Large Language Models for Cyber Security

TL;DR

This paper explores how integrating Large Language Models into cybersecurity enhances detection, scalability, and adaptability, offering solutions like encrypted prompts to mitigate prompt injection attacks and improve traditional intrusion detection systems.

Contribution

It introduces a four-layered architecture for LLM integration into cybersecurity tools and demonstrates the effectiveness of encrypted prompts and decoupled models for improved security.

Findings

Encrypted prompts effectively mitigate prompt injection attacks.

LLM-enhanced tools are more accurate and scalable.

Decoupled model approach yields highest accuracy in IDS.

Abstract

This paper studies the integration off Large Language Models into cybersecurity tools and protocols. The main issue discussed in this paper is how traditional rule-based and signature based security systems are not enough to deal with modern AI powered cyber threats. Cybersecurity industry is changing as threats are becoming more dangerous and adaptive in nature by levering the features provided by AI tools. By integrating LLMs into these tools and protocols, make the systems scalable, context-aware and intelligent. Thus helping it to mitigate these evolving cyber threats. The paper studies the architecture and functioning of LLMs, its integration into Encrypted prompts to prevent prompt injection attacks. It also studies the integration of LLMs into cybersecurity tools using a four layered architecture. At last, the paper has tried to explain various ways of integration LLMs into traditional Intrusion Detection System and enhancing its original abilities in various dimensions. The key findings of this paper has been (i)Encrypted Prompt with LLM is an effective way to mitigate prompt injection attacks, (ii) LLM enhanced cyber security tools are more accurate, scalable and adaptable to new threats as compared to traditional models, (iii) The decoupled model approach for LLM integration into IDS is the best way as it is the most accurate way.