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Google DeepMind's AI agents find two cheaper ways to attack legacy 1024-bit RSA
Working with cryptographers from UC San Diego and Inria, Google DeepMind's AI agents cut the CPU time of a known 1024-bit RSA signature forgery 3.5-fold and devised a batch factoring method that makes each extra key about 30 times cheaper in theory. DeepMind says 2048-bit RSA is unaffected.
Google DeepMind says AI agents working under human supervision have found two ways to make attacks on legacy 1024-bit RSA cheaper than previously assumed[3]. The work, announced by DeepMind VP Pushmeet Kohli[2], was done with cryptographers from UC San Diego and Inria and is set out in two working papers.
"In practice, these results only affect obsolete 1024-bit RSA systems, and modern encryption protocols, which almost universally use 2048-bit or larger keys, remain secure," Kohli said[3].
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Forging signatures with less compute
The first result concerns padding, the preset data many signature standards add to a message. DeepMind found that once an attacker controls roughly half of the signed block or more, padding adds no protection at all. That applies to the PKCS#1 v1.5 format used by the signing interface of hardware security modules[3].
In a sandboxed test, two AI agents re-ran an earlier academic forgery attack on the same 1024-bit key. They designed, ran and debugged the computation in under four days with about 20 messages from the human researcher, mostly approvals. The first forgery took 391 CPU core-years against 1,380 in the original study[3]. The key itself was not factored, which would take an estimated 500,000 to 1 million core-years.
Where the attacker controls less, such as a 32-byte hash, DeepMind found an algorithm it estimates is roughly 1,600 times better than the previous best against 1024-bit keys. That figure is theoretical. The attack also needs around 4 billion signing queries made in advance, does not recover the private key and fails against devices that hash messages internally[3].
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Factoring many keys at once
The second paper tackles factoring many RSA keys of the same size, where one large precomputation can be shared across all of them. It gives each target key its own "fan" of number fields fed by that shared work. With a precomputation budget of about one ordinary factorisation, each further 1024-bit key costs about 1/58 of a normal number field sieve run, roughly 30 times less than the previous best method, which had not improved since 1993[1].
The authors are clear about the limits. No fan factory has been implemented, the figures are leading-order estimates rather than running-time predictions, and the results do not threaten 2048-bit RSA. The analysis was developed with a two-agent AI harness, one model generating proofs and another verifying them, with the algebra machine-checked in the Lean 4 proof assistant[1].
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Why it matters
Neither result breaks anything in modern use, but both chip at assumptions about how hard old RSA keys are to attack, and the agents did most of the heavy lifting. "We cannot treat cryptographic hardness as a fixed assumption," Kohli said[3]. For anyone still running 1024-bit keys in signing hardware or older systems, it is another push to migrate.
Sources
Sources for this story.
- Fan Factories: Multiple Number Fields in Factorization Factories and Batch NFS (Google DeepMind working paper)storage.googleapis.com
- Pushmeet Kohli announcement: DeepMind AI agents find two weaknesses in legacy 1024-bit RSA (X)twitter.com
- Google DeepMind's AI Agents Make 1024-Bit RSA Keys Cheaper to Attack (SecurityWeek)securityweek.com
FAQ
Frequently asked questions.
01Did DeepMind break RSA?
No. No 1024-bit key was factored, and DeepMind says modern systems using 2048-bit or larger keys remain secure. The results only affect obsolete 1024-bit RSA.
02What did the agents actually run?
In a sandboxed test, two AI agents re-ran an earlier academic signature forgery on an already-solved 1024-bit benchmark key. The first forgery took 391 CPU core-years against 1,380 in the original study, a 3.5-fold cut that came from better implementation rather than a new algorithm.
03How much did the human researcher do?
The agents designed, ran and debugged the computation in under four days with about 20 messages from the human researcher, mostly approvals.
04What is the batch factoring result?
A theoretical method called a fan factory. After a shared precomputation costing about as much as factoring one key, each additional 1024-bit key costs roughly 30 times less than with the previous best method. It has not been implemented.
05What is the 1,600 times figure?
DeepMind's theoretical estimate of how much a new algorithm improves forgery when the attacker controls only a small part of the signed data, such as a 32-byte hash. It has not been run at cryptographic size.
06Who should care?
Anyone still running 1024-bit RSA, for example in older hardware security modules or signing systems. The signature attack needs about 4 billion signing queries made in advance and does not work on devices that hash messages internally.
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