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Safety guard evaluations can be misleadingly inflated by superficial cues, with simple edits to tool names or command structure significantly altering measured performance and agent behavior.
Agent guards designed for English requests exhibit significant safety failures when faced with multilingual inputs, with smaller models and specialized guards showing the most pronounced language-dependent biases.
Binary classifiers misclassify nearly 40% of AI agents as humans, but a simple three-class framework achieves perfect detection across all tested evasion strategies.