It needs the largest possible library of formalized knowledge to build on. Verifying software is mathematics: the same reasoning that proves a theorem in abstract algebra proves that a cryptographic library correctly implements its specification. A platform that serves mathematicians and engineers is not a compromise. It is the recognition that rigorous reasoning is one discipline, whether applied to prime numbers or protocol correctness.
The threat extends beyond accidental errors. When AI writes the software, the attack surface shifts: an adversary who can poison training data or compromise the model’s API can inject subtle vulnerabilities into every system that AI touches. These are not hypothetical risks. Supply chain attacks are already among the most damaging in cybersecurity, and AI-generated code creates a new supply chain at a scale that did not previously exist. Traditional code review cannot reliably detect deliberately subtle vulnerabilities, and a determined adversary can study the test suite and plant bugs specifically designed to evade it. A formal specification is the defense: it defines what “correct” means independently of the AI that produced the code. When something breaks, you know exactly which assumption failed, and so does the auditor.
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