10 most critical LLM vulnerabilities

Enterprise adoption of generative AI technologies has exploded due to the rapid evolution of the technology and the emergence of a variety of business use cases. But large language models (LLMs) can accidentally produce harmful results, leak information, or become exposed to threat actors. These vulnerabilities are changing as the technology evolves and as attackers find new ways to compromise systems.

For enterprises, this means the risk of bad publicity, compliance or cybersecurity exposure, legal liability, or even class-action lawsuits. To keep up with the latest in LLM risks, the Open Worldwide Application Security Project (OWASP) has updated its list of the top 10 most critical vulnerabilities of LLM applications.

For the first time, the list is influenced by real-world incident data alongside expert voting on classes of AI security risk.