Google DeepMind AI Agents Discover New Methods to Undermine 1024-Bit RSA Security
Google DeepMind's AI agents have identified two novel techniques that significantly reduce the computational cost of attacking 1024-bit RSA cryptosystems, impacting legacy security protocols.
Google DeepMind has announced a significant breakthrough in cryptanalysis, with its AI agents discovering two new methods that drastically lower the computational resources required to attack 1024-bit RSA encryption.
The research, a collaboration between Google DeepMind, UC San Diego, and Inria, suggests that previously estimated attack times for these older RSA systems are now considerably shorter. While this development poses a risk to obsolete 1024-bit RSA implementations, it is crucial to note that modern encryption standards, which widely employ 2048-bit or larger keys, remain unaffected and secure.
The first identified method targets vulnerabilities in digital signatures and padding, specifically within the PKCS#1 v1.5 format. Padding is a standard technique used to ensure messages meet a fixed block size, making forgery more difficult. However, the DeepMind research reveals that the effectiveness of padding is dependent on the proportion of the signed block controlled by an attacker. Once an attacker gains control of approximately half or more of the block, padding offers no significant protection.
In practical terms, this finding has implications for cryptographic devices like hardware security modules (HSMs) that utilize the PKCS#11 signing interface. In controlled tests, the AI agents were able to reduce the CPU time for a known forgery attack on a 1024-bit key by a factor of 3.5. The agents designed, executed, and debugged this complex computation in under four days, requiring minimal human intervention.
Even in scenarios where an attacker controls a smaller portion of the block, such as a 32-byte hash, DeepMind's AI developed an algorithm that is theoretically estimated to be roughly 1,600 times more efficient than previous best methods for attacking 1024-bit keys. It is important to highlight that this signature attack does not lead to the recovery of the private key itself, but rather enables forgery, and requires a substantial number of signing queries.
The second breakthrough focuses on the efficiency of factoring multiple RSA keys of the same size. The AI agents devised an algorithm that makes the cost of factoring each additional 1024-bit key approximately 30 times cheaper on average compared to prior approaches. This method assumes a significant upfront computational cost for factoring the first key, and it has not yet been implemented in practice.
Pushmeet Kohli, VP at Google DeepMind, emphasized the broader implications of this research, stating, "Together, these results show that in a new era of AI-enabled mathematics and computation, we cannot treat cryptographic hardness as a fixed assumption. Using AI to rigorously test our previously held assumptions will ensure that modern encryption stays secure."
This research underscores the evolving landscape of cybersecurity, where artificial intelligence is increasingly playing a dual role: both a tool for enhancing security and a potential enabler of sophisticated attacks. The findings serve as a critical reminder for organizations to migrate away from legacy cryptographic standards and ensure they are utilizing robust, modern encryption protocols.