TrEEStealer: Stealing Decision Trees via Enclave Side Channels
Today, machine learning is widely applied in sensitive, security-related, and financially lucrative applications. Model extraction attacks undermine current business models where a model owner sells model access, e.g., via MLaaS APIs. Additionally, stolen models can enable powerful white-box attacks, facilitating privacy attacks on sensitive training data, and model evasion. In this paper, we focus on Decision Trees (DT), which are widely deployed in practice. Existing black-box extraction attacks for DTs are either query-intensive, make strong assumptions about the DT structure, or rely on rich API information. To limit attacks to the black-box setting, CPU vendors introduced Trusted Exe...
| Authors | Jonas Sander, Anja Rabich, Nick Mahling et al. |
|---|---|
| Primary category | cs.CR |
| Published | 20 April 2026 |
| Last updated at source | 20 April 2026 |
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