# Forewarned is Forearmed: A Survey on Large Language Model-based Agents in Autonomous Cyberattacks

arXiv: https://arxiv.org/abs/2505.12786 · HTML: https://mlsanity.com/paper/2505.12786/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 May 2025.

## Built on by (most cited first, 16 of 16)

- [Vibe Coding vs. Agentic Coding: Fundamentals and Practical Implications of Agentic AI](https://mlsanity.com/paper/2505.19443/) (2025)
- [A Survey of Agentic AI and Cybersecurity: Challenges, Opportunities and Use-case Prototypes](https://mlsanity.com/paper/2601.05293/) (2026)
- [PACEbench: A Framework for Evaluating Practical AI Cyber-Exploitation Capabilities](https://mlsanity.com/paper/2510.11688/) (2025)
- [Toward Secure LLM Agents: Threat Surfaces, Attacks, Defenses, and Evaluation](https://mlsanity.com/paper/2606.10749/) (2026)
- [Safety Testing LLM Agents at Scale: From Risk Discovery to Evidence-Grounded Verification](https://mlsanity.com/paper/2607.01793/) (2026)
- [Securing Agentic AI: Threat Modeling and Risk Analysis for Network Monitoring Agentic AI System](https://mlsanity.com/paper/2508.10043/) (2025)
- [Beyond Data Privacy: New Privacy Risks for Large Language Models](https://mlsanity.com/paper/2509.14278/) (2025)
- [Hiding in the AI Traffic: Abusing MCP for LLM-Powered Agentic Red Teaming](https://mlsanity.com/paper/2511.15998/) (2025)
- [Agent-based simulation of online social networks and disinformation](https://mlsanity.com/paper/2512.22082/) (2025)
- [Explainable Autonomous Cyber Defense using Adversarial Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2604.04442/) (2026)
- [Autonomous Adversary: Red-Teaming in the age of LLM](https://mlsanity.com/paper/2605.06486/) (2026)
- [Large Language Models in Cybersecurity: Applications, Vulnerabilities, and Defense Techniques](https://mlsanity.com/paper/2507.13629/) (2025)
- [Trustworthiness Calibration Framework for Phishing Email Detection Using Large Language Models](https://mlsanity.com/paper/2511.04728/) (2025)
- [Trustworthy AI LLM Scalability Risk Index (LSRI): A Cybersecurity Framework Assessing Agentic-AI Security & Software Model Supply Chain Safety Boosting AI-Generated Malware Defense & Explainability Mitigating Emerging Risks of Generative AI](https://mlsanity.com/paper/2602.19021/) (2026)
- [Self-Adaptive Multi-Agent LLM-Based Security Pattern Selection for IoT Systems](https://mlsanity.com/paper/2605.00741/) (2026)
- [LLM-Based Agents for Software and Systems Security: Approaches, Applications, and Assessment](https://mlsanity.com/paper/2608.28490/) (2026)

## Abstract (arXiv)

With the continuous evolution of Large Language Models (LLMs), LLM-based agents have advanced beyond passive chatbots to become autonomous cyber entities capable of performing complex tasks, including web browsing, malicious code and deceptive content generation, and decision-making. By significantly reducing the time, expertise, and resources, AI-assisted cyberattacks orchestrated by LLM-based agents have led to a phenomenon termed Cyber Threat Inflation, characterized by a significant reduction in attack costs and a tremendous increase in attack scale. To provide actionable defensive insights, in this survey, we focus on the potential cyber threats posed by LLM-based agents across diverse network systems. Firstly, we present the capabilities of LLM-based cyberattack agents, which include executing autonomous attack strategies, comprising scouting, memory, reasoning, and action, and facilitating collaborative operations with other agents or human operators. Building on these capabilities, we examine common cyberattacks initiated by LLM-based agents and compare their effectiveness across different types of networks, including static, mobile, and infrastructure-free paradigms. Moreover, we analyze threat bottlenecks of LLM-based agents across different network infrastructures and review their defense methods. Due to operational imbalances, existing defense methods are inadequate against autonomous cyberattacks. Finally, we outline future research directions and potential defensive strategies for legacy network systems.