Large Language Models for Cyber Security

📄 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.

🔍 Key Points

Introduction of the χmera framework as the first principled attack evaluation method on LLM factual memory under prompt injection in adversarial scenarios.
Demonstration of various MitM attacks categorized into α, β, and γ types, showcasing how even trivial instruction-based attacks can successfully deceive LLMs with notable accuracy.
Empirical evidence showing high uncertainty levels in LLM responses during attacks, which can be leveraged to build a defense mechanism using machine learning classifiers to alert users of potentially manipulated responses.
Release of a novel factually adversarial dataset containing 3000 samples designed to benchmark and facilitate further research in adversarial vulnerabilities within LLMs.
High performance of Random Forest classifiers (up to ~96% AUC) in detecting attacked queries using uncertainty metrics, establishing a pathway towards user safety in LLM applications.

💡 Why This Paper Matters

This paper is crucial as it addresses the significant vulnerability of LLMs to adversarial attacks, particularly in contexts where factual accuracy is paramount, such as in information retrieval and question-answering systems. By unveiling specific weaknesses and developing the χmera framework, the authors pave the way for future research aimed at enhancing the robustness and trustworthiness of AI systems, thus contributing to safer AI deployment in critical applications.

🎯 Why It's Interesting for AI Security Researchers

This research holds great interest for AI security researchers as it delineates a clear framework for understanding and evaluating adversarial threats in LLMs, a topic of growing concern with the increasing reliance on these models for critical tasks. The findings not only highlight existing vulnerabilities but also propose empirical methods for detection and mitigation, guiding future research and practical implementations aimed at strengthening AI security.

Large Language Models for Cyber Security

📄 Abstract

🔍 Key Points

💡 Why This Paper Matters

🎯 Why It's Interesting for AI Security Researchers

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