AI-Accelerated Brute Force Cryptanalysis

TL;DR

The paper discusses how AI enhances brute-force cryptanalysis by recognizing patterns in seemingly random data, challenging current cryptographic security assumptions.

Contribution

It introduces AI-accelerated brute-force attacks and proposes a new security class called Pattern Devoid Cryptography.

Findings

AI can identify patterns in random-looking plaintexts to accelerate key discovery

Current cryptographic schemes like NIST PQC are vulnerable to AI-accelerated attacks

Strategies like non-trivial ciphertexts and variable key sizes can defend against these attacks

Abstract

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.