A shout of ‘racist’ could also be heard during the segment at France’s version of the Oscars
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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If you are familiar with SMT-LIB you may notice that both the re.from_ecma2020 function and the single-quote string notation ('regex') are nonstandard additions in Ostrich. That's because the regexes in SMT-LIB are textbook regexes without most of the features of full perl-compatible regular expressions, and because they end up needing a lot of escaping in practical use, some of which is nontrivial.
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