The LLM security Guardrails baseline provide a comprehensive approach to ensure the secure design, implementation, and monitoring of Large Language Models, emphasizing best practices for continuous improvement and risk mitigation.
Comprehensive Guidelines for Secure Language Model Management
1. Access Control: Limit LLM privileges to the minimum necessary, preventing unauthorized state alterations.
Ex: Restrict LLM access to sensitive user data in a customer support application, ensuring only authorized personnel can modify or access confidential information.
2. Enhanced Input Validation: Implement robust input validation to filter malicious prompt inputs from untrusted sources.
Ex: Validate and filter user input in a chatbot to prevent malicious prompts, ensuring that only legitimate and safe queries are processed.
3. Segregation and Control of External Content Interaction: Segregate untrusted content, especially with plugins, to prevent irreversible actions or PII exposure.
Ex: In a content generation platform, segregate user-uploaded files from external plugins to prevent plugins from executing actions that may compromise user privacy or security.
4. Manage Trust: Establish trust boundaries, treat LLM as an untrusted user, and apply proper input validation to maintain user control.
Ex: Treat the LLM in a virtual assistant as an untrusted entity, implementing rigorous input validation to maintain user control over sensitive tasks and interactions.
5. Verify Training Data and Legitimacy: Verify external training data supply chain, maintain attestations, and use different models for varied use-cases.
Ex: In a language translation model, thoroughly vet external training data sources, ensuring data legitimacy and diversity to enhance the model's accuracy across various language nuances.
6. Resource Use and Limitations: Limit resource use per request and step, limiting queued and total actions to control system reactions.
Ex: Cap the processing resources for an image recognition model per user request, preventing excessive resource consumption and ensuring fair usage.
7. Vulnerability Scanning and Code Signing: Scan components, deploy code signing, and conduct adversarial robustness tests on models and data throughout the pipeline.
Ex: Before deploying a sentiment analysis model, conduct vulnerability scans on model components, deploy code signing to verify authenticity, and perform adversarial robustness tests.
8. Auditing and Supplier Security: Audit sensitive plugins, vet sources, and monitor supplier security and access.
Ex: Audit plugins in a content creation tool, vetting their security measures and monitoring access to sensitive functions to prevent unauthorized actions.
9. Data Sanitization and Scrubbing: Implement robust input validation, ongoing supply chain risk mitigation, and LLM red team exercises.
Ex: In a document summarization LLM, implement input validation to sanitize user-provided text, mitigating the risk of injecting malicious content into the model.
10. Permissions and Rate Limiting: Reduce LLM permissions, implement rate-limiting, and use human-in-the-loop control.
Ex: Reduce permissions for an LLM in a financial application, limiting its ability to perform high-risk actions, and implement rate-limiting to prevent abuse.
11. Continuous Monitoring and Fact-Checking: Regularly monitor LLM outputs, fact-check information, and employ model tuning techniques for risk reduction.
Ex: Continuously monitor outputs of a news article generation model, fact-checking information against reliable sources to ensure accuracy and avoid misinformation.
12. Validation Mechanisms and Risk Communication: Set up automatic validation mechanisms, strictly parameterize plugin calls, and improve risk communication practices.
Ex: Establish validation mechanisms for a medical diagnosis LLM, parameterizing plugin calls strictly to ensure safe and accurate predictions. Improve risk communication to convey model limitations and uncertainties to users.
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