Building Secure and Responsible LLM Applications Training Course
The security of LLM applications involves the creation and maintenance of safe, reliable, and compliant systems through the use of large language models.
This instructor-led training session (available both online and in-person) is designed for intermediate to advanced AI developers, architects, and product managers who aim to recognize and reduce risks associated with applications powered by LLMs. These risks include prompt injection, data leakage, and unfiltered output, while also integrating security measures such as input validation, human oversight, and controlled outputs.
Upon completion of this training, participants will be able to:
- Identify the primary vulnerabilities in systems based on LLMs.
- Incorporate secure design principles into the architecture of LLM applications.
- Leverage tools like Guardrails AI and LangChain for validation, filtering, and safety measures.
- Implement techniques such as sandboxing, red teaming, and human oversight in production pipelines.
Course Format
- Interactive lectures and discussions.
- Numerous exercises and practice sessions.
- Practical implementation within a live-lab environment.
Customization Options for the Course
- To request a tailored training session, please contact us to make arrangements.
Course Outline
Overview of LLM Architecture and Attack Surface
- How LLMs are built, deployed, and accessed via APIs
- Key components in LLM app stacks (e.g., prompts, agents, memory, APIs)
- Where and how security issues arise in real-world use
Prompt Injection and Jailbreak Attacks
- What is prompt injection and why it’s dangerous
- Direct and indirect prompt injection scenarios
- Jailbreaking techniques to bypass safety filters
- Detection and mitigation strategies
Data Leakage and Privacy Risks
- Accidental data exposure through responses
- PII leaks and model memory misuse
- Designing privacy-conscious prompts and retrieval-augmented generation (RAG)
LLM Output Filtering and Guarding
- Using Guardrails AI for content filtering and validation
- Defining output schemas and constraints
- Monitoring and logging unsafe outputs
Human-in-the-Loop and Workflow Approaches
- Where and when to introduce human oversight
- Approval queues, scoring thresholds, fallback handling
- Trust calibration and role of explainability
Secure LLM App Design Patterns
- Least privilege and sandboxing for API calls and agents
- Rate limiting, throttling, and abuse detection
- Robust chaining with LangChain and prompt isolation
Compliance, Logging, and Governance
- Ensuring auditability of LLM outputs
- Maintaining traceability and prompt/version control
- Aligning with internal security policies and regulatory needs
Summary and Next Steps
Requirements
- An understanding of large language models and prompt-based interfaces
- Experience building 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
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