arXiv:2604.01904v3 Announce Type: replace-cross Abstract: Post-hoc unauthorized-training data detection for large language models (LLMs) typically assumes a query-with-originals regime: rights holders query a target LLM with raw proprietary data and assess whether the model assigns them stronger memorization-based detection signals, e.g., higher confidence or lower loss, than held-out non-training reference texts. We show that this regime becomes brittle under data laundering, where the target LLM is trained on semantics-preserving but stylistically or structurally transformed surrogates of pr
The proliferation of advanced LLMs and their insatiable need for training data is accelerating concerns around intellectual property and data ownership.
This research highlights a significant vulnerability in current methods for policing unauthorized data use in LLM training, suggesting that existing safeguards are insufficient against sophisticated 'data laundering' techniques.
The efficacy of post-hoc detection of unauthorized training data is now called into question, necessitating the development of more robust, proactive intellectual property protection strategies for data used in AI.
- · IP protection services
- · Data rights holders with proactive measures
- · Developers of new data provenance techniques
- · LLM developers relying on current detection methods
- · Data rights holders without proactive measures
- · Existing data detection software
Increased investment in watermarking and data provenance technologies for AI training data.
Potential for new regulations requiring explicit data licensing and auditable training data lineages for LLMs.
A shift towards more 'synthetic data' generation or 'federated learning' to mitigate IP risks associated with proprietary datasets.
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Read at arXiv cs.AI