Beyond Indistinguishability: Measuring Extraction Risk in LLM APIs
Indistinguishability properties such as differential privacy bounds or low empirically measured membership inference are widely treated as proxies to show a model is sufficiently protected against broader memorization risks. However, we show that indistinguishability properties are neither sufficient nor necessary for preventing data extraction in LLM APIs. We formalize a privacy-game separation between extraction and indistinguishability-based privacy, showing that indistinguishability and inextractability are incomparable: upper-bounding distinguishability does not upper-bound extractability. To address this gap, we introduce $(l, b)$-inextractability as a definition that requires at le...
| Authors | Ruixuan Liu, David Evans, Li Xiong |
|---|---|
| Primary category | cs.CR |
| Published | 20 April 2026 |
| Last updated at source | 20 April 2026 |
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