Human safety awareness gaps often prevent the timely recognition of everyday risks.In solving this problem, a proactive safety artificial intelligence (AI) system would work better than a reactive one. Instead of just reacting to users' questions, it would actively watch people’s behavior and their environment to detect potential dangers in advance.Our Proactive Safety Bench (PaSBench) evaluates this capability through 416 multimodal scenarios (128 image sequences, 288 text logs) spanning 5 safety-critical domains.Evaluation of 36 advanced models reveals fundamental limitations: Top performers like Gemini-2.5-pro achieve 71\% image and 64\% text accuracy, but miss 45-55\% risks in repeated trials. Through failure analysis, we identify unstable proactive reasoning rather than knowledge deficits as the primary limitation.This work establishes (1) a proactive safety benchmark, (2) systematic evidence of model limitations, and (3) critical directions for developing reliable protective AI. We believe our dataset and findings can promote the development of safer AI assistants that actively prevent harm rather than merely respond to requests.

Question answering (QA) software uses information retrieval and natural language processing techniques to automatically answer questions posed by humans in a natural language. Like other AI-based software, QA software may contain bugs. To automatically test QA software without human labeling, previous work extracts facts from question answer pairs and generates new questions to detect QA software bugs. Nevertheless, the generated questions could be ambiguous, confusing, or with chaotic syntax, which are unanswerable for QA software. As a result, a relatively large proportion of the reported bugs are false positives. In this work, we proposed QAQA, a sentence-level mutation based metamorphic testing technique for QA software. To eliminate false positives and achieve precise automatic testing, QAQA leverages five Metamorphic Relations (MRs) as well as semantics-guided search and enhanced test oracles. Our evaluation on three QA datasets demonstrates that QAQA outperforms the state-of-the-art in both quantity (8,133 vs. 6,601 bugs) and quality (97.67% vs. 49% true positive rate) of the reported bugs. Moreover, the test inputs generated by QAQA successfully reduce MR violation rate from 44.29% to 20.51% when being adopted in fine-tuning the QA software under test.