LLMs can unmask pseudonymous users at scale with surprising accuracy

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Consider an example. An AI rewrites a TLS library. The code passes every test. But the specification requires constant-time execution: no branch may depend on secret key material, no memory access pattern may leak information. The AI’s implementation contains a subtle conditional that varies with key bits, a timing side-channel invisible to testing, invisible to code review. A formal proof of constant-time behavior catches it instantly. Without the proof, that vulnerability ships to production. Proving such low-level properties requires verification at the right level of abstraction, which is why the platform must support specialized sublanguages for reasoning about timing, memory layout, and other hardware-level concerns.