Frontier LLMs leak prompted secret information thematically in generated stories at rates up to 79% above chance in binary discrimination tests, even when told to hide it, with leakage scaling by model size and vanishing for short-form outputs.
Can llms keep a secret? testing privacy implications of language models via contextual integrity theory
6 Pith papers cite this work. Polarity classification is still indexing.
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Users show curiosity over concern toward LLM inferences of personal information, with acceptability depending on context, alignment with expectations, and who uses the inferences rather than just the content.
CAMP formalizes Cumulative PII Exposure and uses a session registry, co-occurrence graph, and CPE score to trigger retroactive masking in multi-turn LLM conversations, neutralizing re-identifiable profiles in synthetic tests while keeping utility intact.
AgentCollabBench shows that multi-agent reliability is limited by communication topology, with converging-DAG nodes causing synthesis bottlenecks that discard constraints and explain 7-40% of information loss variance.
Vision-language models exhibit perceptual fragility and fail to consistently respect privacy constraints when operating in simulated physical environments, with performance declining in cluttered scenes and under conflicting commands.
The paper reviews the background, technology, applications, limitations, and future directions of OpenAI's Sora text-to-video generative model based on public information.
citing papers explorer
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Can You Keep a Secret? Involuntary Information Leakage in Language Model Writing
Frontier LLMs leak prompted secret information thematically in generated stories at rates up to 79% above chance in binary discrimination tests, even when told to hide it, with leakage scaling by model size and vanishing for short-form outputs.
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When Are LLM Inferences Acceptable? User Reactions and Control Preferences for Inferred Personal Information
Users show curiosity over concern toward LLM inferences of personal information, with acceptability depending on context, alignment with expectations, and who uses the inferences rather than just the content.
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CAMP: Cumulative Agentic Masking and Pruning for Privacy Protection in Multi-Turn LLM Conversations
CAMP formalizes Cumulative PII Exposure and uses a session registry, co-occurrence graph, and CPE score to trigger retroactive masking in multi-turn LLM conversations, neutralizing re-identifiable profiles in synthetic tests while keeping utility intact.
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AgentCollabBench: Diagnosing When Good Agents Make Bad Collaborators
AgentCollabBench shows that multi-agent reliability is limited by communication topology, with converging-DAG nodes causing synthesis bottlenecks that discard constraints and explain 7-40% of information loss variance.
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How Far Are VLMs from Privacy Awareness in the Physical World? An Empirical Study
Vision-language models exhibit perceptual fragility and fail to consistently respect privacy constraints when operating in simulated physical environments, with performance declining in cluttered scenes and under conflicting commands.
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Sora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Models
The paper reviews the background, technology, applications, limitations, and future directions of OpenAI's Sora text-to-video generative model based on public information.