PreCurious: How Innocent Pre-Trained Language Models Turn into Privacy Traps

The pre-training and fine-tuning paradigm has demonstrated its effectiveness and has become the standard approach for tailoring language models to various tasks. Currently, community-based platforms offer easy access to various pre-trained models, as anyone can publish without strict validation processes. However, a released pre-trained model can be a privacy trap for fine-tuning datasets if it is carefully designed. In this work, we propose PreCurious framework to reveal the new attack surface where the attacker releases the pre-trained model and gets a black-box access to the final fine-tuned model. PreCurious aims to escalate the general privacy risk of both membership inference and data extraction on the fine-tuning dataset. The key intuition behind PreCurious is to manipulate the memorization stage of the pre-trained model and guide fine-tuning with a seemingly legitimate configuration. While empirical and theoretical evidence suggests that parameter-efficient and differentially private fine-tuning techniques can defend against privacy attacks on a fine-tuned model, PreCurious demonstrates the possibility of breaking up this invulnerability in a stealthy manner compared to fine-tuning on a benign pre-trained model. While DP provides some mitigation for membership inference attack, by further leveraging a sanitized dataset, PreCurious demonstrates potential vulnerabilities for targeted data extraction even under differentially private tuning with a strict privacy budget e.g. <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML">mml:miϵ</mml:mi> mml:mo=</mml:mo> mml:mn0.05</mml:mn></mml:math> . Thus, PreCurious raises warnings for users on the potential risks of downloading pre-trained models from unknown sources, relying solely on tutorials or common-sense defenses, and releasing sanitized datasets even after perfect scrubbing.