Mastering LLM Security(MLS)

The Mastering Large Language Model (LLM) Security course covers LLM fundamentals, data collection and preprocessing, threat modeling for models, prompt-injection, data-poisoning, model-extraction, adversarial examples, OWASP Top-10 for LLMs, jailbreaking, supply-chain risks, and secure deployment & infrastructure hardening. 

Students must complete all steps in this course to qualify for the Mastering LLM Security (MLS) certification exam from DarkRelay. Earn the Mastering LLM Security (MLS) certification by passing the MLS exam. Passing the exam certifies you to perform the following:

• Manage AI/LLM security pre-engagement and risk scoping.

• Understand model governance, compliance, and data handling requirements.

• Design secure data collection, tokenization, and dataset management.

• Conduct adversarial testing: prompt injection, jailbreaking, adversarial examples, and model extraction tests.

• Perform supply-chain and dependency security assessments for model training & deployment.

• Evaluate model robustness using measurement techniques (confusion/precision/accuracy analysis) and hardening approaches.

• Implement runtime mitigations: input validation, prompt isolation, ACLs and guardrails.

• Execute infrastructure security checks for LLM deployments (inference servers, plugins, and integrations).

• Write technical security reports with remediation guidance for models, pipelines, and deployed services.

• Lead LLM security assessments and advise stakeholders on mitigation and secure lifecycle practices.

Who Should Attend?

• Security Researchers

• Software Developers

• QA Engineers

• Penetration Testers & Red Teamers

• Application Security Engineers

• Security Architects

• LLM Enthusiasts & AI Engineers

Pre-Requisites

Students should have completed or be comfortable with:

• Introduction to Linux

• Introduction to Cybersecurity

• Network Fundamentals

• Python 101

Course Objectives

The mastering LLM security course objectives include:

• Understand LLM architecture (transformers, attention) and stages of model development.

• Identify and defend against LLM-specific attack vectors (data poisoning, model extraction, prompt injection).Apply manual and automated testing techniques for LLMs, including prompt injection frameworks and jailbreaking methods.

• Measure model behavior and robustness; use metrics to guide mitigation and hardening.

• Design and implement input validation, prompt isolation, ACLs, and guardrails for safe inference.

• Assess supply-chain risks, plugin security, and third-party dependencies used in LLM pipelines.

• Perform infrastructure security reviews for inference serving, plugins, and integrations.

• Document findings, produce remediation plans, and communicate risk to technical and non-technical stakeholders.