Independent Chinese AI agent developers focus on user safety risks while overlooking security vulnerabilities, using informal ad-hoc practices due to lack of formal training and tools.
Ghost of the past
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
years
2026 4verdicts
UNVERDICTED 4representative citing papers
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.
Interview study of 44 novice researchers finds privacy fears paradoxically accelerate LLM use for faster publication, with misconceptions about idea value and data dilution, and perceived ineffective mitigations.
Back-Reveal shows that LLM agents with tool access can be backdoored via fine-tuning to exfiltrate stored user context through memory and retrieval tool calls, with multi-turn interactions enabling sustained leakage.
citing papers explorer
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Focused on the User, Overlooking the Risks: Security and Privacy Understandings, Practices and Challenges of Independent Chinese AI Agent Developers
Independent Chinese AI agent developers focus on user safety risks while overlooking security vulnerabilities, using informal ad-hoc practices due to lack of formal training and tools.
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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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Investigating Novice Researchers' Perceptions of Research Privacy Within LLM-Assisted Workflows
Interview study of 44 novice researchers finds privacy fears paradoxically accelerate LLM use for faster publication, with misconceptions about idea value and data dilution, and perceived ineffective mitigations.
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Your LLM Agent Can Leak Your Data: Data Exfiltration via Backdoored Tool Use
Back-Reveal shows that LLM agents with tool access can be backdoored via fine-tuning to exfiltrate stored user context through memory and retrieval tool calls, with multi-turn interactions enabling sustained leakage.