Building Secure and Responsible LLM Applications Training Course
LLM application security constitutes the specialized field dedicated to creating, deploying, and sustaining secure, reliable, and policy-compliant systems leveraging large language models.
This instructor-led, live training, available either online or onsite, is designed for intermediate to advanced AI developers, architects, and product managers aiming to identify and mitigate risks linked to LLM-powered applications. Key risks addressed include prompt injection, data leakage, and unfiltered outputs, alongside the implementation of security measures such as input validation, human-in-the-loop oversight, and output guardrails.
Upon completing this training, participants will be capable of:
- Grasping the fundamental vulnerabilities inherent in LLM-based systems.
- Implementing secure design principles within LLM application architecture.
- Utilizing tools like Guardrails AI and LangChain for validation, filtering, and safety enforcement.
- Incorporating techniques such as sandboxing, red teaming, and human-in-the-loop review into production-grade pipelines.
Course Format
- Interactive lectures and discussions.
- Extensive exercises and practical application.
- Hands-on implementation within a live-lab environment.
Course Customization Options
- For tailored training requirements for this course, please contact us to arrange.
Course Outline
Overview of LLM Architecture and Attack Surface
- Understanding how LLMs are built, deployed, and accessed via APIs
- Key components in LLM app stacks (e.g., prompts, agents, memory, APIs)
- Origins and mechanisms of security issues in real-world scenarios
Prompt Injection and Jailbreak Attacks
- Understanding prompt injection and its associated dangers
- Direct and indirect prompt injection scenarios
- Jailbreaking techniques used to bypass safety filters
- Strategies for detection and mitigation
Data Leakage and Privacy Risks
- Preventing accidental data exposure through model responses
- Addressing PII leaks and misuse of model memory
- Designing privacy-conscious prompts and retrieval-augmented generation (RAG) architectures
LLM Output Filtering and Guarding
- Employing Guardrails AI for content filtering and validation
- Defining strict output schemas and constraints
- Monitoring and logging unsafe outputs
Human-in-the-Loop and Workflow Approaches
- Identifying appropriate contexts for introducing human oversight
- Managing approval queues, scoring thresholds, and fallback handling
- Calibrating trust and leveraging explainability
Secure LLM App Design Patterns
- Applying least privilege principles and sandboxing for API calls and agents
- Implementing 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 prompt/version control
- Aligning operations with internal security policies and regulatory requirements
Summary and Next Steps
Requirements
- Understanding of large language models and prompt-based interfaces
- Experience in developing LLM applications using Python
- Familiarity with API integrations and cloud-based deployments
Audience
- AI developers
- Application and solution architects
- Technical product managers working with LLM tools
Need help picking the right course?
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Building Secure and Responsible LLM Applications Training Course - Enquiry
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