VeriGenAI provides an automated red‑team platform that tests large language models against the OWASP LLM Top 10 2025 framework using up to 42 specialized AI agents. The agents execute multiple adaptive attack strategies and continuously learn from each assessment, improving vulnerability detection accuracy from about 85 % to 95 %. Results are delivered in detailed reports with risk scores and remediation guidance for security and DevSecOps teams.
Funding
Funding not disclosed
Founders
Product
Problem
Organizations deploying generative AI models face a growing set of security risks—including prompt injection, data poisoning, and uncontrolled resource consumption—that are not covered by traditional application security tools. Without specialized testing, these vulnerabilities can lead to data leaks, model manipulation, and operational disruptions.
Solution
VeriGenAI offers an automated red‑team platform that evaluates large language models against the OWASP LLM Top 10 2025 framework. The platform deploys up to 42 AI‑powered agents, each engineered to execute distinct attack strategies such as direct prompt injection, role‑playing, and social engineering. As assessments are repeated, the agents learn the target application’s specific weaknesses, raising detection accuracy from roughly 85 % to 95 % over multiple runs. Test results are presented with detailed vulnerability reports and risk scores, enabling security teams to remediate issues before production deployment. The solution scales from a single free agent for basic coverage to a full‑suite of agents for comprehensive compliance.
Target Audience
Primary customers are security and DevSecOps teams responsible for safeguarding generative AI products in enterprises, SaaS providers, and AI‑focused startups seeking OWASP LLM compliance.
Features
- 42 specialized AI agents mapped to the ten OWASP LLM vulnerability categories, providing full coverage of the 2025 standard
- Six adaptive attack strategies (Direct, Gradual Escalation, Role‑Playing, Technical Obfuscation, Social Engineering, Context Manipulation) that simulate realistic threat scenarios
- Continuous learning engine that refines detection models after each assessment, improving accuracy from 85 % to 95 %
- Automated multi‑turn testing that generates dynamic, context‑aware prompts to uncover hidden weaknesses
- Consolidated risk dashboard with per‑category severity scores, remediation guidance, and compliance tracking
- Flexible tiered pricing allowing customers to start with a single free agent and expand to enterprise‑level coverage