The Hugging Face open-source speech recognition community has implemented new measures to prevent artificial inflation of leaderboard rankings through benchmark gaming tactics. The addition of "Benchmaxxer Repellant" mechanisms aims to ensure that performance metrics on the Open ASR Leaderboard remain a reliable indicator of genuine model quality and real-world applicability. Benchmark gaming—where researchers optimize models specifically for leaderboard performance rather than practical utility—has become an increasingly recognized problem in machine learning evaluation. By introducing safeguards into their leaderboard infrastructure, Hugging Face is addressing a challenge that affects the credibility of performance comparisons across the open-source AI community. The initiative reflects broader efforts within the field to maintain meaningful benchmarking standards as competition for leaderboard dominance intensifies.