LLM applications face four primary security risks: availability breakdowns, integrity violations, privacy compromises, and abuse, which can be mitigated using guardrails.
Implement input guardrails to enforce token limits and output guardrails to detect non-refusal patterns, preventing denial-of-service and identifying model limitations.
Use guardrails to filter gibberish, enforce language consistency, block malicious URLs, check for relevance, and manage response length to maintain output quality.
Prompt injection manipulates an AI model by embedding malicious instructions within user input, similar to SQL injection, requiring specific guardrails for detection.
Use anonymizers like Microsoft Presidio to detect and redact sensitive information such as names and phone numbers from both user inputs and model outputs.
Implement guardrails to block code execution, filter competitor mentions, detect toxicity and bias, and defend against 'Do Anything Now' (DAN) jailbreaking attacks.
A demonstration in Java shows how to create input and output guardrails that use a model to detect violent content and verify URL reachability before processing.
Retrieval-Augmented Generation (RAG) introduces new vulnerabilities, such as poisoned documents and vector store attacks, that require specialized security measures.
Building trustworthy AI requires a strategic application of guardrails tailored to your specific needs, balancing security with performance to navigate the complex landscape.
23:24Keno Dreßel
30:36Mackenzie Jackson
25:17Scott Hanselman
24:11Andreas Erben
26:47Alejandro Saucedo
35:37Liran Tal
27:10Georg Dresler
23:50Aditi Godbole
Jobs that call for the skills explored in this talk.
TRUSTEQ GmbH