PrivScope enforces task-scoped disclosure at the local-cloud boundary in hybrid agents, eliminating profile leakage and halving re-identification risk on medical workflows while preserving task success.
Towards automating data access permissions in ai agents
6 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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background 1
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citation-polarity summary
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cs.CR 6years
2026 6verdicts
UNVERDICTED 6representative citing papers
PrivacySIM shows that conditioning LLMs on user personas like demographics and attitudes improves simulation of privacy choices but reaches only 40.4% accuracy against real responses from 1,000 users.
BIV audits AI agent skills at scale, finding 80% deviate from declared behavior on 49,943 skills and achieving 0.946 F1 for malicious skill detection.
No existing AI security framework covers a majority of the 193 identified multi-agent system threats in any category, with OWASP Agentic Security Initiative achieving the highest overall coverage at 65.3%.
LLM agent security is reframed as an agent-human interaction issue, supported by a survey showing industry preference for human-centric mechanisms over academic favorites and proposing a new research agenda.
Conleash uses a risk lattice, policy engine, and refinement loop to deliver scoped, consent-driven authorization for MCP tool calls, reaching 98.2% accuracy and 99.4% escalation catch rate on 984 traces with 8.2 ms overhead and higher user preference in a 16-person study.
citing papers explorer
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PrivScope: Task-scoped Disclosure Control for Hybrid Agentic Systems
PrivScope enforces task-scoped disclosure at the local-cloud boundary in hybrid agents, eliminating profile leakage and halving re-identification risk on medical workflows while preserving task success.
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PrivacySIM: Evaluating LLM Simulation of User Privacy Behavior
PrivacySIM shows that conditioning LLMs on user personas like demographics and attitudes improves simulation of privacy choices but reaches only 40.4% accuracy against real responses from 1,000 users.
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Behavioral Integrity Verification for AI Agent Skills
BIV audits AI agent skills at scale, finding 80% deviate from declared behavior on 49,943 skills and achieving 0.946 F1 for malicious skill detection.
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Security Considerations for Multi-agent Systems
No existing AI security framework covers a majority of the 193 identified multi-agent system threats in any category, with OWASP Agentic Security Initiative achieving the highest overall coverage at 65.3%.
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Reframing LLM Agent Security as an Agent-Human Interaction Problem
LLM agent security is reframed as an agent-human interaction issue, supported by a survey showing industry preference for human-centric mechanisms over academic favorites and proposing a new research agenda.
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Options, Not Clicks: Lattice Refinement for Consent-Driven MCP Authorization
Conleash uses a risk lattice, policy engine, and refinement loop to deliver scoped, consent-driven authorization for MCP tool calls, reaching 98.2% accuracy and 99.4% escalation catch rate on 984 traces with 8.2 ms overhead and higher user preference in a 16-person study.