Certified Offensive AI Security Professional (C|OASP) — Lesson 2: Prompt Injection, Agent Abuse, Data Exposure, Guardrails, and Output Validation

Learning Objectives

• Explain the roles, services, data, settings, dependencies, and decision criteria governing prompt-injection risk and agent permissions.
• Implement and validate data exposure, AI guardrails, and output validation using controlled evidence and clear acceptance criteria.
• Evaluate the condition where a basic control passes but AI guardrails fails because data exposure data, configuration order, or dependency assumptions differ and select a proportionate corrective response.

Detailed Topics

• prompt-injection risk applied to the course scenario, expected behavior, dependencies, controls, risks, and evidence.
• agent permissions applied to the course scenario, expected behavior, dependencies, controls, risks, and evidence.
• data exposure applied to the course scenario, expected behavior, dependencies, controls, risks, and evidence.
• AI guardrails applied to the course scenario, expected behavior, dependencies, controls, risks, and evidence.
• output validation applied to the course scenario, expected behavior, dependencies, controls, risks, and evidence.

Instructor Demonstration

The instructor traces an authorized AI red team evaluating model and application weaknesses safely, preserving evidence, and strengthening defensive controls. The walkthrough connects tools, configurations or analysis logic, system messages, decisions, and evidence captured in the AI red-team charter, attack-scenario evidence, defensive-control assessment, and retest report. It also tests the exception where a basic control passes but AI guardrails fails because data exposure data, configuration order, or dependency assumptions differ.

Hands-On Exercise

Complete a controlled, authorized EC-Council exercise covering Prompt Injection, Agent Abuse, Data Exposure, Guardrails, and Output Validation and preserve the verified result in the AI red-team charter, attack-scenario evidence, defensive-control assessment, and retest report.

1. Confirm scope, authorization, prerequisites, versions, identities, dependencies, and acceptance criteria.
2. Execute the workflow while recording settings, outputs, events, decisions, and system messages.
3. Test normal behavior, one boundary condition, and the stated exception without targeting real third-party systems.
4. Document corrections, evidence, ownership, retest criteria, rollback or recovery, and handover actions.

Scenario Analysis

• Identify which object, service, policy, setting, or sequence most strongly influences prompt-injection risk.
• Compare two defensible responses and explain technical, security, operational, legal, and support trade-offs.
• Determine how the AI red-team charter, attack-scenario evidence, defensive-control assessment, and retest report should be approved, retained, retested, and updated.

Troubleshooting Guidance

Start where expected behavior first differs from the observed result. Check authorization, prerequisites, identity, configuration order, data quality, version compatibility, connectivity, status, resource limits, and evidence separately. Treat “a basic control passes but AI guardrails fails because data exposure data, configuration order, or dependency assumptions differ” as a hypothesis until logs, settings, captured outputs, and retest outcomes confirm the cause.

Validation Checklist

• Required components, identities, permissions, settings, safety controls, and dependencies are present.
• The AI red-team charter, attack-scenario evidence, defensive-control assessment, and retest report connects each conclusion to objective, reviewable evidence.
• Normal processing, a boundary condition, dependency behavior, and the stated exception were tested.
• Corrections, ownership, retest criteria, rollback or recovery, documentation, and operational handover are complete.

Lesson Outcome

Learners can apply Prompt Injection, Agent Abuse, Data Exposure, Guardrails, and Output Validation, explain the resulting security, governance, business, or operational behavior, and defend the outcome through the AI red-team charter, attack-scenario evidence, defensive-control assessment, and retest report.