arXiv:2505.24621v3 Announce Type: replace 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 its connection to LLMs' generalization abilities - remains underexplored in LLM evaluations. To address this gap, we evaluate the cryptanalytic potential of state-of-the-art LLMs on ciphertexts produced by a range of cryptographic algorithms. We introduce a benchmark dataset of diverse plaintexts, spanning multi
The rapid advancement and widespread deployment of large language models necessitated an immediate exploration of their security implications, particularly in areas like cryptanalysis and side-channel vulnerabilities.
This research reveals emergent risks associated with LLMs that could compromise data security and highlights a new frontier for cybersecurity, impacting governments, corporations, and individuals.
The understanding of LLM capabilities is expanding beyond general tasks to include highly specialized and potentially adversarial domains like cryptanalysis, requiring new security protocols and evaluation methods for AI systems.
- · Cybersecurity firms
- · AI ethics and safety researchers
- · Organizations focused on secure AI development
- · Developers of unhardened LLMs
- · Organizations relying on traditional cryptographic assumptions alone
- · Data security without robust AI-aware defenses
LLMs will be increasingly scrutinized for their potential to be exploited or to become tools for sophisticated cyberattacks.
There will be a push for explainable AI and built-in security features in LLM development to mitigate these identified risks.
The intersection of AI and cryptography will become a major research and development area, potentially leading to 'AI-proof' cryptographic methods or entirely new paradigms of cyber defense.
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