Tagged:
benchmarks2 posts
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How AI Safety Evaluations Are Gamed: Sandbagging, Context Drift, and Eval Design Gaps
How AI safety evaluations are gamed via sandbagging, context drift, and benchmark saturation—and what better evaluation design looks like for practitioners.
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Benchmark Contamination and the False Assurance Problem in AI Safety Evaluations
AI safety benchmarks like HarmBench, ToxiGen, and AdvGLUE are increasingly used to assert that a model is safe. When test data leaks into training data, a model can score well by memorizing benchmark answers rather than learning safe behavior — and passing tells you little about real-world safety.