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Singapore University of Technology and Design (SUTD)
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Current ML model security scanners miss nearly half of malicious models because they fail to observe runtime behavior, but a new dynamic analysis technique closes this gap.
Code LLMs fail consistency checks on 15% of inputs, revealing a significant reliability gap that existing benchmarks miss.
