Cracking Mixed-Shift Ciphers: How AI Perplexity Scoring Achieved 100% Accuracy

DecipherLM demonstrates how LLMs can be repurposed for cryptanalysis when properly constrained with perplexity scoring and a novel 'Contextual Consensus' architecture that combines global and local pattern analysis. This breakthrough is significant because it solves limitations of traditional frequency analysis on short texts while managing inherent LLM weaknesses like hallucination and noise sensitivity. For developers and security researchers, this case study provides practical insights into iterative AI problem-solving and model selection for specialized cryptographic tasks.