AI-Accelerated Brute Force Cryptanalysis

Authors: Gideon Samid

arXiv · PDF

Summary

This paper argues that AI can accelerate brute-force cryptanalysis by learning patterns in the random-looking plaintexts generated by wrong decryption keys, thereby "flattening" the remaining key-space probability curve and speeding up key search. The author claims this threatens modern cryptography's assumption that trying wrong keys yields no information, suggests that NIST post-quantum crypto (PQC) is not immune, and proposes "Pattern Devoid Cryptography" as a defense—using non-trivial ciphertexts, unilateral randomness, and variable key sizes to deny AI pattern-recognition opportunities.

Main takeaways:

• Claims AI can find patterns in the "wrong plaintext" candidates produced by incorrect keys during brute-force search, making key search faster.
• Argues this violates cryptography's core assumption: "not learning from mistakes" (i.e., wrong keys should yield no information).
• Suggests NIST post-quantum cryptography (PQC) is vulnerable to this AI-accelerated brute force.
• Proposes "Pattern Devoid Cryptography" as a defense: ciphertexts should be non-trivial, use unilateral randomness, and support variable key sizes.
• Calls for a thorough review of cryptographic security posture in light of AI's pattern-recognition capabilities.

Relevance

No connection to my LLM persona or midtraining work—this is about cryptographic key search, not language model behavior. Included because it's a speculative claim about AI's impact on cryptography with no overlap with behavioral installation or persona geometry.

Abstract

arXiv:2605.08690v1 Announce Type: new 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.