As Large Language Models (LLMs) become integral to various applications, understanding and mitigating associated security risks is paramount. The OWASP Top 10 for LLM Applications provides a comprehensive framework to address these vulnerabilities. Among the identified risks, Prompt Injection stands out as a significant concern. This vulnerability occurs when malicious inputs manipulate LLMs, leading to unauthorized access or unintended outputs. Insecure Output Handling is another critical risk, where insufficient validation of LLM outputs can result in security exploits, including code execution that compromises systems and exposes data. Training Data Poisoning involves tampering with training data, impairing LLM models and leading to responses that may compromise security, accuracy, or ethical behavior. Model Denial of Service refers to overloading LLMs with resource-heavy operations, causing service disruptions and increased costs. Supply Chain Vulnerabilities highlight the risks associated with depending on compromised components, services, or datasets, undermining system integrity and causing data breaches. Sensitive Information Disclosure pertains to the failure to protect against the disclosure of sensitive information in LLM outputs, resulting in legal consequences or a loss of competitive advantage. Insecure Plugin Design involves LLM plugins processing untrusted inputs and having insufficient access control, risking severe exploits like remote code execution. Excessive Agency refers to granting LLMs unchecked autonomy to take action, leading to unintended consequences that jeopardize reliability, privacy, and trust. Overreliance involves failing to critically assess LLM outputs, leading to compromised decision-making, security vulnerabilities, and legal liabilities. Model Theft pertains to unauthorized access to proprietary large language models, risking theft, competitive advantage, and dissemination of sensitive information. owasp.org
Mitigating these risks requires a multifaceted approach. Implementing robust input validation mechanisms can prevent prompt injection attacks. Establishing comprehensive output validation processes ensures that LLM outputs are safe and reliable. Regularly auditing and sanitizing training data helps prevent data poisoning. Employing rate-limiting and resource management strategies can protect against denial of service attacks. Ensuring the integrity of supply chain components through rigorous vetting processes is essential. Implementing strict access controls and data protection measures safeguards sensitive information. Designing plugins with secure coding practices and access controls mitigates exploitation risks. Defining clear operational boundaries for LLMs prevents excessive agency. Encouraging critical evaluation of LLM outputs reduces overreliance. Securing proprietary models through encryption and access controls prevents model theft. By proactively addressing these vulnerabilities, organizations can enhance the security and trustworthiness of their LLM applications. owasp.org