AI-Accelerated Brute Force Cryptanalysis
This work identifies a fundamental vulnerability in cryptographic systems, potentially affecting all current encryption standards and demanding a re-evaluation of security postures.
The paper introduces a novel AI-accelerated brute force attack that uses machine learning to infer patterns from wrong plaintexts, enabling faster key recovery. This challenges the foundational assumption of modern cryptography and suggests that NIST PQC standards are vulnerable, requiring a new security class called Pattern Devoid Cryptography.
Modern cryptography is hinged on "not learning from mistakes": trying numerous wrong keys, should not help one identify the right key. Indeed, it worked -- until recently when the surprising power of AI to see pattern in apparent randomness has turned the 'wrong plaintexts' generated by the 'wrong key' into productive inferential input. Crunching through these random-looking plaintext candidates AI can de-flatten the probability curve over the remaining key space. The more spiked this curve, the faster the ciphertext is defeated. This new attack vector demands a thorough review of our cryptographic security posture. NIST PQC is not immunized against AI-Accelerated Brute Force attack. Defense is rooted in non-trivial ciphertexts, in unilateral randomness, and in variable key size. This points to a new security class: Pattern Devoid Cryptography which is to be added into the toolbox used by the cyber security community.