Plug-and-Play RSI Harness Module: Empirical Validation of a Recursive Self-Improvement Safety Harness

We report empirical validation results for a plug-and-play recursive self-improvement (RSI) harness module designed to safely contain, evaluate, and integrate AI-generated code mutations. The module was subjected to a five-phase test suite comprising nine distinct validation scenarios. Key findings include: (1) robust API-client integration and graceful degradation on malformed LLM payloads; (2) zero sandbox escapes after patching a critical AST-sanitization vulnerability; (3) end-to-end validation of the recursive improvement loop; (4) automatic rollback recovery from AI-induced system crashes; and (5) viable meta-mutation capability with safe subprocess containment. A 10-generation empirical run demonstrated non-regressive fitness improvement (score: 0.905 → 1.780) with active statistical noise rejection. The degenerate-seed recovery test exposed a known limitation of stochastic-only repair. All validation was conducted in an isolated Python sandbox without live large-language-model integration.