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Course Outline

Overview of Large Language Model Architecture and Threat Landscape

  • An examination of the construction, deployment methodologies, and API access mechanisms for large language models (LLMs).
  • A review of essential components within LLM application stacks, including prompt engineering, agent frameworks, memory systems, and application programming interfaces.
  • An analysis of vulnerabilities and security incidents observed in operational environments for government applications.

Prompt Injection and Bypass Techniques

  • Definition of prompt injection threats and their potential impact on system integrity.
  • Scenarios involving direct and indirect prompt injection vectors.
  • Methods used to circumvent safety controls, including jailbreaking techniques.
  • Strategies for detection and remediation of malicious inputs.

Data Confidentiality and Privacy Risks

  • Risks associated with unintended data exposure through model outputs.
  • Potential for personally identifiable information (PII) leaks and improper utilization of model memory.
  • Best practices for developing privacy-preserving prompts and implementing retrieval-augmented generation (RAG) architectures.

Output Containment and Protective Measures

  • Utilization of Guardrails AI frameworks for content validation and filtering mechanisms.
  • Establishment of strict output schemas and operational constraints.
  • Procedures for monitoring, logging, and responding to unsafe or non-compliant outputs.

Human Oversight and Operational Workflows

  • Criteria for determining appropriate points for human intervention.
  • Implementation of approval workflows, confidence thresholds, and fallback protocols.
  • Approaches to trust calibration and the importance of explainability in decision-making processes.

Secure Design Principles for LLM Applications

  • Application of least privilege principles and sandboxing techniques for API interactions and autonomous agents.
  • Management of request volume through rate limiting, throttling, and detection of abusive patterns.
  • Implementation of secure chaining methods using LangChain with isolated prompt handling for government systems.

Regulatory Compliance, Auditing, and Governance

  • Maintenance of comprehensive audit trails for LLM outputs and actions.
  • Strategies for ensuring traceability, including version control for prompts and model iterations.
  • Alignment with federal security standards, internal governance policies, and regulatory requirements.

Conclusion and Strategic Recommendations

Requirements

  • Demonstrated knowledge of large language models and prompt-driven interaction frameworks
  • Proven capability in developing LLM-based applications utilizing Python
  • Proficiency in managing API integrations and executing cloud-based deployments

Target Audience

  • Artificial intelligence engineers
  • Solution and application architects
  • Technical product managers engaged with LLM tools for government
 14 Hours

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