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 pre
Record details
Published: 17 July 2026
Source: arXiv cs.CR (AI security)
Category: Research
Topics: Privacy · Healthcare · Agents & autonomy
Retrieved: 20 July 2026
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How to cite this record
ethics.ai (17 July 2026), “Code-Poisoning Property Inference Attacks,” evidence record 11863, https://ethics.ai/record/11863 (originally published by arXiv cs.CR (AI security)).
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