Hack The Box HTB Certified Offensive AI Expert (HTB COAE) Practice Test
Prepare for the hands-on HTB Certified Offensive AI Expert exam with free practice tests covering the AI Red Teamer path, including prompt injection, adversarial ML, AI data attacks, privacy, evasion, and defense topics.
Mixed Set — HTB COAE Practice Tests
Practice across all 12 AI Red Teamer modules with mixed question sets that reinforce AI security foundations, LLM attack paths, data-layer weaknesses, adversarial evasion, privacy risk, and defensive controls.
Domain Wise — HTB COAE Mock Tests
Target one Offensive AI topic at a time. Each focused mock test maps to a module from the HTB Academy AI Red Teamer path and helps you review core concepts before attempting full mixed sets.
About the HTB COAE Certification Exam
HTB COAE validates practical offensive AI security skills through a real-world style assessment rather than a traditional multiple-choice exam.
What Is HTB COAE?
The Hack The Box Certified Offensive AI Expert (HTB COAE) is a hands-on certification for security professionals who want to assess, exploit, and report vulnerabilities in AI and machine learning systems. It focuses on adversarial machine learning, LLM prompt injection and jailbreaking, LLM output exploitation, AI application and system security, AI defense, and AI privacy.
COAE is best suited for penetration testers, red teamers, application security engineers, AI security specialists, machine learning security researchers, and consultants who need to evaluate AI-driven applications and infrastructure. The related AI Red Teamer path was developed in collaboration with Google and aligns with major AI security frameworks such as Google SAIF, OWASP ML Security Top 10, OWASP Agentic Top 10, and OWASP Top 10 for LLM Applications 2025.
Cybersecurity roles tied to this skill set remain strong. In the United States, information security analysts had a median annual wage of $124,910 in May 2024, and employment is projected to grow 29% from 2024 to 2034. Offensive AI security is a specialized extension of that broader demand, especially as organizations adopt LLMs, agents, and ML-driven decision systems.
Exam Format (2026)
Testing method: Hands-on practical assessment in an HTB Academy exam environment.
Questions: No traditional multiple-choice question count is published. Candidates work through practical objectives in an AI-driven infrastructure.
Duration: 7-day assessment window, including technical work and report preparation.
Question types: Practical exploitation, AI/ML security analysis, evidence collection, and commercial-grade technical reporting.
Passing score: HTB evaluates practical progress and the submitted report; no public MCQ-style scaled score applies.
Exam fee: $490 USD with 1 exam voucher required.
Eligibility Requirements
Required path: Complete the AI Red Teamer Job Role Path before attempting the COAE exam.
Modules: The path includes 12 modules and 230 sections covering AI foundations, LLM attacks, adversarial ML, AI privacy, and defense.
Prerequisites: HTB does not list a separate degree requirement, but candidates should be comfortable with cybersecurity fundamentals, Python-based experimentation, AI/ML concepts, and technical reporting.
Voucher: One voucher is required to start the exam. HTB has described access options that include a voucher valid for 2 attempts.
Reporting: Exploitation alone is not enough. Candidates must submit a professional technical report that explains findings, impact, evidence, and remediation guidance.
HTB COAE Module Weights — AI Red Teamer Path Share
HTB does not publish fixed multiple-choice exam domain percentages for COAE. The table below uses the official AI Red Teamer path section counts as an approximate study-weight guide across the 12 modules.
| Module | Topic | Approx. Path Share |
|---|---|---|
| Module 1 | Fundamentals of AI | 10.4% |
| Module 2 | Applications of AI in Information Security | 10.9% |
| Module 3 | Introduction to Red Teaming AI | 4.8% |
| Module 4 | Prompt Injection Attacks | 5.2% |
| Module 5 | LLM Output Attacks | 6.1% |
| Module 6 | AI Data Attacks | 10.9% |
| Module 7 | Attacking AI - Application and System | 6.1% |
| Module 8 | AI Evasion - Foundations | 5.2% |
| Module 9 | AI Evasion - First-Order Attacks | 10.0% |
| Module 10 | AI Evasion - Sparsity Attacks | 12.2% |
| Module 11 | AI Privacy | 9.1% |
| Module 12 | AI Defense | 9.1% |
How Our Practice Tests Are Designed
Mapped to the AI Red Teamer path — Each topic-wise test aligns with one module from the HTB Academy AI Red Teamer curriculum, including AI foundations, prompt injection, output exploitation, data attacks, evasion, privacy, and defense.
Scenario-based review style — Questions focus on applied reasoning: identifying attack surfaces, recognizing vulnerable AI workflows, interpreting model-risk scenarios, choosing safer mitigations, and understanding how findings should be documented.
Transparent timer approach — The real COAE exam is a 7-day hands-on assessment, not a timed MCQ exam with a public question count. Each 20-question practice test uses an approximate 20-minute learning-check timer to build recall and decision speed without claiming a direct exam-time conversion.
Mixed and focused practice — Use mixed sets to check readiness across the whole path, then use module-wise tests to strengthen weak areas such as AI data attacks, adversarial evasion, LLM output risks, or AI privacy defenses.
HTB COAE Exam Preparation Tips
Study Strategy
Complete the full path: COAE is tied closely to the AI Red Teamer path, so work through all modules, exercises, and skills checks before starting the exam.
Build AI security notes: Keep organized notes for model evaluation terms, LLM attack classes, data pipeline risks, evasion categories, privacy attacks, defensive controls, and reporting language.
Understand concepts, not just steps: The exam rewards candidates who can reason through new AI-driven systems. Focus on why a weakness exists, what impact it creates, and how it can be proven safely.
Practice reporting early: After each lab, write a short finding summary with impact, evidence, reproduction logic, and remediation. This builds the client-ready reporting habit required for HTB practical exams.
Test-Taking Strategy
Scope first: Begin by identifying components, data flows, model touchpoints, APIs, prompts, tools, and trust boundaries before attempting deeper testing.
Capture evidence as you go: Save screenshots, commands, observations, input/output examples, model behavior notes, and mitigation ideas throughout the assessment.
Manage the 7-day window: Do not leave reporting until the end. Draft findings while testing so your final report is complete, readable, and technically defensible.
Prioritize high-impact findings: Focus on exploitable AI workflow weaknesses, meaningful model or data impact, and clear business risk rather than chasing every minor observation.
Frequently Asked Questions
Ready to Test Your HTB COAE Knowledge?
Start with a mixed set to measure your readiness, then use module-wise tests to strengthen weak areas before attempting the 7-day hands-on COAE assessment.
Start HTB COAE Practice Test 1 →
