Introduction to AUDN: Adversarial Simulation for AI
AUDN is a cutting-edge platform designed to protect open-ended interfaces to intelligent systems, with a primary focus on voice and text AI agents. By simulating adversarial scenarios using advanced AI-driven techniques, AUDN performs penetration testing on voice AI agents and their underlying large language models (LLMs). The platform leverages Pingu Unchained, an unrestricted AI model optimized for high-risk research and red teaming, to identify vulnerabilities that traditional security tools might miss. AUDN's customers include leading voice AI providers such as Freya (YC S25) and Intouchnow, highlighting its value in securing next-generation AI systems.
The product is built for teams developing voice, text, and multimodal AI agents, offering automated behavioral security that adapts to evolving threats. With a focus on real-world attack simulations, AUDN ensures that AI systems are resilient against adversarial inputs, including deepfakes, social engineering, and data leaks. Its solution is particularly relevant in regulated industries like finance, healthcare, and government, where AI security is critical.
Takeaways
- Automated adversarial testing using Pingu Unchained LLM
- Real-time vulnerability detection and patching
- Supports both voice and text AI agents
- Integrates with major cloud platforms and LLMs
- Focuses on business-specific risks, not just model vulnerabilities
How AUDN Works
AUDN operates through two core components: Audn Red and Audn Blue. Audn Red conducts autonomous adversarial testing by generating millions of attack vectors, simulating real-world scenarios such as jailbreaks, deepfakes, and social engineering. This process includes voice-based attacks, API injection, and expert-guided campaigns depending on the interface type. Audn Blue then uses the findings from Audn Red to strengthen defenses, implementing real-time guardrails and policy enforcement to block harmful inputs.
The platform also features a continuous hardening loop known as RL-SEC, which trains both red and blue teams simultaneously through A2A (Agent-to-Agent) simulations. This allows for ongoing adaptation to emerging threats without requiring manual intervention. Users can connect to their AI systems via phone numbers or API integrations, making it easy to implement without significant code changes.
Core Benefits and Applications
| Benefit | Description |
|---|---|
| Proactive Security | Identifies vulnerabilities before they are exploited by malicious actors |
| Comprehensive Coverage | Tests both voice and text AI agents, supporting various infrastructure setups |
| Regulatory Compliance | Aligns with industry frameworks like OWASP, NIST, and ISO 42001 |
| Scalable Testing | Runs adversarial tests at scale with no human-in-the-loop requirements |
| Continuous Monitoring | Provides real-time protection and updates based on new threat intelligence |
AUDN is ideal for organizations looking to secure their AI systems against sophisticated attacks while maintaining compliance and operational efficiency.
