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LLM-Attacker: Privacy Evaluation Framework for Split LLM Systems
- Engineer — Learn: Research demonstrates that intermediate activations in split LLM architectures can enable high-accuracy input reconstruction (94–97% token recovery). Engineers deploying split inference for privacy should evaluate whether intermediate representations actually provide the isolation they assume.
- SOC/IR — Skip
- Leader — Learn: If your organization uses or is evaluating split LLM deployments as a data-privacy control, this research indicates that approach may not provide the assumed protection — worth factoring into AI governance or vendor data-handling assessments.
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