Generative AI systems such as Large Language Models (LLMs) open up enormous potential – but they also bring new, hard-to-assess security risks.
This white paper provides a concise, hands-on overview of how targeted penetration testing and proven best practices can help you identify vulnerabilities, meet regulatory requirements such as the Cyber Resilience Act, and sustainably strengthen the resilience of your AI applications.
Through concrete attack scenarios such as prompt injection or training data poisoning, you will gain a clear roadmap for the secure use of AI in your organization.
What to expect – the key focus topics of the white paper
- Understanding the risks of generative AI: How LLMs create new attack surfaces and which security challenges arise.
- Penetration testing for AI: Why traditional pentests are not sufficient – and which methods have been specifically developed for AI systems.
- Top attack scenarios at a glance: The three biggest risks according to OWASP – prompt injection, insecure output handling, and training data poisoning.
- A structured approach: From scoping to follow-up – how a pentest for LLMs should ideally be carried out.
- Best practices for greater security: Recommendations such as access controls, logging, filtering mechanisms, and regular audits.
- How we can support you: An overview of TÜV Rheinland’s services and tools for penetration testing and AI security.
Download whitepaper now!
The fields marked with * are required for a region-specific contact to coordinate conversations. This enables us to process your inquiries quickly and competently.
Back to top
