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Benchmarking Large Language Models for Cryptanalysis and Mismatched-Generalization

Authors: Utsav Maskey, Chencheng Zhu, Usman Naseem

Published: 2025-05-30

arXiv ID: 2505.24621v1

Added to Library: 2025-06-02 03:01 UTC

Red Teaming

📄 Abstract

Recent advancements in Large Language Models (LLMs) have transformed natural language understanding and generation, leading to extensive benchmarking across diverse tasks. However, cryptanalysis a critical area for data security and encryption has not yet been thoroughly explored in LLM evaluations. To address this gap, we evaluate cryptanalytic potential of state of the art LLMs on encrypted texts generated using a range of cryptographic algorithms. We introduce a novel benchmark dataset comprising diverse plain texts spanning various domains, lengths, writing styles, and topics paired with their encrypted versions. Using zero-shot and few shot settings, we assess multiple LLMs for decryption accuracy and semantic comprehension across different encryption schemes. Our findings reveal key insights into the strengths and limitations of LLMs in side-channel communication while raising concerns about their susceptibility to jailbreaking attacks. This research highlights the dual-use nature of LLMs in security contexts and contributes to the ongoing discussion on AI safety and security.

🔍 Key Points

  • The introduction of a novel benchmark dataset comprising diverse plain texts and their encrypted versions generated through various cryptographic algorithms to evaluate the cryptanalytic potential of state-of-the-art Large Language Models (LLMs).
  • A detailed assessment of multiple LLMs in zero-shot and few-shot settings to measure their decryption accuracy and semantic comprehension across different encryption schemes and complexities, revealing significant performance challenges in more complex ciphers.
  • Identification of the susceptibility of LLMs to jailbreaking attacks, emphasizing the importance of partial comprehension in the context of AI safety by demonstrating how LLMs may be exploited even when not fully decrypting the texts.
  • Findings suggest that LLM performance is highly dependent on the presence of specific ciphers in pre-training corpora, indicating limitations in generalization capabilities for unfamiliar encryption methods.
  • The paper provides useful insights that can guide future research into enhancing LLMs' security and robustness, recommending necessary adjustments in LLM safeguard mechanisms to mitigate vulnerabilities.

💡 Why This Paper Matters

This paper is significant because it addresses a critical gap in the evaluation of LLMs—cryptanalysis. By systematically benchmarking the cryptanalytic capabilities of different LLMs against a comprehensive dataset, it not only shines a light on their potential vulnerabilities but also offers valuable insights for improving AI safety protocols. The findings underscore the crucial implications of AI's dual-use nature in security contexts where model understanding or manipulation could lead to serious security concerns.

🎯 Why It's Interesting for AI Security Researchers

AI security researchers would be particularly interested in this paper, as it investigates the intersection of large language models and cryptanalysis, a field increasingly relevant in today's digital landscape. The findings reveal not just the current limitations of LLMs in handling encrypted data, but also their potential exploitation through specific attack vectors. As AI models become more common in various applications, understanding their weaknesses becomes paramount for developing more secure AI systems.

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