.causal Cryptanalysis: Black-Box Security Margins and Blind Cipher Identification

We demonstrate that deterministic causal graph inference over black-box cipher output yields a universal framework for automated security margin analysis and cipher architecture identification. Using the .causal binary format for structured knowledge graph inference, we introduce CASI (Causal Amplification Security Index), a single-number metric that quantifies the structural weakness of reduced-round cipher output relative to the full-round baseline. Across six cipher families spanning four architectural classes — ARX stream (ChaCha20, Salsa20), SPN block (AES-128), ARX Feistel (Speck 32/64), Feistel with key-dependent S-boxes (Blowfish), and Feistel with fixed S-boxes (3DES) — CASI detection frontiers match published cryptanalysis results. On Speck 32/64, the CASI frontier aligns with Gohr's neural distinguisher (CRYPTO 2019) within one round, achieved without training, without GPU computation, and without cipher-specific tuning. Furthermore, .causal graph topology enables correct identification of all five architecture classes from reduced-round output alone. Key results: 6 ciphers × 28 round configurations analyzed; CASI frontier matches Gohr within 1 round on Speck 32/64; 100% blind architecture classification (5 classes); same 17 extraction strategies across all ciphers with no cipher-specific tuning. All code, cipher implementations, and generated .causal files are included in this deposit.