Ciphey is an automated decryption tool that identifies and breaks ciphers, decodes encodings, and cracks hashes without requiring knowledge of the encryption method or key in advance.
The tool solves the problem of identifying unknown encryption schemes by combining multiple approaches: it uses natural language processing to recognize when decryption has succeeded, applies deep neural networks to classify cipher types, and attempts decryption across a range of known algorithms. Rather than requiring users to manually test different cipher families, Ciphey automates the reconnaissance and trial process, making it practical for penetration testing and capture-the-flag scenarios where the encryption method is unknown.
Ciphey suits security professionals, penetration testers, and CTF participants who encounter encrypted or encoded data without metadata about its origin. It is particularly valuable when speed matters and manual cipher identification would be time-consuming. The tool handles multiple input formats and can chain decryption attempts, so it works well in workflows where data may be encrypted multiple times or encoded in layers. Users should expect it to work best on text-based ciphers and encodings rather than modern authenticated encryption schemes designed to resist automated attacks.
The project shows active development with regular commits across its codebase. Work spans both the core decryption logic and the machine learning components that power cipher classification. The maintainers have integrated feedback from users and contributors, refining the tool's accuracy and expanding its cipher coverage. Development includes ongoing improvements to the natural language processing pipeline and the neural network models that identify encryption types.
