This whitepaper provides an overview of the most common security risks associated with large language models (LLMs). It offers practical solutions for mitigating these risks. It also presents design patterns that you can implement to ensure robust security in LLM applications.
Content Highlights:
- While transformative, LLMs present unique security challenges due to their complex architecture, reliance on vast datasets, and inherent non-determinism.
- Key vulnerabilities include prompt injection, data poisoning, model theft, and insecure output handling, each with potentially severe consequences for data privacy, system integrity, and organisational reputation.
- The whitepaper explores practical design patterns, including input/output validation, prompt engineering techniques, access control, and monitoring, to counter these threats effectively.
- Real-world examples, such as the DPD chatbot incident, illustrate the tangible impact of LLM vulnerabilities and underscore the need for proactive security measures.
- A comprehensive checklist guides organisations like yours through essential steps to secure LLMs in production. It encompasses data integrity, access control, encryption, monitoring, output validation, and incident response planning.
Download now to gain insights into securing your LLM applications