Building Secure and Responsible LLM Applications Training Course
Security in Large Language Model (LLM) applications involves the design, construction, and maintenance of systems that are safe, trustworthy, and adhere to established policies while leveraging LLMs.
This instructor-led, live training session (available online or in-person) is designed for AI developers, architects, and product managers with intermediate to advanced skill levels. Participants will learn to identify and mitigate risks associated with LLM-driven applications, such as prompt injection, data leaks, and unfiltered responses, by implementing security measures like input validation, human oversight, and output guardrails.
Upon completing this training, attendees will be capable of:
- Grasping the fundamental vulnerabilities inherent in LLM-based systems.
- Implementing secure design principles within LLM application architectures.
- Utilizing tools such as Guardrails AI and LangChain to ensure validation, filtering, and safety.
- Incorporating techniques like sandboxing, red teaming, and human-in-the-loop reviews into production-ready pipelines.
Course Format
- Engaging lectures and discussions.
- Extensive exercises and practical practice.
- Hands-on implementation within a live laboratory environment.
Customization Options
- For those interested in a tailored training experience for this course, please reach out to us to arrange it.
Course Outline
LLM Architecture and Attack Surface Overview
- Methods for building, deploying, and accessing LLMs via APIs
- Essential components within LLM application stacks (e.g., prompts, agents, memory, APIs)
- Identification and analysis of security issues in real-world scenarios
Prompt Injection and Jailbreak Attacks
- Definition of prompt injection and its associated dangers
- Scenarios involving direct and indirect prompt injection
- Techniques used for jailbreaking to bypass safety filters
- Strategies for detection and mitigation
Data Leakage and Privacy Risks
- Unintentional data exposure through system responses
- Leaks of Personally Identifiable Information (PII) and misuse of model memory
- Designing privacy-preserving prompts and retrieval-augmented generation (RAG) approaches
LLM Output Filtering and Protection
- Utilizing Guardrails AI for content filtering and validation
- Establishing output schemas and constraints
- Monitoring and logging unsafe outputs
Human-in-the-Loop and Workflow Strategies
- Determining optimal points for introducing human oversight
- Managing approval queues, scoring thresholds, and fallback mechanisms
- Calibrating trust and the role of explainability
Secure LLM Application Design Patterns
- Implementing least privilege and sandboxing for API calls and agents
- Applying rate limiting, throttling, and abuse detection
- Ensuring robust chaining with LangChain and prompt isolation
Compliance, Logging, and Governance
- Ensuring the auditability of LLM outputs
- Maintaining traceability and version control for prompts
- Aligning with internal security policies and regulatory requirements
Summary and Next Steps
Requirements
- Familiarity with large language models and prompt-based interfaces
- Practical experience developing LLM applications using Python
- Knowledge of API integrations and cloud-based deployments
Target Audience
- AI developers
- Application and solution architects
- Technical product managers collaborating with LLM tools
Open Training Courses require 5+ participants.
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