Building Secure and Responsible LLM Applications Training Course
LLM application security involves the practice of designing, constructing, and sustaining safe, reliable, and policy-adherent systems that utilise large language models.
This instructor-led, live training (delivered online or onsite) is designed for intermediate to advanced AI developers, architects, and product managers who aim to identify and mitigate risks linked to LLM-powered applications. These risks include prompt injection, data leakage, and unfiltered outputs. Participants will learn to incorporate security controls such as input validation, human-in-the-loop oversight, and output guardrails.
Upon completion of this training, participants will be able to:
- Grasp the core vulnerabilities inherent in LLM-based systems.
- Apply secure design principles to LLM application architecture.
- Utilise tools like Guardrails AI and LangChain for validation, filtering, and ensuring safety.
- 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 practical practice.
- Hands-on implementation within a live-lab environment.
Course Customisation Options
- To request a customised training session for this course, 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 within LLM application stacks (e.g., prompts, agents, memory, APIs).
- Where and how security issues arise in real-world usage.
Prompt Injection and Jailbreak Attacks
- Understanding prompt injection and why it poses a danger.
- Direct and indirect prompt injection scenarios.
- Jailbreaking techniques used to bypass safety filters.
- Strategies for detection and mitigation.
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).
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
- Identifying where and when to introduce human oversight.
- Managing approval queues, scoring thresholds, and fallback handling.
- Calibrating trust and the role of explainability.
Secure LLM App 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 prompt/version control.
- Aligning with internal security policies and regulatory requirements.
Summary and Next Steps
Requirements
- A foundational understanding of large language models and prompt-based interfaces.
- Practical experience 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.
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