# Information Security Based on LLM Approaches: A Review

arXiv: https://arxiv.org/abs/2507.18215 · HTML: https://mlsanity.com/paper/2507.18215/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 60 references that are in the map.
- First posted on arXiv 24 Jul 2025.

## Builds on (most cited first, 18 of 18)

- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [A Survey on Evaluation of Large Language Models](https://mlsanity.com/paper/2307.03109/) (2023)
- [Transformers in Vision: A Survey](https://mlsanity.com/paper/2101.01169/) (2021)
- [A Comprehensive Overview of Large Language Models](https://mlsanity.com/paper/2307.06435/) (2023)
- [Large Language Model (LLM) for Telecommunications: A Comprehensive Survey on Principles, Key Techniques, and Opportunities](https://mlsanity.com/paper/2405.10825/) (2024)
- [Deep Learning in Deterministic Computational Mechanics](https://mlsanity.com/paper/2309.15421/) (2023)
- [Assessing LLMs in Malicious Code Deobfuscation of Real-world Malware Campaigns](https://mlsanity.com/paper/2404.19715/) (2024)
- [A Survey of Malware Detection Using Deep Learning](https://mlsanity.com/paper/2407.19153/) (2024)
- [Unveiling Wash Trading in Popular NFT Markets](https://mlsanity.com/paper/2403.10361/) (2024)
- [StateGuard: Detecting State Derailment Defects in Decentralized Exchange Smart Contract](https://mlsanity.com/paper/2405.09181/) (2024)
- [DeFiTail: DeFi Protocol Inspection through Cross-Contract Execution Analysis](https://mlsanity.com/paper/2405.11035/) (2024)
- [Malicious Code Detection in Smart Contracts via Opcode Vectorization](https://mlsanity.com/paper/2504.12720/) (2025)
- [GasTrace: Detecting Sandwich Attack Malicious Accounts in Ethereum](https://mlsanity.com/paper/2405.19971/) (2024)
- [Detecting Malicious Accounts in Web3 through Transaction Graph](https://mlsanity.com/paper/2410.20713/) (2024)
- [SmartBugBert: BERT-Enhanced Vulnerability Detection for Smart Contract Bytecode](https://mlsanity.com/paper/2504.05002/) (2025)
- [Enhancing Smart Contract Vulnerability Detection in DApps Leveraging Fine-Tuned LLM](https://mlsanity.com/paper/2504.05006/) (2025)
- [Exploring Vulnerabilities and Concerns in Solana Smart Contracts](https://mlsanity.com/paper/2504.07419/) (2025)
- [Guardians of the Ledger: Protecting Decentralized Exchanges from State Derailment Defects](https://mlsanity.com/paper/2411.18935/) (2024)

## Built on by (most cited first, 1 of 1)

- [A Novel GPT-Based Framework for Anomaly Detection in System Logs](https://mlsanity.com/paper/2510.16044/) (2025)

## Abstract (arXiv)

Information security is facing increasingly severe challenges, and traditional protection means are difficult to cope with complex and changing threats. In recent years, as an emerging intelligent technology, large language models (LLMs) have shown a broad application prospect in the field of information security. In this paper, we focus on the key role of LLM in information security, systematically review its application progress in malicious behavior prediction, network threat analysis, system vulnerability detection, malicious code identification, and cryptographic algorithm optimization, and explore its potential in enhancing security protection performance. Based on neural networks and Transformer architecture, this paper analyzes the technical basis of large language models and their advantages in natural language processing tasks. It is shown that the introduction of large language modeling helps to improve the detection accuracy and reduce the false alarm rate of security systems. Finally, this paper summarizes the current application results and points out that it still faces challenges in model transparency, interpretability, and scene adaptability, among other issues. It is necessary to explore further the optimization of the model structure and the improvement of the generalization ability to realize a more intelligent and accurate information security protection system.