Hidden Risks of Jev: An Empirical Study of Security, Privacy, and Dual Use
Jev turns natural-language questions into typed answers and probabilities with low latency and cost, enabling applications to route requests and select tools. While this interface allows Jev to integrate naturally into application workflows as a decision layer, the security and privacy implications of this emerging use remain largely unexplored. To address this gap, we conduct the first systematic study of these implications using the official Jev API and NanoJev, a local model with controllable training data and updates, focusing on three research questions: (1) What security threats arise when Jev is deployed as an application decision layer? (2) What private information can Jev reveal ...
| Authors | Shang Wang, Tianqing Zhu, Huajie Chen et al. |
|---|---|
| Primary category | cs.CR |
| Published | 4 October 2026 |
| Last updated at source | 4 October 2026 |
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