On the Privacy of LLMs: An Ablation Study
Large language models (LLMs) face diverse privacy threats, yet existing studies typically evaluate individual attacks in isolation, offering limited guidance on which system factors drive privacy vulnerabilities. This paper presents a unified threat model and a systematic ablation study of four representative attacks: Membership Inference (MIA), Attribute Inference (AIA), Data Extraction (DEA), and Backdoor Attacks (BA). Our findings reveal attack-specific sensitivities: membership inference depends strongly on dataset characteristics and attack strategy, attribute inference is influenced by model and training characteristics, data extraction increases with model scale and training-data r...
| Authors | Karima Makhlouf, Lamiaa Basyoni, Syed Khaderi et al. |
|---|---|
| Primary category | cs.CR |
| Published | 4 May 2026 |
| Last updated at source | 15 October 2026 |
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