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 Duration 14 hours

Course Outline

Foundations of Large Language Model (LLM) Architecture and Vulnerability Landscape

  • Methodologies for the construction, deployment, and API-mediated access of LLM systems
  • Core architectural elements within LLM application stacks, including prompt engineering, autonomous agents, memory management, and API interfaces
  • Identification of security vulnerabilities within operational environments for government applications

Prompt Injection Mechanisms and Jailbreak Vectors

  • Analysis of prompt injection threats and their potential impact on system integrity
  • Examination of both direct and indirect prompt injection attack scenarios
  • Review of techniques utilized to circumvent established safety filters
  • Strategies for detecting and mitigating unauthorized manipulation attempts

Data Integrity, Leakage Prevention, and Privacy Protections

  • Prevention of inadvertent data disclosure through model-generated responses
  • Mitigation of Personally Identifiable Information (PII) exposure and improper use of model memory
  • Implementation of privacy-centric prompt design and Retrieval-Augmented Generation (RAG) architectures

LLM Output Validation and Content Guardrails

  • Deployment of Guardrails AI for rigorous content filtering and compliance validation
  • Definition of strict output schemas and operational constraints
  • Establishment of protocols for monitoring and logging non-compliant or unsafe outputs

Human-in-the-Loop Integration and Process Oversight

  • Determination of optimal points for introducing human oversight in automated workflows
  • Implementation of approval queues, performance scoring thresholds, and exception handling procedures
  • Calibration of trust levels and the role of explainability in decision-making processes

Secure Design Patterns for LLM Applications

  • Enforcement of least privilege principles and sandboxing for API interactions and autonomous agents
  • Application of rate limiting, traffic throttling, and abuse detection mechanisms
  • Construction of robust execution chains using LangChain with isolated prompt environments

Regulatory Compliance, Audit Logging, and Governance Frameworks

  • Ensuring full auditability of LLM-generated outputs for accountability
  • Maintenance of precise traceability and version control for prompts and model configurations
  • Alignment of systems with internal security policies and external regulatory requirements for government operations

Conclusion and Strategic Recommendations

Requirements

  • Demonstrated understanding of large language models and prompt-based interfaces
  • Practical experience in developing LLM applications using Python
  • Proficiency with API integrations and cloud-based deployment strategies

Target Audience

  • AI developers
  • Application and solution architects
  • Technical product managers overseeing LLM tool implementations

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