The OWASP Top 10 for Large Language Models (LLMs

The OWASP Top 10 for Large Language Models (LLMs) highlights critical vulnerabilities that organizations must address to ensure secure deployment as these models grow in use. Among the most important risks, Prompt Injection stands out, where attackers manipulate inputs to inject malicious commands, potentially leading to harmful outputs or data breaches. Sensitive Information Disclosure poses a significant threat, as LLMs may inadvertently reveal confidential data from training sets, compromising privacy. Supply Chain Vulnerabilities arise from compromised third-party components, risking the model's security and functionality. Training Data Poisoning is another key concern, where corrupted datasets can skew the model’s outputs, reducing reliability. Improper Output Handling can disseminate misleading information if outputs aren’t validated, endangering users. Excessive Agency risks occur when LLMs gain too much autonomy, leading to unchecked actions, while Model Theft threatens intellectual property through unauthorized access and replication. Addressing these vulnerabilities through robust mitigation strategies is essential to safeguard LLM applications and protect against exploitation.