Agent Turing is an agentic AI red teaming platform developed by PrivaSapien. It autonomously interviews, stress-tests, and scores large language models (LLMs) and generative AI agents across four evaluation dimensions: privacy, safety, security, and fairness — all prior to deployment. The platform addresses risks that organizations face when adopting AI, including privacy leaks exposing customer data, security vulnerabilities enabling jailbreaking and prompt injection, and bias leading to unfair outcomes. Key testing capabilities include: - Security Testing: Assesses vulnerabilities such as prompt leakage, filter bypass, and command execution through multi-turn adversarial jailbreaking techniques. - Fairness Testing: Simulates diverse user personas to detect and flag bias when AI responses exceed defined ethical thresholds. - Privacy Testing: Probes for risks of PII leakage, membership inference, and re-identification from partial cues and system prompts. - User Safety Testing: Stress-tests LLMs against toxic, unsafe, and adversarial queries using layered prompt-chaining methods. Agent Turing uses a proprietary Turing Tree™ algorithm that enables multi-round adversarial testing through advanced questioning algorithms. It provides a comparative risk score across tested models, enabling organizations to assess and compare the trustworthiness of different AI models before selection or deployment. The platform claims to achieve 20x faster audits compared to manual red teaming approaches. It functions as an end-to-end AI red teaming and risk assessment solution, simulating real-world attacks to uncover model vulnerabilities and providing a clear security posture assessment.