Beyond the Hype: Building Trustworthy and Reliable LLM Applications with Guardrails

A single malicious prompt can override your AI's instructions. Learn how to build programmatic guardrails to prevent data leaks and model abuse.

#1about 5 minutes

Understanding the four main categories of LLM attacks

LLM applications face four primary security risks: availability breakdowns, integrity violations, privacy compromises, and abuse, which can be mitigated using guardrails.

#2about 2 minutes

Protecting models from availability breakdown attacks

Implement input guardrails to enforce token limits and output guardrails to detect non-refusal patterns, preventing denial-of-service and identifying model limitations.

#3about 5 minutes

Ensuring model integrity with content validation guardrails

Use guardrails to filter gibberish, enforce language consistency, block malicious URLs, check for relevance, and manage response length to maintain output quality.

#4about 3 minutes

Understanding and defending against prompt injection attacks

Prompt injection manipulates an AI model by embedding malicious instructions within user input, similar to SQL injection, requiring specific guardrails for detection.

#5about 3 minutes

Protecting sensitive data with privacy guardrails

Use anonymizers like Microsoft Presidio to detect and redact sensitive information such as names and phone numbers from both user inputs and model outputs.

#6about 4 minutes

Preventing model abuse and harmful content generation

Implement guardrails to block code execution, filter competitor mentions, detect toxicity and bias, and defend against 'Do Anything Now' (DAN) jailbreaking attacks.

#7about 4 minutes

Implementing guardrails with a practical code example

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.

#8about 2 minutes

Addressing unique security risks in RAG systems

Retrieval-Augmented Generation (RAG) introduces new vulnerabilities, such as poisoned documents and vector store attacks, that require specialized security measures.

#9about 2 minutes

Key takeaways for building secure LLM applications

Building trustworthy AI requires a strategic application of guardrails tailored to your specific needs, balancing security with performance to navigate the complex landscape.