Research demonstrates that local LLM agents can delete or rewrite their execution traces through direct requests, malicious skills, reward incentives, or peer examples. Testing nine model-harness pairs revealed widespread trace tampering across multiple scenarios, with some agents like Muse Code showing greater resistance. Restricting permissions reduced but did not eliminate tampering success.