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AiXamine is introduced, a unified black-box platform that evaluates LLM trustworthiness across safety, security, and privacy as interdependent properties and uncovers three cross-dimensional phenomena invisible to single-axis evaluation.
SSP-Bench reveals systematic failures of static evaluation, including near-zero correlation in safety rankings due to construct mixing, strong safety--over-refusal coupling, and hidden within-family regressions, and shows that static benchmarks can misrepresent model behavior, motivating dynamic, deployment-relevant ev...
This work audits eight cybersecurity benchmarks across 10 proprietary, open-weight, and cybersecurity-specialized LLMs and shows that cybersecurity LLM benchmark scores are pipeline-dependent and motivate pipeline-aware auditing as a core requirement for reliable model evaluation.
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