufficient models for every task. We propose a new benchmark called CommaQA that contains three kinds of complex reasoning tasks designed to be solved by talking'' to four agents with different capabilities.
  arXiv  Detail & Related papers  (2021-10-16T10:37:34Z)
• FACMAC: Factored Multi-Agent Centralised Policy Gradients [103.30380537282517]
  We propose FACtored Multi-Agent Centralised policy gradients (FACMAC) It is a new method for cooperative multi-agent reinforcement learning in both discrete and continuous action spaces. We evaluate FACMAC on variants of the multi-agent particle environments, a novel multi-agent MuJoCo benchmark, and a challenging set of StarCraft II micromanagement tasks.
  arXiv  Detail & Related papers  (2020-03-14T21:29:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.