Building Secure and Responsible LLM Applications Training Course
LLM application security refers to the discipline of designing, building, and maintaining safe, trustworthy, and policy-compliant systems using large language models.
This instructor-led, live training (available online or on-site) is designed for intermediate to advanced-level AI developers, architects, and product managers who aim to identify and mitigate risks associated with LLM-powered applications. These risks include prompt injection, data leakage, and unfiltered output, while integrating essential security controls such as input validation, human-in-the-loop oversight, and output guardrails.
By the end of this training, participants will be able to:
- Understand the core vulnerabilities inherent in LLM-based systems.
- Apply secure design principles to LLM application architecture.
- Leverage tools like Guardrails AI and LangChain for validation, filtering, and safety assurance.
- Integrate 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 hands-on practice.
- Live-lab implementation in a real-time environment.
Customisation Options
- To request a customised version of this course, please contact us to arrange.
Course Outline
Overview of LLM Architecture and Attack Surface
- How LLMs are built, deployed, and accessed via APIs
- Key components in LLM application stacks (e.g., prompts, agents, memory, APIs)
- Where and how security issues arise in real-world scenarios
Prompt Injection and Jailbreak Attacks
- What prompt injection is and why it poses a significant risk
- Direct and indirect prompt injection scenarios
- Jailbreaking techniques used to bypass safety filters
- Detection and mitigation strategies
Data Leakage and Privacy Risks
- Accidental data exposure through model responses
- PII leaks and misuse of model memory
- Designing privacy-conscious prompts and retrieval-augmented generation (RAG) strategies
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, and fallback handling
- Trust calibration and the role of explainability
Secure LLM App Design Patterns
- Implementing 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 requirements
Summary and Next Steps
Requirements
- A solid understanding of large language models and prompt-based interfaces
- Practical 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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