# Security The FLock system is designed to be resilient against a wide array of potential attacks, ensuring the integrity and security of its operations. For instance, Sybil attacks are mitigated by requiring participants to stake a minimum amount of assets, making it costly to control multiple identities. Furthermore, validators are kept unaware of the model origins, reducing the risk of collusion, and only the top-performing training nodes and validators receive rewards, discouraging poor performance and manipulation. To mitigate DoS attacks, the Flock system implements rate limiting, preventing any single participant from monopolising resources. Free-rider attacks are addressed by rewarding only the top contributors, ensuring that participants who do not genuinely contribute cannot benefit. The use of dual datasets (Dataset A and B) in evaluations prevents lookup attacks, as optimizing for one dataset does not guarantee success in the other. For FL model poisoning attacks, a majority voting system and slashing mechanism protect the model's integrity, punishing malicious actors and discouraging future attempts. These measures collectively fortify the FLock system against a range of threats, promoting a secure and reliable environment for participants. --- # 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.flock.io/flock-tokenomics/overview/security.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.