Over 175,000 AI servers are publicly exposed on the internet right now, many with no or weak authentication. A lot of them sit on GPU-backed cloud instances with production models loaded.
Your cloud bill is one open port away from an unexpected billing spike.
Unprotected servers let attackers list models, hijack compute power, and steal proprietary weights. They can even poison model behavior or execute remote code without any credentials.
The bigger problem is coverage. Standard application security coverage was designed for web apps and APIs. Self-hosted AI servers (like Ollama) sit outside that perimeter by default and create blind spots across attack surface, vulnerability management and runtime protection.
In this session Vivek Gopalan, VP of Products and Phani Deepak Akella, VP of Marketing, walk through an end-to-end AI application security workflow. The workflow demonstrates:
Discovering exposed Ollama servers on your network.
Identifying the endpoints they host.
Scanning for OWASP LLM Top 10 vulnerabilities.
Applying active protection against prompt injection, denial-of-wallet attacks, and other LLM-specific threats.
All in a single workflow, without switching tools or losing context between steps.