AI Drives Breakthroughs Against Historic Ciphers
🔍 This post argues that AI language models and their DSP-like architectures are now good enough to break many older codes and ciphers by exploiting persistent statistical features. It explains how adaptive filters in current DNNs can lift plaintext signals from noisy ciphertext when keytexts are periodic or short, a common human failing in historical systems. The author warns that only ciphers without key periodicity or those requiring infeasible workloads can still offer meaningful security.
