LLMs and Contextual Integrity
" CIMemories: A Compositional Benchmark for Contextual Integrity of Persistent Memory in LLMs ": However, this memory introduces critical risks when sensitive information is revealed in inappropriate contexts.
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" CIMemories: A Compositional Benchmark for Contextual Integrity of Persistent Memory in LLMs ":
Our evaluation reveals that frontier models exhibit up to 69% attribute-level violations (leaking information inappropriately), with lower violation rates often coming at the cost of task utility.
However, this memory introduces critical risks when sensitive information is revealed in inappropriate contexts.
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The useful part
" CIMemories: A Compositional Benchmark for Contextual Integrity of Persistent Memory in LLMs ": However, this memory introduces critical risks when sensitive information is revealed in inappropriate contexts. CIMemories uses synthetic user profiles with over 100 attributes per user, paired with diverse task contexts in which each attribute may be essential for some tasks but inappropriate for others.
How it works
- Our evaluation reveals that frontier models exhibit up to 69% attribute-level violations (leaking information inappropriately), with lower violation rates often coming at the cost of task utility.
- Privacy-conscious prompting does not solve this—models overgeneralize, sharing everything or nothing rather than making nuanced, context-dependent decisions.
Details worth keeping
I have been thinking a lot about AI and integrity.
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