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.
Start with how you talk about your own work. “Implemented feature X” doesn’t mean much. But “evaluated three approaches including an event-driven architecture and a custom abstraction layer, determined that a straightforward implementation met all current and projected requirements, and shipped in two days with zero incidents over six months”, that’s the same simple work, just described in a way that captures the judgment behind it. The decision not to build something is a decision, an important one! Document it accordingly.。关于这个话题，爱思助手下载最新版本提供了深入分析
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