From Beats to Breaches:How Offensive AI Infers Sensitive User Information from Playlists
The pervasive integration of AI has enabled Offensive AI: the exploitation of AI for malicious ends across the cyber-kill chain. A critical manifestation is the user attribute inference attack, where AI infers sensitive Personally Identifiable Information (PII) from innocuous public data. We explore how music streaming ecosystems, where users routinely release public playlists, can be exploited for Offensive AI. To quantify this threat, we developed musicPIIrate. This novel tool leverages deep learning architectures that utilize both standalone data representations and the structural information embedded in a user's playlist collection. Our design explores set-based approaches (e.g., Deep...
| Authors | Stefano Cecconello, Mauro Conti, Luca Pajola et al. |
|---|---|
| Primary category | cs.CR |
| Published | 6 May 2026 |
| Last updated at source | 8 July 2026 |
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