PAAC aligns planner-executor decomposition with the device-cloud boundary via typed placeholders and on-device sanitization, delivering 15-36% higher accuracy and 2-6x lower leakage than prior device-cloud baselines on agentic benchmarks.
Pr ϵϵmpt: Sanitizing Sensitive Prompts for LLMs
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LLMs exhibit 20-40% lower recall on ambiguous human names for PII detection, worsening under prompt injections, as shown via the new AmBench benchmark.
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PAAC: Privacy-Aware Agentic Device-Cloud Collaboration
PAAC aligns planner-executor decomposition with the device-cloud boundary via typed placeholders and on-device sanitization, delivering 15-36% higher accuracy and 2-6x lower leakage than prior device-cloud baselines on agentic benchmarks.
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Can Large Language Models Really Recognize Your Name?
LLMs exhibit 20-40% lower recall on ambiguous human names for PII detection, worsening under prompt injections, as shown via the new AmBench benchmark.