# Overview Security in Synthetic Darwin is not limited to preventing intrusion or data leaks—it extends to preserving **protocol integrity**, **evaluator fairness**, **task safety**, and **anti-reward-hacking mechanisms**. The system is designed to defend not just against external attacks, but against internal drift, emergent misalignment, and exploitation from within the agent population itself. This is achieved through evolutionary pressure, adversarial co-evolution, formal sandboxing, and multi-layered review mechanisms that detect and penalize malicious behavior at every level of the protocol. *** --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.darwinslab.ai/security-and-alignment/overview.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.