Experiment Shows Naive LLM Code-Auditing Loops Destroy Correct Code
sebpaquet · x · 2026-08-09
The author points out that when LLMs audit code, they are essentially looking for patterns that look like bugs, which can often appear in perfectly correct code.
To demonstrate the consequences, an experiment was set up using a loop where a model audits and fixes an algorithm that was already mathematically proven correct. The result was complete degradation: in early iterations, the model broke the working logic by fixing non-existent bugs. Subsequent patches then introduced new errors, ultimately destroying the codebase. This proves that naive adversarial loops of AI code auditing and fixing will inevitably lead to failure.
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