AI Grinding for Fun and Cryptanalysis

Abstract

We present an autonomous cryptanalysis workflow in which agents generate, test, and refine hypotheses before human review. The autonomous stage returns reproducible candidates with exact witnesses, controls, code, and run records. A researcher then decides whether the evidence establishes a break, defect, or coverage gap. Two failure modes recur. First, a public algebraic map or input representation erases or exposes a relation that a construction must hide. Examples include multiplication by zero, boundary coefficients of a polynomial product, quotients, characters, Schur squares, and variable-length byte encodings without boundaries. Second, a simulator, error law, or parameter certification uses a distribution different from the one claimed. Several targets fail in both ways. Every result has an exact witness and a discriminating control; every stated boundary has a proof. Three further targets yielded no attack but support narrower guarantees than a generic reading suggests. Eight published constructions fail at stated parameters or claims. A Ring-LWR commitment opens to every message with probability one. One ciphertext reveals two middle-product encryption rows. A lattice e-voting protocol loses receipt-freeness. A permutation-recovery attack against updatable encryption extends by linear algebra to the old decryption key. An explicit normal basis splits a degree-63 instance into seven degree-nine instances. A signature hash outside the lattice setting maps two printable equal-length messages to the same digest. A rerandomisable scheme's accept bit is a threshold oracle on its decryption noise. Separately, a group-ring decision claim and a multivariate MinRank hardening fail at the assumption or accounting level rather than as complete construction breaks. Each failure occurs one level above its supporting assumption.