Lifecycle-Aware Dynamic Analysis for Secure ML Model Execution
The growing reliance on pre-trained Machine Learning (ML) models has introduced new attack surfaces. Recent vulnerabilities demonstrate that malicious behavior can be embedded within model artifacts, often bypassing existing defenses. Current model-scanning solutions primarily rely on static, format-specific rules or known attack signatures, which limit their ability to generalize across frameworks and to detect novel exploitation paths. In contrast, we propose a solution that focuses on the effects an attack has on the host system executing the model and builds on foundational intuitions about ML model execution. In particular, we observe that ML models operate within well-defined lifecy...
| Authors | Gabriele Digregorio, Marco Di Gennaro, Francesco Pastore et al. |
|---|---|
| Primary category | cs.CR |
| Published | 17 June 2026 |
| Last updated at source | 17 June 2026 |
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