Dear Community:
I have drafted the following document for my team in terms of security measures for LLM applications. I would like to seek your comments:
1:Avoid PII and PHI Data: Ensure that prompts and training data used for LLMs do not contain Personally Identifiable Information (PII) or Protected Health Information (PHI) to prevent the risk of unauthorized disclosure.
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Access Control for Fine-Tuned Model and Vector Database: Implement strict access controls and authentication mechanisms to restrict access to the fine-tuned LLM model and any associated vector databases. Only authorized individuals should be granted access.
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Enforce API Access Control: Implement robust access control measures for LLM APIs, including authentication, authorization, and rate limiting, to prevent unauthorized access or abuse of the API endpoints.
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Log Access Details: Maintain comprehensive logs of API access to the LLM and vector database, capturing information such as the user, timestamp, and details of the accessed data. This information can be crucial for auditing, monitoring, and detecting potential security incidents.
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Clean Data to Reduce Bias: Thoroughly clean and preprocess training data to minimize bias and ensure fair and unbiased behavior of the LLM. Regularly review and update the training data to avoid perpetuating biases.
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Implement Guardrails: Integrate guardrails into the LLM's output validation process using open-source libraries like guardrails.ai. This helps verify the model's outputs for compliance, ethics, and other predefined criteria before the results are presented or acted upon.
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Conduct Internal and External Red Team Testing: Perform rigorous testing of the LLM both internally and through external red team engagements. This helps identify vulnerabilities, weaknesses, and potential attack vectors to address before deploying the model into production.
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Prevent Prompt Injection: Validate and sanitize user prompts to prevent prompt injection attacks, where malicious input is used to manipulate or exploit the LLM's behavior. Implement input validation techniques to ensure that user prompts meet specific criteria.
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Validate Chain of Inputs: When using AutoGPT or plug-in modules, validate and sanitize inputs at each step of the chain to ensure the integrity and security of the data. Avoid blindly trusting inputs from upstream sources without appropriate validation.
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Collaborate with InfoSec Team: Engage and collaborate with your information security (InfoSec) team throughout the development and deployment process. Involve them in security assessments, risk analysis, and compliance evaluations to address any potential security concerns or doubts.
Note: These guidelines provide a high-level overview and should be tailored to the specific requirements and risk profile of your application. Consulting with security experts and adhering to relevant industry standards and best practices is essential.
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Ken Huang
CEO
DistributedApps
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