GAAP guarantees confidentiality of private user data for AI agents by enforcing user-specified permissions deterministically through persistent information flow tracking, without trusting the agent or requiring attack-free models.
Pandya, Ashish Hooda, Xiaohan Fu, and Earlence Fernandes
2 Pith papers cite this work. Polarity classification is still indexing.
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DART raises difference-awareness accuracy from 39% to 68.8% on benchmarks while cutting harm-drift cases by 72.6% and improving real-world appropriate responses from 39.8% to 77.5%.
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An AI Agent Execution Environment to Safeguard User Data
GAAP guarantees confidentiality of private user data for AI agents by enforcing user-specified permissions deterministically through persistent information flow tracking, without trusting the agent or requiring attack-free models.
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DART: Mitigating Harm Drift in Difference-Aware LLMs via Distill-Audit-Repair Training
DART raises difference-awareness accuracy from 39% to 68.8% on benchmarks while cutting harm-drift cases by 72.6% and improving real-world appropriate responses from 39.8% to 77.5%.