Code-Poisoning Property Inference Attacks Enable Stealthy Training Data Leakage
Researchers introduce CPPIA, a novel code-level property inference attack that allows adversaries to embed property information into secret samples during model training. By leveraging poisoned code from platforms like GitHub or coding agents such as Codex, attackers can later query the trained model to extract global properties of the private training data. The attack achieves 100% accuracy, does not degrade model performance, is computationally efficient, and bypasses existing defenses across multiple datasets and architectures.
Why it matters: CPPIA reveals a new and effective privacy threat in machine learning pipelines that depend on third-party code, highlighting the risk of sensitive data leakage even when standard defenses are in place.
Full story at: arXiv Cryptography and Security ↗