Building Secure and Responsible LLM Applications Training Course
LLM application security constitutes the discipline of designing, constructing, and sustaining safe, trustworthy, and policy-compliant systems that leverage large language models.
This instructor-led, live training (available online or onsite) is designed for intermediate to advanced AI developers, architects, and product managers aiming to identify and mitigate risks inherent in LLM-powered applications, such as prompt injection, data leakage, and unfiltered outputs, while implementing security controls like input validation, human-in-the-loop oversight, and output guardrails.
Upon completion of this training, participants will be able to:
- Grasp the core vulnerabilities of LLM-based systems.
- Implement secure design principles within LLM application architecture.
- Utilize tools such as Guardrails AI and LangChain for validation, filtering, and safety assurance.
- Integrate techniques including sandboxing, red teaming, and human-in-the-loop reviews into production-grade pipelines.
Course Format
- Interactive lectures and discussions.
- Extensive exercises and practical work.
- Hands-on implementation within a live-lab environment.
Course Customization Options
- To request a customized training session for this course, please contact us to arrange details.
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 usage.
Prompt Injection and Jailbreak Attacks
- Understanding prompt injection and its dangers.
- Scenarios involving direct and indirect prompt injection.
- Techniques used to bypass safety filters (jailbreaking).
- 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
- Determining where and when to introduce human oversight.
- Managing approval queues, scoring thresholds, and fallback handling.
- Calibrating trust and the role of explainability.
Secure LLM Application Design Patterns
- Applying least privilege 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 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.
- Experience developing LLM applications using Python.
- Familiarity with API integrations and cloud-based deployments.
Target Audience
- AI developers.
- Application and solution architects.
- Technical product managers working with LLM tools.
Open Training Courses require 5+ participants.
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