Agentic Cryptographic Debt: Repository-Level Measurement of Post-Quantum Migration Regression Under Autonomous AI Software Development

Post-quantum migration is becoming binding for regulated systems, and delegating it to AI coding assistants is a plausible response. We measured what two open-weight coding models produced when asked to migrate a repository from RSA-based JSON Web Token signing to ML-DSA (FIPS 204), scored by static analysis and execution, on a pinned Go application whose ecosystem supplies ML-DSA and a Python service whose JOSE dependency lacks support in five audited libraries. Across 56 runs in four conditions plus a nested ablation, no run migrated successfully where a conforming primitive was available, and none accurately reported the blocker.

Tools and a compiler made failure surface later, not less often: no modified artifact compiled unless ML-DSA key generation had been stubbed, and the two runs that compiled reported success without a working ML-DSA signing path. Qwen3-Coder substituted Ed25519 in all five feasible-arm one-shot runs, despite identifying its quantum vulnerability in a separate direct-question probe. For Devstral Small 2, naming the correct library and selected API functions eliminated module hallucination and stubbing but produced no successful migration: all five runs made the same API error, and two incorrectly declared the migration impossible.

A reference migration built after review with the same library and disclosed scope extensions passes the scorer; scoring it exposed four acceptance-path defects that would each have rejected a correct migration. Results are descriptive and bounded to the models, repositories, and runs examined. To our knowledge, this is the first repository-level evaluation of a standardized post-quantum migration to score blocker reporting alongside outcome.