• Understand common security threats to LLMs

• Install and configure Rebuff for real-time protection

• Detect and mitigate prompt injection attacks

• Analyze and respond to jailbreak prompt attempts

• Customize filters for your use case and model type

• Integrate Rebuff into Python, LangChain, or FastAPI projects

• Log, audit, and monitor blocked prompt traffic

• Design policies for user-level or app-level security

• Reduce risks in public LLM endpoints

• Apply Rebuff in RAG, chatbot, and agent-based architectures

Course Syllabus

1. Introduction to LLM Security and Rebuff

2. Types of Prompt Injection and Jailbreak Attacks

3. Installing and Configuring Rebuff in Python Projects

4. Defining Detection Patterns and Mitigation Rules

5. Using Rebuff in Chatbots and RAG Pipelines

6. Custom Security Policies for Different LLM Workflows

7. Logging and Monitoring Prompt Traffic for Threats

8. Integrating Rebuff with FastAPI and LangChain

9. Handling Advanced Attacks and False Positives

10. Case Study: Securing an OpenAI-Powered Helpdesk Bot

11. Evaluating and Testing LLM Security Posture

12. Deploying Rebuff in Production Environments

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.

1. What is Rebuff?
   Rebuff is an open-source framework that detects and mitigates prompt injection attacks in LLM applications.

2. 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.

3. Can Rebuff be used with LangChain?
   Yes, Rebuff integrates easily into LangChain chains to secure LLM-based workflows.

4. What are prompt injection attacks?
   These are malicious prompts crafted to override instructions, bypass safety, or manipulate AI output.

5. Does Rebuff require training data?
   No, it uses predefined patterns, heuristics, and configurable rules. You can add your own filters.

6. Can Rebuff log and monitor blocked attacks?
   Yes, it supports audit logging to review attack attempts and monitor system health.

7. Is Rebuff open-source?
   Yes, Rebuff is free and customizable under an open-source license.

8. Can Rebuff stop all attacks?
   While no system is foolproof, Rebuff significantly reduces risk and detects most common injection patterns.

9. What’s the difference between Rebuff and Guardrails AI?
   Rebuff focuses on input/output prompt security, while Guardrails focuses on structured output validation.

10. Why is prompt injection dangerous?
    It allows users to bypass controls, access restricted functions, or produce misleading or harmful content.