Large language models (LLMs) are powerful but also vulnerable to misuse. Malicious users can craft prompts that bypass safeguards, extract sensitive data, or hijack AI behavior. Rebuff is a Python-based framework designed to defend LLM applications from prompt injection and adversarial attacks.
What is Rebuff? Rebuff sits between the user and your LLM, inspecting prompts and responses to detect anomalies, block known attack vectors, and log suspicious activity. It offers out-of-the-box protection against jailbreak attempts and malicious input patterns.
How to Use This Course: This course teaches you to secure AI apps using Rebuff. You’ll learn about the types of LLM attacks, how to configure Rebuff’s defense engine, implement security policies, monitor attack attempts, and create custom mitigation rules. You’ll integrate Rebuff into chatbot flows, RAG pipelines, and public APIs to build resilient AI systems.
By the end, you’ll have a production-ready security layer that adapts to evolving threats—protecting both users and AI agents.
Course/Topic 1 - Coming Soon
The videos for this course are being recorded freshly and should be available in a few days. Please contact info@uplatz.com to know the exact date of the release of this course.
Upon course completion, learners will receive a Uplatz Certificate of Completion certifying their expertise in securing LLM applications using Rebuff. This certification signals your ability to detect, block, and respond to adversarial prompt behavior in real-time—making you a valuable asset for AI operations, security, and MLOps teams. Whether you're developing AI tools or deploying public-facing agents, your certified skills will demonstrate your commitment to safe and responsible AI use.
With AI systems now facing increasing regulatory and cybersecurity scrutiny, developers with LLM security expertise are in high demand. Rebuff gives you a foundation in real-world prompt defense.
Career roles include:
AI Security Engineer
LLM Application Defender
Adversarial Prompt Analyst
AI Trust & Safety Engineer
MLOps Security Specialist
Prompt Injection Detection Developer
These roles exist in AI startups, security teams, finance, healthcare, and SaaS platforms—where prompt-based attacks can lead to misinformation, data leaks, or service misuse. As LLMs scale into mission-critical tools, securing them becomes a top priority.
What is Rebuff? Rebuff is an open-source framework that detects and mitigates prompt injection attacks in LLM applications.
How does Rebuff protect LLM apps? It analyzes input/output prompts in real-time and applies rules or ML-based filters to block unsafe interactions.
Can Rebuff be used with LangChain? Yes, Rebuff integrates easily into LangChain chains to secure LLM-based workflows.
What are prompt injection attacks? These are malicious prompts crafted to override instructions, bypass safety, or manipulate AI output.
Does Rebuff require training data? No, it uses predefined patterns, heuristics, and configurable rules. You can add your own filters.
Can Rebuff log and monitor blocked attacks? Yes, it supports audit logging to review attack attempts and monitor system health.
Is Rebuff open-source? Yes, Rebuff is free and customizable under an open-source license.
Can Rebuff stop all attacks? While no system is foolproof, Rebuff significantly reduces risk and detects most common injection patterns.
What’s the difference between Rebuff and Guardrails AI? Rebuff focuses on input/output prompt security, while Guardrails focuses on structured output validation.
Why is prompt injection dangerous? It allows users to bypass controls, access restricted functions, or produce misleading or harmful content.
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