On November 6, 2026, Google DeepMind announced that its AI agent, guided by human cryptography experts, had discovered two weaknesses in the 1024-bit RSA encryption system in just a few weeks. The research reveals that AI is fundamentally changing the timescale of cryptographic research.
Two Specific Findings
The research was led by DeepMind researchers Dj Dvijotham and Abhradeep Thakurta, in collaboration with University of California, San Diego and Inria cryptographer Nadia Heninger and Inria researcher Emmanuel Thomé. The study targeted 1024-bit RSA systems and found two qualitatively different types of weaknesses.
The first concerns digital signature forgery. RSA signature schemes add a structured block of “padding” before a message is signed. The AI agent found that when an attacker can control about half a block or more of the signature block, this padding protection becomes effectively useless—signature forgery is almost as easy as with bare RSA and no padding at all. In scenarios where the attacker controls only a small segment of data, such as a 32-byte digital fingerprint, the new algorithm generated by the AI agent is estimated theoretically to be about 1,600 times more efficient than the previous best method. The team also validated the approach in a sandbox on an RSA-1024 benchmark key for which a prior solution existed, and its implementation used 3.5 times less CPU compute than earlier academic work.
The second concerns a batch factoring algorithm. RSA security rests on the computational difficulty of factoring large integers. DeepMind’s AI agent found a batch algorithm with better asymptotic complexity than all known methods, reducing the average cost of factoring each additional 1024-bit key by about 30 times.
Both findings apply only to 1024-bit RSA. DeepMind explicitly stated that modern systems using 2048-bit or longer keys are unaffected, covering almost all mainstream application scenarios such as online banking, encrypted communications, and software update signing.
A Standard Abandoned for a Decade
1024-bit RSA is not an innocent victim. The U.S. National Institute of Standards and Technology (NIST) already listed it as “not recommended” in SP 800-131A, published in 2010, and formally prohibited it for new signatures and encryption in 2013. NIST assessed the security strength of 1024-bit RSA at 80 bits—below its required minimum of 112 bits. Public certificate authorities stopped issuing 1024-bit certificates years ago.
Globally, a hard-to-quantify number of embedded devices, industrial control systems, legacy server infrastructure, and outdated software still run 1024-bit RSA—systems whose update cycles are measured in years or even decades. DeepMind’s framing is that “AI can compress cryptanalysis work that would originally take years into weeks.”
The Time Window Is Narrowing
Traditional cryptographic research has a built-in safety buffer: it often takes years from the discovery of a theoretical weakness to the emergence of a practical attack tool. DeepMind’s experiment provides a new data point: under expert guidance, AI agents can test mathematical hypotheses in a highly parallel manner, far faster than teams of human mathematicians. The research team completed exploratory work in weeks that might previously have taken years. In the same period in 2026, researchers also published an attack study on the post-quantum cryptography candidate HAWK, and the cryptography field is coming under pressure on multiple fronts at once.
This means cryptographic standard migration plans need to be reassessed under a more aggressive threat timeline. NIST’s final IR 8547, released on October 27, 2026, set the deprecation date for all RSA and elliptic curve cryptography (ECC) at 2030 and the disallowance date at 2035. That timetable was developed assuming a human-driven pace of cryptanalysis. If AI raises that pace by an order of magnitude, whether the 2030 timetable is still sufficient needs to be discussed again.
The Real Role of AI Agents in Cryptographic Research
DeepMind’s formulation is that “AI agents accelerated cryptanalysis research under human guidance.” Pushmeet Kohli used the phrase “stress-testing cryptographic systems” in the announcement. Top cryptographers such as Heninger and Thomé played the role of direction-setters, while AI handled the heavy exploratory computation.
A large amount of work in cryptanalysis consists of “hypothesis testing”—proposing a mathematical path and then exhaustively verifying it. This kind of work is time-consuming and labor-intensive for humans, but it is precisely the type of work current AI agents excel at. The theoretical bottleneck is not AI’s comprehension, but whether human experts can propose the right research direction. This is a kind of “leverage amplification”—a team of excellent cryptographers using AI agents may produce output close to what a large research institution would produce over several years.
At the same time, this means offensive and defensive cryptographic research will accelerate at the same rate. The gap between nation-state and academic attack research capabilities may narrow as AI tools become more widespread.
Who Should Act Immediately
For operators still running 1024-bit RSA systems, this research has not actually created a new threat—it has merely made an existing theoretical threat more quantified and concrete. The real question is: have the inventory and migration of such systems been prioritized?
For cryptographic standards bodies, AI’s ability to accelerate cryptanalysis means there is a need to establish continuous AI-assisted stress-testing mechanisms, rather than waiting to respond only after passive weaknesses appear in standards. DeepMind framed this research as “a stress-testing methodology contributed to the cryptography community.”
For post-quantum cryptography migration plans, this finding reinforces the urgency of migration: not because RSA-2048 will be broken tomorrow, but because AI tools are shortening the time gap between “theoretical weakness” and “practical attack,” while existing migration timetables were built on assumptions of the old research pace.
Independent Assessment
Calling this discovery “breaking RSA” is an exaggeration; treating it as an academic exercise targeting only a deprecated standard is an underestimation. The most valuable contribution of DeepMind’s work is not the 1,600x figure itself, but the quantifiable benchmark it provides: under expert guidance, AI agents can compress the timescale of cryptanalysis from years to weeks.
This compression effect is the variable that truly needs to be incorporated into cryptographic security planning. Any security design based on the assumption that “such attacks are too time-consuming and practically infeasible” should re-examine whether that premise still holds.
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