# An Empirical Evaluation of LLMs for Solving Offensive Security Challenges

arXiv: https://arxiv.org/abs/2402.11814 · HTML: https://mlsanity.com/paper/2402.11814/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Feb 2024.

## Built on by (most cited first, 30 of 35)

- [NYU CTF Bench: A Scalable Open-Source Benchmark Dataset for Evaluating LLMs in Offensive Security](https://mlsanity.com/paper/2406.05590/) (2024)
- [SelfDefend: LLMs Can Defend Themselves against Jailbreaking in a Practical Manner](https://mlsanity.com/paper/2406.05498/) (2024)
- [HyperAgent: Generalist Software Engineering Agents to Solve Coding Tasks at Scale](https://mlsanity.com/paper/2409.16299/) (2024)
- [D-CIPHER: Dynamic Collaborative Intelligent Multi-Agent System with Planner and Heterogeneous Executors for Offensive Security](https://mlsanity.com/paper/2502.10931/) (2025)
- [Incalmo: An Autonomous LLM-assisted System for Red Teaming Multi-Host Networks](https://mlsanity.com/paper/2501.16466/) (2025)
- [Hacking Back the AI-Hacker: Prompt Injection as a Defense Against LLM-driven Cyberattacks](https://mlsanity.com/paper/2410.20911/) (2024)
- [Measuring and Augmenting Large Language Models for Solving Capture-the-Flag Challenges](https://mlsanity.com/paper/2506.17644/) (2025)
- [Benchmarking Practices in LLM-driven Offensive Security: Testbeds, Metrics, and Experiment Design](https://mlsanity.com/paper/2504.10112/) (2025)
- [CRAKEN: Cybersecurity LLM Agent with Knowledge-Based Execution](https://mlsanity.com/paper/2505.17107/) (2025)
- [OCCULT: Evaluating Large Language Models for Offensive Cyber Operation Capabilities](https://mlsanity.com/paper/2502.15797/) (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)
- [LLMs Can Defend Themselves Against Jailbreaking in a Practical Manner: A Vision Paper](https://mlsanity.com/paper/2402.15727/) (2024)
- [What Makes a Good LLM Agent for Real-world Penetration Testing?](https://mlsanity.com/paper/2602.17622/) (2026)
- [AutoPentest: Enhancing Vulnerability Management With Autonomous LLM Agents](https://mlsanity.com/paper/2505.10321/) (2025)
- [Dynamic Risk Assessments for Offensive Cybersecurity Agents](https://mlsanity.com/paper/2505.18384/) (2025)
- [AIRTBench: Measuring Autonomous AI Red Teaming Capabilities in Language Models](https://mlsanity.com/paper/2506.14682/) (2025)
- [eyeballvul: a future-proof benchmark for vulnerability detection in the wild](https://mlsanity.com/paper/2407.08708/) (2024)
- [Applying Refusal-Vector Ablation to Llama 3.1 70B Agents](https://mlsanity.com/paper/2410.10871/) (2024)
- [LLMs unlock new paths to monetizing exploits](https://mlsanity.com/paper/2505.11449/) (2025)
- [A Comprehensive Survey on the Security of Smart Grid: Challenges, Mitigations, and Future Research Opportunities](https://mlsanity.com/paper/2407.07966/) (2024)
- [SeCodePLT: A Unified Platform for Evaluating the Security of Code GenAI](https://mlsanity.com/paper/2410.11096/) (2024)
- [Large Language Models and Arabic Content: A Review](https://mlsanity.com/paper/2505.08004/) (2025)
- [From Trace to Line: LLM Agent for Real-World OSS Vulnerability Localization](https://mlsanity.com/paper/2510.02389/) (2025)
- [Hackers or Hallucinators? A Comprehensive Analysis of LLM-Based Automated Penetration Testing](https://mlsanity.com/paper/2604.05719/) (2026)
- [CTFusion: A CTF-based Benchmark for LLM Agent Evaluation](https://mlsanity.com/paper/2605.11504/) (2026)
- [No Size Fits All: The Perils and Pitfalls of Leveraging LLMs Vary with Company Size](https://mlsanity.com/paper/2408.01444/) (2024)
- [The Best Defense is a Good Offense: Countering LLM-Powered Cyberattacks](https://mlsanity.com/paper/2410.15396/) (2024)
- [CTFExplorer: Evaluating LLM Offensive Agents Through Multi-Target Web CTF Benchmarking](https://mlsanity.com/paper/2602.08023/) (2026)
- [What Makes an Evaluation Useful? Common Pitfalls and Best Practices](https://mlsanity.com/paper/2503.23424/) (2025)
- [AI In Cybersecurity Education -- Scalable Agentic CTF Design Principles and Educational Outcomes](https://mlsanity.com/paper/2603.21551/) (2026)

## Abstract (arXiv)

Capture The Flag (CTF) challenges are puzzles related to computer security scenarios. With the advent of large language models (LLMs), more and more CTF participants are using LLMs to understand and solve the challenges. However, so far no work has evaluated the effectiveness of LLMs in solving CTF challenges with a fully automated workflow. We develop two CTF-solving workflows, human-in-the-loop (HITL) and fully-automated, to examine the LLMs' ability to solve a selected set of CTF challenges, prompted with information about the question. We collect human contestants' results on the same set of questions, and find that LLMs achieve higher success rate than an average human participant. This work provides a comprehensive evaluation of the capability of LLMs in solving real world CTF challenges, from real competition to fully automated workflow. Our results provide references for applying LLMs in cybersecurity education and pave the way for systematic evaluation of offensive cybersecurity capabilities in LLMs.