Building Secure and Responsible LLM Applications Training Course
LLM application security involves the discipline of designing, constructing, and sustaining safe, reliable, and policy-compliant systems that utilize large language models.
This instructor-led, live training (available online or onsite) is designed for intermediate to advanced AI developers, architects, and product managers who seek to identify and mitigate risks associated with LLM-powered applications, such as prompt injection, data leakage, and unfiltered output. The course also covers 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 inherent in LLM-based systems.
- Apply secure design principles to the architecture of LLM applications.
- Utilize tools such as Guardrails AI and LangChain for validation, filtering, and ensuring safety.
- Incorporate techniques like sandboxing, red teaming, and human-in-the-loop review into production-grade pipelines.
Course Format
- Interactive lectures and discussions.
- Extensive exercises and practical sessions.
- Hands-on implementation within a live-lab environment.
Customization Options
- To request customized training for this course, please contact us to arrange a session.
Course Outline
Overview of LLM Architecture and Attack Surface
- Understanding 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 cases.
Prompt Injection and Jailbreak Attacks
- What prompt injection is 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
- Determining 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.
- Experience in 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.
Custom Corporate Training
Training solutions designed exclusively for businesses.
- Customized Content: We adapt the syllabus and practical exercises to the real goals and needs of your project.
- Flexible Schedule: Dates and times adapted to your team's agenda.
- Format: Online (live), In-company (at your offices), or Hybrid.
Price per private group, online live training, starting from 2600 € + VAT*
Contact us for an exact quote and to hear our latest promotions
(*The final price may vary depending on the technical specialization of the course, the level of customization, the method of delivery and the number of learners)
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