Fenner’s cricket ground sits next door to Hughes Hall, where the Cricket Research Network held their third annual conference last week. The organisation, headed by Raf Nicholson, sometimes of this parish, is a place for cricket academics to exchange ideas, and the conference a chance for rest of us to put an ear to the door of data and detail.
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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Apache HttpClient 低于 5.2.1
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Listen to our full conversation here and read Tom’s main takeaways for pushing past bad decisions, big and small.
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