# From Texts to Shields: Convergence of Large Language Models and Cybersecurity arXiv: https://arxiv.org/abs/2505.00841 ยท HTML: https://mlsanity.com/paper/2505.00841/ ## Key facts - Cited by 4 papers in the map (data through 14 Oct 2026). - Builds on 35 of its 69 references that are in the map. - First posted on arXiv 1 May 2025. ## Builds on (most cited first, 30 of 35) - [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020) - [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023) - [Emergent Abilities of Large Language Models](https://mlsanity.com/paper/2206.07682/) (2022) - [Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Survey](https://mlsanity.com/paper/2111.01243/) (2021) - [Adaptive Attacks Break Defenses Against Indirect Prompt Injection Attacks on LLM Agents](https://mlsanity.com/paper/2503.00061/) (2025) - [LogBERT: Log Anomaly Detection via BERT](https://mlsanity.com/paper/2103.04475/) (2021) - [LLM Agents can Autonomously Hack Websites](https://mlsanity.com/paper/2402.06664/) (2024) - [SecureBERT: A Domain-Specific Language Model for Cybersecurity](https://mlsanity.com/paper/2204.02685/) (2022) - [Generating Fake Cyber Threat Intelligence Using Transformer-Based Models](https://mlsanity.com/paper/2102.04351/) (2021) - [Log Parsing with Prompt-based Few-shot Learning](https://mlsanity.com/paper/2302.07435/) (2023) - [Looking Beyond IoCs: Automatically Extracting Attack Patterns from External CTI](https://mlsanity.com/paper/2211.01753/) (2022) - [The Confluence of Networks, Games and Learning](https://mlsanity.com/paper/2105.08158/) (2021) - [URLTran: Improving Phishing URL Detection Using Transformers](https://mlsanity.com/paper/2106.05256/) (2021) - [The Looming Threat of Fake and LLM-generated LinkedIn Profiles: Challenges and Opportunities for Detection and Prevention](https://mlsanity.com/paper/2307.11864/) (2023) - [AGIR: Automating Cyber Threat Intelligence Reporting with Natural Language Generation](https://mlsanity.com/paper/2310.02655/) (2023) - [Conjectural Online Learning with First-order Beliefs in Asymmetric Information Stochastic Games](https://mlsanity.com/paper/2402.18781/) (2024) - [Automated Security Response through Online Learning with Adaptive Conjectures](https://mlsanity.com/paper/2402.12499/) (2024) - [Self-Adaptive Driving in Nonstationary Environments through Conjectural Online Lookahead Adaptation](https://mlsanity.com/paper/2210.03209/) (2022) - [Scenario-Agnostic Zero-Trust Defense with Explainable Threshold Policy: A Meta-Learning Approach](https://mlsanity.com/paper/2303.03349/) (2023) - [Digital Twin-Enhanced Wireless Indoor Navigation: Achieving Efficient Environment Sensing with Zero-Shot Reinforcement Learning](https://mlsanity.com/paper/2306.06766/) (2023) - [On the Uses of Large Language Models to Interpret Ambiguous Cyberattack Descriptions](https://mlsanity.com/paper/2306.14062/) (2023) - [Using LLMs to Automate Threat Intelligence Analysis Workflows in Security Operation Centers](https://mlsanity.com/paper/2407.13093/) (2024) - [Meta Stackelberg Game: Robust Federated Learning against Adaptive and Mixed Poisoning Attacks](https://mlsanity.com/paper/2410.17431/) (2024) - [Symbiotic Game and Foundation Models for Cyber Deception Operations in Strategic Cyber Warfare](https://mlsanity.com/paper/2403.10570/) (2024) - [A First Order Meta Stackelberg Method for Robust Federated Learning](https://mlsanity.com/paper/2306.13800/) (2023) - [Decision-Dominant Strategic Defense Against Lateral Movement for 5G Zero-Trust Multi-Domain Networks](https://mlsanity.com/paper/2310.01675/) (2023) - [Multi-level Traffic-Responsive Tilt Camera Surveillance through Predictive Correlated Online Learning](https://mlsanity.com/paper/2408.02208/) (2024) - [Understanding and Measuring Robustness of Multimodal Learning](https://mlsanity.com/paper/2112.12792/) (2021) - [Designing Policies for Truth: Combating Misinformation with Transparency and Information Design](https://mlsanity.com/paper/2304.08588/) (2023) - [A hybrid LLM workflow can help identify user privilege related variables in programs of any size](https://mlsanity.com/paper/2403.15723/) (2024) ## Built on by (most cited first, 4 of 4) - [Internet of Agentic AI: Incentive-Compatible Distributed Teaming and Workflow](https://mlsanity.com/paper/2602.03145/) (2026) - [Online Incident Response Planning under Model Misspecification through Bayesian Learning and Belief Quantization](https://mlsanity.com/paper/2508.14385/) (2025) - [Agentic AI for Cyber Resilience: A New Security Paradigm and Its System-Theoretic Foundations](https://mlsanity.com/paper/2512.22883/) (2025) - [Guarding Against Malicious Biased Threats (GAMBiT) Experiments: Revealing Cognitive Bias in Human-Subjects Red-Team Cyber Range Operations](https://mlsanity.com/paper/2508.20963/) (2025) ## Abstract (arXiv) This report explores the convergence of large language models (LLMs) and cybersecurity, synthesizing interdisciplinary insights from network security, artificial intelligence, formal methods, and human-centered design. It examines emerging applications of LLMs in software and network security, 5G vulnerability analysis, and generative security engineering. The report highlights the role of agentic LLMs in automating complex tasks, improving operational efficiency, and enabling reasoning-driven security analytics. Socio-technical challenges associated with the deployment of LLMs -- including trust, transparency, and ethical considerations -- can be addressed through strategies such as human-in-the-loop systems, role-specific training, and proactive robustness testing. The report further outlines critical research challenges in ensuring interpretability, safety, and fairness in LLM-based systems, particularly in high-stakes domains. By integrating technical advances with organizational and societal considerations, this report presents a forward-looking research agenda for the secure and effective adoption of LLMs in cybersecurity.