COASP
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ECCouncil
The COASP certification teaches professionals to design, execute, and assess adversarial attacks—including evasion, data poisoning, model extraction, and LLM security testing—ensuring AI systems are robust against emerging threats.
120
Minutes
50
Questions
70/100
Passing Score
$250
Exam Cost
Who Should Take This
It is intended for security engineers, AI researchers, and penetration testers who already possess a solid foundation in machine‑learning concepts and defensive security practices. These individuals seek to deepen their offensive AI expertise to proactively evaluate and harden intelligent applications in enterprise environments.
What's Covered
1
Adversarial ML Fundamentals
2
Evasion Attacks
3
Data Poisoning
4
Model Extraction
5
LLM Security Testing
6
AI Red Teaming
7
AI Supply Chain
8
Defense Validation
9
Generative AI Security
10
Reporting and Remediation
What's Included in AccelaStudy® AI
Adaptive Knowledge Graph
Practice Questions
Lesson Modules
Console Simulator Labs
Exam Tips & Strategy
20 Activity Formats
Course Outline
60 learning goals
1
Adversarial ML Fundamentals
2 topics
Attack taxonomy
- Apply adversarial ML attack classification including evasion poisoning extraction and inference attacks against machine learning systems.
- Analyze adversarial attack surfaces to identify model input output training and deployment vulnerabilities in AI systems.
- Design adversarial threat models incorporating attack categorization capability assessment and defense gap identification.
Threat modeling for AI
- Apply AI-specific threat modeling including ATLAS framework MITRE ATLAS and AI-specific kill chain for security assessment.
- Analyze AI threat landscapes to identify relevant attack vectors actor capabilities and organizational exposure levels.
- Design AI security assessment frameworks incorporating threat modeling attack simulation and defense validation methodologies.
2
Evasion Attacks
2 topics
Image perturbation
- Apply adversarial perturbation techniques including FGSM PGD C&W and patch attacks against computer vision classification models.
- Analyze evasion attack effectiveness to evaluate perturbation visibility detection evasion rates and transferability across models.
- Design evasion testing programs incorporating white-box black-box and transferability assessment for vision model validation.
Text and tabular evasion
- Apply text-based adversarial attacks including synonym substitution character perturbation and semantic preservation for NLP models.
- Analyze text evasion effectiveness to evaluate semantic preservation detection evasion and model behavior under adversarial input.
- Design NLP adversarial testing incorporating automated perturbation human evaluation and domain-specific attack scenarios.
3
Data Poisoning
2 topics
Training data attacks
- Apply data poisoning techniques including label flipping backdoor insertion and clean-label attacks against training datasets.
- Analyze poisoning attack impact to evaluate model behavior changes trigger effectiveness and detection difficulty.
- Design data poisoning assessment incorporating supply chain analysis training pipeline review and detection mechanism testing.
Backdoor attacks
- Apply backdoor attack techniques including trigger pattern insertion model trojan deployment and activation mechanism analysis.
- Analyze backdoor presence using neural cleanse activation clustering and other detection methods for compromised models.
- Design backdoor detection programs incorporating model inspection training data audit and behavioral analysis for validation.
4
Model Extraction
2 topics
Model stealing
- Apply model extraction attacks including API-based querying prediction analysis and model replication for intellectual property theft.
- Analyze extraction attack effectiveness to evaluate model fidelity query efficiency and defense mechanism resistance.
- Design model extraction testing incorporating query strategies fidelity assessment and defense validation for API-served models.
Membership inference
- Apply membership inference attacks to determine whether specific data points were used in model training for privacy assessment.
- Analyze privacy leakage through model outputs to evaluate information disclosure risks and individual privacy impacts.
- Design privacy attack testing incorporating membership inference attribute inference and training data reconstruction assessment.
5
LLM Security Testing
2 topics
Prompt injection
- Apply prompt injection techniques including direct indirect and multi-turn injection attacks against large language model applications.
- Analyze prompt injection results to evaluate defense bypass effectiveness data leakage potential and action hijacking risks.
- Design prompt injection testing programs incorporating automated fuzzing manual crafting and defense validation for LLM apps.
Jailbreaking
- Apply LLM jailbreaking techniques including role-play encoding obfuscation and multi-step manipulation for safety bypass.
- Analyze jailbreak effectiveness to evaluate safety filter resilience content policy enforcement and alignment robustness.
- Design jailbreak testing incorporating technique catalogs automated generation and safety evaluation for model assessment.
6
AI Red Teaming
2 topics
Red team methodology
- Apply AI red teaming methodology including scope definition attack planning execution and finding documentation for assessments.
- Analyze red team results to evaluate defense effectiveness identify systemic weaknesses and prioritize remediation.
- Design AI red team programs incorporating team skills development engagement frameworks and continuous assessment cycles.
Automated testing
- Apply automated AI security testing tools including Counterfit ART TextAttack and model scanning for vulnerability discovery.
- Analyze automated testing results to evaluate coverage effectiveness false positive rates and manual verification needs.
- Design automated AI security testing incorporating tool selection pipeline integration and continuous monitoring strategies.
7
AI Supply Chain
2 topics
Model supply chain
- Apply AI supply chain security assessment including pre-trained model verification fine-tuning pipeline analysis and weight tampering.
- Analyze model supply chain risks to identify compromised models tampered weights and insufficient provenance verification.
- Design model supply chain security incorporating provenance verification integrity checking and trusted model registries.
Dependency security
- Apply AI dependency security assessment including framework vulnerabilities GPU driver exploits and ML library supply chain risks.
- Analyze AI pipeline dependencies to identify vulnerable libraries serialization exploits and infrastructure attack surfaces.
- Design AI dependency management incorporating vulnerability scanning approved library catalogs and continuous monitoring.
8
Defense Validation
2 topics
Defense testing
- Apply AI defense validation including adversarial training robustness testing input sanitization and output filtering assessment.
- Analyze defense effectiveness to evaluate robustness certification adversarial accuracy and defense bypass opportunities.
- Design defense validation programs incorporating benchmark attacks robustness metrics and continuous defense assessment.
Monitoring validation
- Apply AI monitoring system testing including drift detection anomaly alerting and adversarial input detection validation.
- Analyze monitoring gaps to identify blind spots detection latency and insufficient coverage in production AI defense systems.
- Design monitoring validation incorporating synthetic adversarial traffic detection threshold testing and alert pipeline review.
9
Generative AI Security
2 topics
GenAI attacks
- Apply generative AI attacks including deepfake generation voice synthesis AI-generated phishing and synthetic identity creation.
- Analyze generative AI threat capabilities to evaluate detection difficulty social engineering potential and defense requirements.
- Design generative AI defense testing incorporating deepfake detection synthetic content identification and authentication validation.
Content integrity
- Apply AI content authenticity testing including watermark detection C2PA verification and provenance tracking for generated content.
- Analyze content integrity mechanisms to evaluate watermark resilience provenance chain reliability and detection accuracy.
- Design content authenticity programs incorporating watermarking provenance standards and detection tool evaluation.
10
Reporting and Remediation
2 topics
Finding documentation
- Apply AI security assessment documentation including attack reproduction evidence capture and vulnerability classification for AI systems.
- Analyze assessment results to identify systemic AI security weaknesses organizational risk themes and strategic improvement priorities.
- Design AI security reporting frameworks incorporating AI-specific risk metrics attack reproducibility and defense recommendations.
Remediation guidance
- Apply AI security remediation recommendations including adversarial training input validation model hardening and monitoring enhancements.
- Analyze remediation effectiveness to evaluate defense improvement attack surface reduction and residual vulnerability assessment.
- Design AI security improvement roadmaps incorporating phased remediation defense maturity advancement and continuous testing.
Scope
Included Topics
- EC-Council COASP covering offensive AI security including adversarial attacks model exploitation prompt injection and AI red teaming.
- Adversarial machine learning including evasion attacks poisoning attacks model extraction and membership inference attacks.
- LLM security testing including prompt injection jailbreaking system prompt extraction and output manipulation techniques.
- AI red teaming including threat modeling attack simulation defense validation and vulnerability assessment of AI systems.
- AI supply chain security including model tampering training data poisoning and dependency exploitation for AI pipelines.
Not Covered
- AI fundamentals covered by AIE.
- General offensive security covered by CEH/CPENT.
- AI project management covered by CAIPM.
- AI governance covered by CRAGE.
Official Exam Page
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