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
 14 Hours

Delivery Options

Private Group Training

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  • Delivered online, onsite/classroom or hybrid by experts sharing real world experience

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