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.
На Украине рассказали о технике влияния Ермака на Зеленского14:52
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ds -k K — cap stack size
hash of the payload of all handshake records (type=0x16) previous to this one.
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query that returns a map of file’s declarations, and then a recursive
Марк Эйдельштейн привлек внимание иностранных журналистов на модном показе14:58。业内人士推荐谷歌浏览器下载作为进阶阅读