# D-CIPHER: Dynamic Collaborative Intelligent Multi-Agent System with Planner and Heterogeneous Executors for Offensive Security

arXiv: https://arxiv.org/abs/2502.10931 · HTML: https://mlsanity.com/paper/2502.10931/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use.
- Builds on 29 of its 52 references that are in the map.
- First posted on arXiv 15 Feb 2025.

## Placed in

- LLM agents and tool use / [Agent threats, guardrails and governance](https://mlsanity.com/agents/agent-threats-guardrails-and-governance/)

## Builds on (most cited first, 29 of 29)

- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [A Survey on Large Language Model based Autonomous Agents](https://mlsanity.com/paper/2308.11432/) (2023)
- [SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering](https://mlsanity.com/paper/2405.15793/) (2024)
- [Large Language Model based Multi-Agents: A Survey of Progress and Challenges](https://mlsanity.com/paper/2402.01680/) (2024)
- [Automatic Chain of Thought Prompting in Large Language Models](https://mlsanity.com/paper/2210.03493/) (2022)
- [Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models](https://mlsanity.com/paper/2305.04091/) (2023)
- [InterCode: Standardizing and Benchmarking Interactive Coding with Execution Feedback](https://mlsanity.com/paper/2306.14898/) (2023)
- [ReWOO: Decoupling Reasoning from Observations for Efficient Augmented Language Models](https://mlsanity.com/paper/2305.18323/) (2023)
- [More Agents Is All You Need](https://mlsanity.com/paper/2402.05120/) (2024)
- [Cybench: A Framework for Evaluating Cybersecurity Capabilities and Risks of Language Models](https://mlsanity.com/paper/2408.08926/) (2024)
- [Keep the Conversation Going: Fixing 162 out of 337 bugs for 0.42 each using ChatGPT](https://mlsanity.com/paper/2304.00385/) (2023)
- [NYU CTF Bench: A Scalable Open-Source Benchmark Dataset for Evaluating LLMs in Offensive Security](https://mlsanity.com/paper/2406.05590/) (2024)
- [Unveiling Security, Privacy, and Ethical Concerns of ChatGPT](https://mlsanity.com/paper/2307.14192/) (2023)
- [EnIGMA: Interactive Tools Substantially Assist LM Agents in Finding Security Vulnerabilities](https://mlsanity.com/paper/2409.16165/) (2024)
- [PentestAgent: Incorporating LLM Agents to Automated Penetration Testing](https://mlsanity.com/paper/2411.05185/) (2024)
- [HackSynth: LLM Agent and Evaluation Framework for Autonomous Penetration Testing](https://mlsanity.com/paper/2412.01778/) (2024)
- [An Empirical Evaluation of LLMs for Solving Offensive Security Challenges](https://mlsanity.com/paper/2402.11814/) (2024)
- [AutoSafeCoder: A Multi-Agent Framework for Securing LLM Code Generation through Static Analysis and Fuzz Testing](https://mlsanity.com/paper/2409.10737/) (2024)
- [From Text to MITRE Techniques: Exploring the Malicious Use of Large Language Models for Generating Cyber Attack Payloads](https://mlsanity.com/paper/2305.15336/) (2023)
- [Using Large Language Models for Cybersecurity Capture-The-Flag Challenges and Certification Questions](https://mlsanity.com/paper/2308.10443/) (2023)
- [Outside the Comfort Zone: Analysing LLM Capabilities in Software Vulnerability Detection](https://mlsanity.com/paper/2408.16400/) (2024)
- [Hacking CTFs with Plain Agents](https://mlsanity.com/paper/2412.02776/) (2024)
- [Revisiting Automated Prompting: Are We Actually Doing Better?](https://mlsanity.com/paper/2304.03609/) (2023)
- [Evaluating Large Language Models for Line-Level Vulnerability Localization](https://mlsanity.com/paper/2404.00287/) (2024)
- [Benefits and Pitfalls of Using Capture the Flag Games in University Courses](https://mlsanity.com/paper/2004.11556/) (2020)
- [Multi-Agent Collaboration in Incident Response with Large Language Models](https://mlsanity.com/paper/2412.00652/) (2024)
- [Battle Ground: Data Collection and Labeling of CTF Games to Understand Human Cyber Operators](https://mlsanity.com/paper/2307.10877/) (2023)
- [Observations From an Online Security Competition and Its Implications on Crowdsourced Security](https://mlsanity.com/paper/2204.12601/) (2022)
- [Capture the Flag for Team Construction in Cybersecurity](https://mlsanity.com/paper/2206.08971/) (2022)

## Built on by (most cited first, 20 of 20)

- [A Survey on Agentic Security: Applications, Threats and Defenses](https://mlsanity.com/paper/2510.06445/) (2025)
- [CRAKEN: Cybersecurity LLM Agent with Knowledge-Based Execution](https://mlsanity.com/paper/2505.17107/) (2025)
- [Towards Effective Offensive Security LLM Agents: Hyperparameter Tuning, LLM as a Judge, and a Lightweight CTF Benchmark](https://mlsanity.com/paper/2508.05674/) (2025)
- [From Trace to Line: LLM Agent for Real-World OSS Vulnerability Localization](https://mlsanity.com/paper/2510.02389/) (2025)
- [CTFusion: A CTF-based Benchmark for LLM Agent Evaluation](https://mlsanity.com/paper/2605.11504/) (2026)
- [CTFExplorer: Evaluating LLM Offensive Agents Through Multi-Target Web CTF Benchmarking](https://mlsanity.com/paper/2602.08023/) (2026)
- [CREBench: Evaluating Large Language Models in Cryptographic Binary Reverse Engineering](https://mlsanity.com/paper/2604.03750/) (2026)
- [CrackMeBench: Binary Reverse Engineering for Agents](https://mlsanity.com/paper/2605.10597/) (2026)
- [CritBench: A Framework for Evaluating Cybersecurity Capabilities of Large Language Models in IEC 61850 Digital Substation Environments](https://mlsanity.com/paper/2604.06019/) (2026)
- [Honeyval: A Comprehensive Evaluation Framework for LLM-powered HTTP Honeypots](https://mlsanity.com/paper/2605.29963/) (2026)
- [Cyber-Capable AI Agents: Vulnerabilities, Evaluation Containment, and Defensive Response](https://mlsanity.com/paper/2607.25379/) (2026)
- [Beyond End-to-End Success: Diagnosing Failures in Long-Horizon Security LLM Agents](https://mlsanity.com/paper/2608.20563/) (2026)
- [How Do LLM Agents Actually Get the Flag? Trace-Level Provenance for Agentic Offensive Security Evaluation](https://mlsanity.com/paper/2608.26237/) (2026)
- [Systematic Capability Benchmarking of Frontier Large Language Models for Offensive Cyber Tasks](https://mlsanity.com/paper/2604.17159/) (2026)
- [RAVEN: Retrieval-Augmented Vulnerability Exploration Network for Memory Corruption Analysis in User Code and Binary Programs](https://mlsanity.com/paper/2604.17948/) (2026)
- [uGen: An Agentic Framework for Generating Microarchitectural Attack PoCs](https://mlsanity.com/paper/2605.15503/) (2026)
- [CyberEvolver: Structured Self-Evolution for Cybersecurity Agents On the Fly](https://mlsanity.com/paper/2605.26195/) (2026)
- [Data-Centric Benchmarking of Exploit Generation in LLMs: Understanding the Impact of Fine-Tuning](https://mlsanity.com/paper/2606.15123/) (2026)
- [Reveree: Diagnosing LLM Reverse-Engineering Agents](https://mlsanity.com/paper/2609.01185/) (2026)
- [CAIRN: Dynamic Fact-Intent DAGs for Multi-Agent Exploration](https://mlsanity.com/paper/2609.32700/) (2026)

## Abstract (arXiv)

Large Language Models (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.