DynaHug trains an OCSVM on dynamic runtime behaviors of benign PTMs and achieves up to 44% higher F1-score than static, dynamic, and LLM-based baselines on over 25,000 models.
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Moat performs lifecycle-aware dynamic analysis to detect malicious behavior in ML model execution across frameworks, achieving full detection of tested attacks with near-zero false positives on large real-world datasets.
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Malicious ML Model Detection by Learning Dynamic Behaviors
DynaHug trains an OCSVM on dynamic runtime behaviors of benign PTMs and achieves up to 44% higher F1-score than static, dynamic, and LLM-based baselines on over 25,000 models.
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Lifecycle-Aware Dynamic Analysis for Secure ML Model Execution
Moat performs lifecycle-aware dynamic analysis to detect malicious behavior in ML model execution across frameworks, achieving full detection of tested attacks with near-zero false positives on large real-world datasets.