What Is LLM Security? Risks and Threats

As the integration of Large Language Models (LLMs) into applications becomes widespread, traditional application security tools fall short in addressing the unique vulnerabilities these AI components introduce. LLM security involves safeguarding applications from risks like prompt injection, context poisoning, and improper output handling, which are not typically detected by standard security testing methods. The OWASP LLM Security Top 10 highlights these vulnerabilities, emphasizing the need for runtime testing that evaluates how applications behave with user interactions involving LLMs. Attackers exploit LLMs by manipulating natural language inputs, which can lead to data leaks, unauthorized actions, and bypassing security controls. To protect against these threats, organizations must implement robust input validation, output monitoring, and context isolation, while also conducting runtime testing to ensure defenses are effective under attack conditions. As LLMs become integral to customer-facing features, addressing these security challenges is crucial to prevent data breaches and service disruptions.

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