Recursive self-improvement (RSI) is a feedback loop in which an AI system tries to improve its own performance by evaluating outputs, generating revisions, and using those results to produce the next version. In current practice, RSI is usually bounded: the system may self-review, self-test, or refine code and prompts, but it is not autonomous superintelligence. The security value of understanding RSI is that every loop can also amplify mistakes.
In cyber security, RSI matters because weak evaluators, poisoned training data, prompt injection, or unsafe tool output can make automated refinement worse over time. An attacker may try to steer the loop toward insecure code, bad policy decisions, or data leakage by influencing the system’s feedback channel. Defenders reduce this risk with sandboxing, approval gates, version tracking, independent tests, and human review for high-impact changes. The core lesson is simple: a self-improving system is only as safe as the controls around its feedback loop.