Code-Poisoning Property Inference Attacks
The flourishing code hosting platforms and coding agents enable even beginners with private data to build tailored Machine Learning (ML) models using available code quickly. The training data for ML models, often regarded as private property (e.g., clinical records, transaction information), is at significant risk of information leakage. Property Inference Attacks (PIAs), as a significant type of privacy attack, aim to expose global property information of the training set. In this paper, we present Code-Poisoning Property Inference Attack (CPPIA), the first code-level PIA, which overcomes four limitations of existing works: insufficient attack performance, severe degradation of model acc...
| Authors | Xukun Luan, Yuhui Gong, Gang Zhang et al. |
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| Primary category | cs.CR |
| Published | 17 July 2026 |
| Last updated at source | 17 July 2026 |
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