Empirical study of 400 LLM attack runs finds exploitation success rates of 25-85% across four models against a fixed multi-service honeypot, with model-distinctive failure modes and p<0.001 differences.
Building Safe GenAI Applications: An End-to-End Overview of Red Teaming for Large Language Models
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DoubleAgents shows that a distributed-cognition design with coordination agent, dashboard, and policy module increases user comfort and reliance on AI agents for coordination tasks over time.
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How Reliable Are AI Attackers Against a Fixed Vulnerable Target? A 400-Run Empirical Study of LLM Penetration Testing Consistency
Empirical study of 400 LLM attack runs finds exploitation success rates of 25-85% across four models against a fixed multi-service honeypot, with model-distinctive failure modes and p<0.001 differences.
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DoubleAgents: Human-Agent Alignment in a Socially Embedded Workflow
DoubleAgents shows that a distributed-cognition design with coordination agent, dashboard, and policy module increases user comfort and reliance on AI agents for coordination tasks over time.