In the age of AI, large language models (LLMs) are increasingly being integrated into web applications, offering powerful capabilities for content creation, customer support, and more. However, their widespread adoption has introduced a new cybersecurity challenge—Web LLM attacks.
These attacks exploit vulnerabilities in how LLMs are deployed and interact with users online. By manipulating inputs or exploiting security gaps, attackers can trigger unintended actions, potentially leading to data breaches, misinformation, or unauthorized access.
As businesses incorporate LLMs into their web services, it is essential to recognize these emerging threats and implement robust security measures. Attacking an LLM integration is conceptually similar to exploiting a server-side request forgery (SSRF) vulnerability, where an attacker leverages a server-side system to target otherwise inaccessible components.