The large language model (LLM) service on various cloud platforms allows organizations to create specialized applications, such as Retrieval Augmented Generation (RAG) for building chatbots for specific tasks. AWS, for example, highlights 8 security considerations for such applications.
An example chat app on AWS includes a web interface developed with Streamlit, the main application developed with Lambda, a DynamoDB database for storing chat history, connected to the core LLM – Cluade 3 Sonnet, pulling document data from S3 into OpenSearch by converting text into vectors using Titan Embedding.
The 8 security considerations recommended by AWS are:
– Failure to protect the app with login authentication can lead to unauthorized access and data theft.
– Failing to sanitize user input can expose the app to various attacks before connecting to LLM.
– Inadequately securing connections between different parts of the application can lead to direct breaches.
– Insufficiently detailed logging processes make it difficult to analyze and address security issues.
– Insecure data storage such as unencrypted databases on S3 or insufficient access control pose risks.
– Neglecting security measures for the LLM model itself can lead to exploitation or model manipulation.
– Lack of ethical control policies in AI usage requires systematic risk assessment and bias-checking of training data.
– Full-scale testing of the system is essential to ensure accurate question responses by the application.
AWS emphasizes the importance of maintaining security in LLM chat applications at the same level as other organizational apps, requiring a consistent security management approach.
Source: AWS Blog
TLDR: AWS advises on 8 security considerations for LLM chat applications, stressing the need for robust security measures throughout the development and deployment process.
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