Persona-driven workflow and interface improve automated and human-AI red-teaming of generative AI by incorporating diverse perspectives into adversarial prompt creation.
How well can llm agents simulate end-user security and privacy attitudes and behaviors?
6 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 6verdicts
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WhatIf provides an interactive platform for real-time exploration of LLM-driven social simulations, enabling policymakers to iteratively test plans, reflect on assumptions, and uncover vulnerabilities in emergency preparedness scenarios.
Narriva generates behavior-grounded text personas from survey data that achieve up to 87% accuracy in predicting privacy decisions, improve 6-17 points over baselines, cut tokens by 80-95%, and reproduce aggregate distributions across different studies.
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.
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.
Stronger reasoning models in LLMs reduce behavioral negotiation by defaulting to authority outcomes in multi-agent settings, unlike structured scaffolds that enable concessions.
citing papers explorer
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PersonaTeaming: Supporting Persona-Driven Red-Teaming for Generative AI
Persona-driven workflow and interface improve automated and human-AI red-teaming of generative AI by incorporating diverse perspectives into adversarial prompt creation.
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WhatIf: Interactive Exploration of LLM-Powered Social Simulations for Policy Reasoning
WhatIf provides an interactive platform for real-time exploration of LLM-driven social simulations, enabling policymakers to iteratively test plans, reflect on assumptions, and uncover vulnerabilities in emergency preparedness scenarios.
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Text-Based Personas for Simulating User Privacy Decisions
Narriva generates behavior-grounded text personas from survey data that achieve up to 87% accuracy in predicting privacy decisions, improve 6-17 points over baselines, cut tokens by 80-95%, and reproduce aggregate distributions across different studies.
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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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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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When Reasoning Models Hurt Behavioral Simulation: A Solver-Sampler Mismatch in Multi-Agent LLM Negotiation
Stronger reasoning models in LLMs reduce behavioral negotiation by defaulting to authority outcomes in multi-agent settings, unlike structured scaffolds that enable concessions.