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aiXamine: Cross-dimensional LLM safety/security/privacy evaluation study
- Engineer — Learn: The findings — that safety alignment increases over-refusal (safety tax), privacy is near-orthogonal to other trustworthiness dimensions, and distillation degrades robustness — are useful mental models for engineers selecting or evaluating LLMs in their stack, though no immediate system changes are required.
- SOC/IR — Skip
- Leader — Learn: The finding that strong alignment does not protect privacy, and that distilled models suffer robustness collapse, provides empirical grounding for AI governance decisions and risk conversations with leadership about LLM adoption — useful for future board decks but no same-week action needed.
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