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.
MinCaml, mlml, AQaml
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Блогеру Арсену Маркаряну дали срок14:50
Generative AI Use. Generative AI was used for labeling participants’ responses, developing Javascript for the survey, drafting code for data cleaning and formatting figures, and copyediting select sections of the manuscript. The authors maintain full responsibility for the integrity of the final content. The level of AI involvement was consistent with tasks typically performed by a research assistant.
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