HTML injection in Large Language Models (LLMs) involves embedding malicious HTML code within prompts or inputs to manipulate the model’s output or behavior. Attackers exploit the model’s ability to interpret and process text-based HTML, aiming to introduce unintended formatting, misleading content, or harmful instructions. For instance, injected HTML could alter the structure of the model’s responses, embed deceptive links, or simulate legitimate interfaces for phishing attacks. This technique highlights vulnerabilities in LLMs, particularly in scenarios where they are integrated with web-based applications or used to generate content for rendering in HTML environments. Mitigating such risks requires input sanitization, robust filtering mechanisms, and strict handling protocols to ensure that the AI processes text inputs securely without executing or rendering harmful HTML code.