AI-ATCK-DEF.AJ1
Adversarial AI Attacks, Mitigations, and Defense Strategies
Explore uCertify's Adversarial AI Attacks, Mitigations, and Defense Strategies course and virtual labs to start building essential security skills today.
- Practice in 18 Hands-On Labs — nothing to install
- 20 Interactive Lessons and 132 topics mapped to the official exam objectives
Expert Self-paced · 1 year access
18 Hands-On LiveLabs
Practice real IT tasks in guided environments.
- Real environments
- Auto-graded
- No installation
20Interactive Lessons
132Topics
18LiveLab
4Videos
190Flashcards
190Glossary of terms
01 / Skills you'll get
What you will be able to do
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Adversarial AI Attacks, Mitigations, and Defense Strategies is a hands‑on, practitioner‑focused course designed to help you understand, break, defend, and secure modern AI systems. From classic ML pipelines to cutting‑edge LLMs and generative AI, you’ll explore how adversarial AI attacks work—and how to stop them.
AI is everywhere—and so are adversarial AI attacks. Models can be poisoned, stolen, manipulated, or tricked into leaking sensitive data. This course teaches you how attackers think, where AI systems break, and how to build resilient defenses using AI security and MLSecOps best practices.
You’ll not only learn what can go wrong, but also how to fix it.
- Launching & Mitigating Attacks: Execute and defend against a full spectrum of adversarial AI attacks, including poisoning, evasion, model extraction, and new-age LLM prompt injection.
- Defense Architectures: Implement robust defense strategies like adversarial training, differential privacy, and privacy-preserving AI techniques.
- Secure by Design: Apply threat modeling and risk assessment to the AI lifecycle (Secure by Design).
- MLSecOps & Governance: Integrate security into the machine learning pipeline using the MLSecOps framework.
- Trustworthy AI Principles: Master the pillars of trustworthy AI to ensure your systems are secure, fair, transparent, and reliable.
Course Highlights
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20 Structured Lessons Comprehensive coverage of core course objectives
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18 Hands-On LiveLabs Interactive guided scenarios with instant evaluation
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1 Year Full Access Self-paced learning accessible anytime on all devices
02 / Lessons & labs
See exactly what you will learn and practice
Lessons
20 Interactive Lessons · 132 topics01 Preface 3 topics +
- Who this course is for
- What this course covers
- To get the most out of this course
02 Getting Started with AI 6 topics +
- Understanding AI and ML
- Types of ML and the ML life cycle
- Key algorithms in ML
- Neural networks and deep learning
- ML development tools
- Summary
03 Building Our Adversarial Playground 6 topics · 1 LiveLab +
- Technical requirements
- Setting up your development environment
- Hands-on basic baseline ML
- Developing our target AI service with CNNs
- ML development at scale
- Summary
1 LiveLab in this lesson — see the labs panel →
04 Security and Adversarial AI 6 topics · 2 LiveLab +
- Technical requirements
- Security fundamentals
- Securing our adversarial playground
- Securing code and artifacts
- Bypassing security with adversarial AI
- Summary
2 LiveLab in this lesson — see the labs panel →
05 Poisoning Attacks 8 topics · 2 LiveLab +
- Basics of poisoning attacks
- Staging a simple poisoning attack
- Backdoor poisoning attacks
- Hidden-trigger backdoor attacks
- Clean-label attacks
- Advanced poisoning attacks
- Mitigations and defenses
- Summary
2 LiveLab in this lesson — see the labs panel →
06 Model Tampering with Trojan Horses and Model Reprogramming 7 topics · 2 LiveLab +
- Injecting backdoors using pickle serialization
- Injecting Trojan horses with Keras Lambda layers
- Trojan horses with custom layers
- Neural payload injection
- Attacking edge AI
- Model hijacking
- Summary
2 LiveLab in this lesson — see the labs panel →
07 Supply Chain Attacks and Adversarial AI 5 topics · 1 LiveLab +
- Traditional supply chain risks and AI
- AI supply chain risks
- Data poisoning
- AI/ML SBOMs
- Summary
1 LiveLab in this lesson — see the labs panel →
08 Evasion Attacks against Deployed AI 7 topics · 1 LiveLab +
- Fundamentals of evasion attacks
- Perturbations and image evasion attack techniques
- NLP evasion attacks with BERT using TextAttack
- Universal Adversarial Perturbations (UAPs)
- Black-box attacks with transferability
- Defending against evasion attacks
- Summary
1 LiveLab in this lesson — see the labs panel →
09 Privacy Attacks – Stealing Models 4 topics · 1 LiveLab +
- Understanding privacy attacks
- Stealing models with model extraction attacks
- Defenses and mitigations
- Summary
1 LiveLab in this lesson — see the labs panel →
10 Privacy Attacks – Stealing Data 8 topics · 2 LiveLab +
- Understanding model inversion attacks
- Types of model inversion attacks
- Example model inversion attack
- Understanding inference attacks
- Attribute inference attacks
- Example attribute inference attack
- Membership inference attacks
- Summary
2 LiveLab in this lesson — see the labs panel →
11 Privacy-Preserving AI 10 topics · 2 LiveLab +
- Privacy-preserving ML and AI
- Simple data anonymization
- Advanced anonymization
- Differential privacy (DP)
- Federated learning (FL)
- Split learning
- Advanced encryption options for privacy-preserving ML
- Advanced ML encryption techniques in practice
- Applying privacy-preserving ML techniques
- Summary
2 LiveLab in this lesson — see the labs panel →
12 Generative AI – A New Frontier 4 topics +
- A brief introduction to generative AI
- Using GANs
- Using pre-trained GANs
- Summary
13 Weaponizing GANs for Deepfakes and Adversarial Attacks 4 topics +
- Use of GANs for deepfakes and deepfake detection
- Using GANs in cyberattacks and offensive security
- Defenses and mitigations
- Summary
14 LLM Foundations for Adversarial AI 7 topics · 1 LiveLab +
- A brief introduction to LLMs
- Developing AI applications with LLMs
- Hello LLM with Python
- Hello LLM with LangChain
- Bringing your own data
- How LLMs change Adversarial AI
- Summary
1 LiveLab in this lesson — see the labs panel →
15 Adversarial Attacks with Prompts 10 topics · 1 LiveLab +
- Adversarial inputs and prompt injection
- Direct prompt injection
- Automated gradient-based prompt injection
- Risks from bringing your own data
- Indirect prompt injection
- Data exfiltration with prompt injection
- Privilege escalation with prompt injection
- RCE with prompt injection
- Defenses and mitigations
- Summary
1 LiveLab in this lesson — see the labs panel →
16 Poisoning Attacks and LLMs 3 topics +
- Poisoning embeddings in RAG
- Poisoning attacks on fine-tuning LLMs
- Summary
17 Advanced Generative AI Scenarios 7 topics +
- Supply-chain attacks in LLMs
- Privacy attacks and LLMs
- Model inversion and training data extraction attacks on LLMs
- Inference attacks on LLMs
- Model cloning with LLMs using a secondary model
- Defenses and mitigations for privacy attacks
- Summary
18 Secure by Design and Trustworthy AI 14 topics · 1 LiveLab +
- Secure by design AI
- Building our threat library
- Industry AI threat taxonomies
- AI threat taxonomy mapping
- Threat modeling for AI
- Threat modelling in action
- Enhanced FoodieAI threat model
- Risk assessment and prioritization
- Security design and implementation
- Testing and verification
- Shifting left – embedding security into the AI life cycle
- Live operations
- Beyond security – Trustworthy AI
- Summary
1 LiveLab in this lesson — see the labs panel →
19 AI Security with MLSecOps 7 topics +
- The MLSecOps imperative
- Toward an MLSecOps 2.0 framework
- Building a primary MLSecOPs platform
- MLSecOps in action
- Integrating MLSecOps with LLMOps
- Advanced MLSecOps with SBOMs
- Summary
20 Maturing AI Security 6 topics · 1 LiveLab +
- Enterprise security AI challenges
- Foundations of enterprise AI security
- Protecting AI with enterprise security
- Operational AI security
- Iterative enterprise security
- Summary
1 LiveLab in this lesson — see the labs panel →
Hands-On Labs Our edge
18 LiveLabs- Building Baseline ML and CNN Models
- Securing the Adversarial AI Playground
- Performing a Simple Evasion Attack
- Demonstrating a Simple Data Poisoning Attack
- Demonstrating a Backdoor Data Poisoning Attack
- Exploiting Pickle Serialization Vulnerability
- Crafting a Neural Payload Attack
- Simulating and Detecting a Data Poisoning Attack
- Performing a Black-Box Adversarial Attack
- Performing a Model Extraction Attack
- Performing a Model Inversion Attack
- Performing an Attribute Inference Attack on the CIFAR-10 CNN Model
- Implementing Image Anonymization Techniques
- Implementing DP in Model Training
- Building a Basic Chat LLM Application
- Exploiting LLMs Using Direct Prompt Injection
- Understanding Secure Design, Threats, and Trustworthy AI
- Strengthening Enterprise AI Security Maturity
Labs run in your browser — nothing to install.
03 / FAQs
Questions before you start
What are Adversarial AI Attacks, Mitigations, and Defense Strategies?+
They are techniques used to attack AI systems and the corresponding defenses used to protect models, data, and pipelines.
Does this course cover AI security for LLMs and generative AI?+
Yes! You’ll learn prompt injection, RAG poisoning, LLM privacy attacks, and GenAI defenses.
Is this course hands‑on?+
Absolutely. Performance‑based labs let you practice real adversarial AI attacks and mitigations.
How does MLSecOps fit into adversarial AI attacks, mitigations, and defense strategies?+
MLSecOps helps operationalize AI security across the ML lifecycle, from training to production.
Keep going
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Ready to Defend AI Like a Pro?
Enroll now and master adversarial AI attacks, mitigations, and defense strategies. Learn how to outthink attackers, secure AI systems, and build trustworthy AI with confidence.
- 1 year of full access
- 18 LiveLab included
- Certificate of completion
No credit card required