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.
7.山西师范大学文学院“学雷锋 送温暖”小组
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WHAT THIS DOESN'T COVER:
公募基金GEO的落地，是对现有非结构化投研内容进行“AI友好型”与“合规前置化”的深度改造。具体实操路径可拆解为三个维度：
And it needs deep extensibility. Users and AI must be able to write extensions that access the system’s internals, building custom tools, automation, and domain-specific reasoning engines. This is already happening: AI agents build their own proof strategies on top of the platform. The platform adapts to its users, not the other way around.