# On Large Language Models in National Security Applications

arXiv: https://arxiv.org/abs/2407.03453 · HTML: https://mlsanity.com/paper/2407.03453/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 89 references that are in the map.
- First posted on arXiv 3 Jul 2024.

## Builds on (most cited first, 28 of 28)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [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)
- [This Looks Like That: Deep Learning for Interpretable Image Recognition](https://mlsanity.com/paper/1806.10574/) (2018)
- [Towards Reasoning in Large Language Models: A Survey](https://mlsanity.com/paper/2212.10403/) (2022)
- [Large Language Models: A Survey](https://mlsanity.com/paper/2402.06196/) (2024)
- [Are Emergent Abilities of Large Language Models a Mirage?](https://mlsanity.com/paper/2304.15004/) (2023)
- [Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Survey](https://mlsanity.com/paper/2111.01243/) (2021)
- [Benchmarking Large Language Models for News Summarization](https://mlsanity.com/paper/2301.13848/) (2023)
- [Can LLM-Generated Misinformation Be Detected?](https://mlsanity.com/paper/2309.13788/) (2023)
- [Generative Language Models and Automated Influence Operations: Emerging Threats and Potential Mitigations](https://mlsanity.com/paper/2301.04246/) (2023)
- [BooookScore: A systematic exploration of book-length summarization in the era of LLMs](https://mlsanity.com/paper/2310.00785/) (2023)
- [Is LLM-as-a-Judge Robust? Investigating Universal Adversarial Attacks on Zero-shot LLM Assessment](https://mlsanity.com/paper/2402.14016/) (2024)
- [Automatic and Universal Prompt Injection Attacks against Large Language Models](https://mlsanity.com/paper/2403.04957/) (2024)
- [Are Emergent Abilities in Large Language Models just In-Context Learning?](https://mlsanity.com/paper/2309.01809/) (2023)
- [Spinning Language Models: Risks of Propaganda-As-A-Service and Countermeasures](https://mlsanity.com/paper/2112.05224/) (2021)
- [Escalation Risks from Language Models in Military and Diplomatic Decision-Making](https://mlsanity.com/paper/2401.03408/) (2024)
- [From task structures to world models: What do LLMs know?](https://mlsanity.com/paper/2310.04276/) (2023)
- [DELL: Generating Reactions and Explanations for LLM-Based Misinformation Detection](https://mlsanity.com/paper/2402.10426/) (2024)
- [DisasterResponseGPT: Large Language Models for Accelerated Plan of Action Development in Disaster Response Scenarios](https://mlsanity.com/paper/2306.17271/) (2023)
- [Human-like Few-Shot Learning via Bayesian Reasoning over Natural Language](https://mlsanity.com/paper/2306.02797/) (2023)
- [A Survey on Game Playing Agents and Large Models: Methods, Applications, and Challenges](https://mlsanity.com/paper/2403.10249/) (2024)
- [Does Human Collaboration Enhance the Accuracy of Identifying LLM-Generated Deepfake Texts?](https://mlsanity.com/paper/2304.01002/) (2023)
- [AI-Powered Autonomous Weapons Risk Geopolitical Instability and Threaten AI Research](https://mlsanity.com/paper/2405.01859/) (2024)
- [COA-GPT: Generative Pre-trained Transformers for Accelerated Course of Action Development in Military Operations](https://mlsanity.com/paper/2402.01786/) (2024)
- [Fakes of Varying Shades: How Warning Affects Human Perception and Engagement Regarding LLM Hallucinations](https://mlsanity.com/paper/2404.03745/) (2024)
- [Stochastic LLMs do not Understand Language: Towards Symbolic, Explainable and Ontologically Based LLMs](https://mlsanity.com/paper/2309.05918/) (2023)
- [Open-Ended Wargames with Large Language Models](https://mlsanity.com/paper/2404.11446/) (2024)

## Built on by (most cited first, 19 of 19)

- [Measuring Free-Form Decision-Making Inconsistency of Language Models in Military Crisis Simulations](https://mlsanity.com/paper/2410.13204/) (2024)
- [Large Language Models in Politics and Democracy: A Comprehensive Survey](https://mlsanity.com/paper/2412.04498/) (2024)
- [Multi-level Value Alignment in Agentic AI Systems: Survey and Perspectives](https://mlsanity.com/paper/2506.09656/) (2025)
- [Military AI Needs Technically-Informed Regulation to Safeguard AI Research and its Applications](https://mlsanity.com/paper/2505.18371/) (2025)
- [MAD-Fact: A Multi-Agent Debate Framework for Long-Form Factuality Evaluation in LLMs](https://mlsanity.com/paper/2510.22967/) (2025)
- [Causes and Consequences of Representational Similarity in Machine Learning Models](https://mlsanity.com/paper/2505.13899/) (2025)
- [Managing Escalation in Off-the-Shelf Large Language Models](https://mlsanity.com/paper/2508.01056/) (2025)
- [Shall We Play a Game? Language Models for Open-ended Wargames](https://mlsanity.com/paper/2509.17192/) (2025)
- [DispatchMAS: Fusing taxonomy and artificial intelligence agents for emergency medical services](https://mlsanity.com/paper/2510.21228/) (2025)
- [Sovereign Large Language Models: Advantages, Strategy and Regulations](https://mlsanity.com/paper/2503.04745/) (2025)
- [Mitigating LLM Hallucinations with Knowledge Graphs: A Case Study](https://mlsanity.com/paper/2504.12422/) (2025)
- [Semantic Scheduling for LLM Inference](https://mlsanity.com/paper/2506.12204/) (2025)
- [Cross-Lingual Stability and Bias in Instruction-Tuned Language Models for Humanitarian NLP](https://mlsanity.com/paper/2510.22823/) (2025)
- [ROK Defense M&S in the Age of Hyperscale AI: Concepts, Challenges, and Future Directions](https://mlsanity.com/paper/2410.00367/) (2024)
- [Towards AI-Assisted Generation of Military Training Scenarios](https://mlsanity.com/paper/2511.07690/) (2025)
- [Data Analysis and Performance Evaluation of Simulation Deduction Based on LLMs](https://mlsanity.com/paper/2511.10651/) (2025)
- [Domain Adapted Large Language Models for Additive Manufacturing](https://mlsanity.com/paper/2603.22017/) (2026)
- [Application of LLMs to Threat Assessment of Foreign Peacekeeping Missions](https://mlsanity.com/paper/2606.27106/) (2026)
- [Quantization-Triggered Backdoors in Language Models: Cross-Quantizer Transferability and the Validation--Deployment Gap](https://mlsanity.com/paper/2608.27512/) (2026)

## Abstract (arXiv)

The overwhelming success of GPT-4 in early 2023 highlighted the transformative potential of large language models (LLMs) across various sectors, including national security. This article explores the implications of LLM integration within national security contexts, analyzing their potential to revolutionize information processing, decision-making, and operational efficiency. Whereas LLMs offer substantial benefits, such as automating tasks and enhancing data analysis, they also pose significant risks, including hallucinations, data privacy concerns, and vulnerability to adversarial attacks. Through their coupling with decision-theoretic principles and Bayesian reasoning, LLMs can significantly improve decision-making processes within national security organizations. Namely, LLMs can facilitate the transition from data to actionable decisions, enabling decision-makers to quickly receive and distill available information with less manpower. Current applications within the US Department of Defense and beyond are explored, e.g., the USAF's use of LLMs for wargaming and automatic summarization, that illustrate their potential to streamline operations and support decision-making. However, these applications necessitate rigorous safeguards to ensure accuracy and reliability. The broader implications of LLM integration extend to strategic planning, international relations, and the broader geopolitical landscape, with adversarial nations leveraging LLMs for disinformation and cyber operations, emphasizing the need for robust countermeasures. Despite exhibiting "sparks" of artificial general intelligence, LLMs are best suited for supporting roles rather than leading strategic decisions. Their use in training and wargaming can provide valuable insights and personalized learning experiences for military personnel, thereby improving operational readiness.