Membership Inference Attacks for Retrieval Based In-Context Learning for Document Question Answering
We show that remotely hosted applications employing in-context learning when augmented with a retrieval function to select in-context examples can be vulnerable to membership-inference attacks even when the service provider and users are separate parties. We propose two black-box membership inference attacks that exploit query text prefixes to distinguish member from non-member inputs. The first attack uses a reference model to estimate an otherwise unavailable loss metric. The second attack improves upon it by eliminating the reference model and instead computing a membership statistic through a simple but novel weighted-averaging scheme. Our comprehensive empirical evaluations consider ...
| Authors | Tejas Kulkarni, Antti Koskela, Laith Zumot |
|---|---|
| Primary category | cs.CR |
| Published | 5 May 2026 |
| Last updated at source | 5 May 2026 |
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