EvalShield: Secure LLM Benchmarking for Attack-Resilient Evaluations and Trustworthy Leaderboards
The stakes around large language model evaluations have never been higher. Model scores influence product roadmaps, research funding, procurement decisions, and—more often than we admit—public perception. But as benchmarks get popular, they also become targets. Test-set leakage, prompt injection, and targeted overfitting can quietly warp results, undermining trust in leaderboards and research claims. AI Safety…