The Double-Edged Sword Effect of Large Language Models in Cybersecurity and Governance Challenges

Original Author/Maintainer: Ari Cooper, Ryan Tran, John Winborne Source Platform: GitHub Original Title: cybersecurity-llm-research: The Dual-Use Nature of Large Language Models and the Need for Robust Governance Original Link: https://github.com/aricooper/cybersecurity-llm-research Publication Date: December 15, 2025

This study deeply explores the dual-use nature of large language models (LLMs) in cybersecurity—they can both enhance defense capabilities and be used for attacks. The study analyzes the performance of LLMs in scenarios such as CTF competitions, autonomous vulnerability exploitation, and threat detection from three dimensions: technical performance, government applications, and governance frameworks, and proposes multi-level governance strategies.

Large language models (LLMs) are changing the cybersecurity landscape at an unprecedented speed, and this change is bidirectional: on one hand, they provide powerful automated tools for defenders; on the other hand, they lower the technical threshold for attackers. This "dual-use" characteristic makes them one of the most controversial and urgent technical issues in the current cybersecurity field.

This study examines the problem from three interrelated perspectives: the technical performance of cutting-edge models in CTF environments, the application impact of LLM-driven workflows in government agencies (such as the U.S. Department of Homeland Security, DHS), and how emerging governance frameworks manage the risks of high-capability models.

The study investigates several recent academic studies evaluating the cybersecurity capabilities of LLMs, focusing on key benchmarks:

1. CTF-Know benchmark: A specially designed knowledge assessment framework that tests LLMs' mastery of knowledge in structured cybersecurity tasks. Results show that cutting-edge models perform well in conceptual understanding, but there are still obvious gaps in real-world vulnerability exploitation scenarios;
2. CTFAgent autonomous framework: A system that allows LLMs to participate in CTF competitions independently. Research indicates that LLMs can complete some simple tasks, but their planning ability and proficiency in tool usage are limited in complex multi-step attack chains;
3. Threat detection pipeline: LLMs show potential in analyzing security logs and identifying abnormal patterns, especially with unique advantages in processing unstructured data and generating human-readable security reports.

The study analyzes the current deployment status of LLMs in the cybersecurity workflows of government agencies. Taking the U.S. Department of Homeland Security as an example, LLMs are used for automated threat intelligence analysis, assisting malware classification, generating security incident reports, and code audit assistance.

However, deployment brings multiple risks: data exposure risk (sensitive data input into third-party LLM services or the security of internal model training data sources needs strict review), hallucination issues (generating seemingly reasonable but incorrect security recommendations), operational misalignment (deviation between model training objectives and security operation objectives), and adversarial abuse (malicious actors using LLMs to generate phishing emails, write malicious code, or perform automated vulnerability scanning).

The study integrates contemporary governance literature and proposes multi-level governance strategies:

• Technical level: Develop specialized security assessment benchmarks, establish red team testing standards, and implement a model capability classification system;
• Organizational level: Formulate internal usage policies, establish human-machine collaboration review mechanisms, and ensure that key decisions are ultimately made by humans;
• Policy level: Promote the formulation of industry standards, facilitate international coordination, and establish incentive mechanisms for responsible disclosure;
• Research level: Support adversarial machine learning research, explore the application of explainable AI in the security field, and develop more robust evaluation methods.

This study provides key insights for cybersecurity practitioners:

1. LLMs are powerful auxiliary tools but cannot replace human professional judgment. In key security decisions, LLM outputs should be regarded as references rather than instructions;
2. Organizations need to establish clear usage boundaries and review processes when adopting LLMs, especially in scenarios involving sensitive data and critical infrastructure;
3. Defenders need to accelerate their understanding and application of LLM technology, as attackers are already exploring its potential.

As LLMs are increasingly embedded in digital infrastructure, society needs to find a balance between innovation, security, and ethical management. Through a comprehensive perspective of technology, policy, and practice, this study provides a valuable analytical framework for this complex issue. For researchers and practitioners concerned about AI security, this is an area worthy of continuous attention.