Many NLP tasks suffer from limited aligned supervision in the target domain. Historical cipher decryption represents an extreme case: aligned plaintext–ciphertext pairs are scarce, access to decrypted archives is restricted, and prior work often relies on synthetic data that is neither released nor evaluated for realism. This limits reproducibility and obscures whether models trained on artificial benchmarks transfer to archival conditions. We introduce HistCiph, the first publicly available multilingual collection of historically grounded plaintext–ciphertext datasets for classical ciphers. Spanning ten languages and multiple centuries, the collection combines diachronically balanced historical plaintext with independently generated homophonic substitution keys and controlled transcription noise. Synthetic generation is explicitly constrained by documented properties of historical ciphers, including multi-homophone allocation and variable-length codes. We validate the datasets using information-theoretic diagnostics—entropy, redundancy, frequency masking, and unicity distance—showing that ciphertext distributions approach theoretical bounds while preserving cross-linguistic variation. HistCiph provides a reproducible benchmark for neural decryption and alignment, and illustrates a principled framework for empirically grounded synthetic data generation in low-resource NLP.