Building Secure and Responsible LLM Applications Training Course
Security for Large Language Model applications involves the practice of designing, developing, and sustaining systems that are safe, reliable, and adhere to policy standards by leveraging large language models.
This instructor-led, live training (available online or onsite) targets intermediate to advanced AI developers, architects, and product managers who aim to identify and reduce risks linked to LLM-powered applications, such as prompt injection, data leakage, and unfiltered outputs, while implementing security measures like input validation, human-in-the-loop oversight, and output guardrails.
Upon completion of this training, participants will be capable of:
- Grasping the fundamental vulnerabilities of LLM-based systems.
- Implementing secure design principles within LLM application architecture.
- Utilizing tools such as Guardrails AI and LangChain for validation, filtering, and safety enhancements.
- Integrating 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 practice.
- Hands-on implementation within a live lab environment.
Customization Options
- To arrange customized training for this course, please contact us.
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
- A solid understanding of large language models and prompt-based interfaces
- Experience in 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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