š¤ AI Security Assessment Lab
Comprehensive AI/ML system security testing - From adversarial attacks to model extraction
Expert Level LabLab Overview
This advanced lab provides hands-on experience with AI security assessment techniques. You'll test machine learning models for vulnerabilities, perform adversarial attacks, analyze model robustness, and understand the unique security challenges of AI systems. The lab covers both offensive AI security testing and defensive hardening techniques.
Learning Objectives
- Execute adversarial attacks on machine learning models
- Perform model extraction and inference attacks
- Test AI systems for data poisoning vulnerabilities
- Assess model robustness and evasion resistance
- Implement AI security defenses and hardening
- Understand privacy implications in AI systems
Prerequisites
- Basic understanding of machine learning concepts
- Python programming experience
- Knowledge of neural networks and deep learning
- Familiarity with PyTorch or TensorFlow
- Understanding of cybersecurity fundamentals
šļø Lab Environment Setup
Environment Requirements
Setting up the AI security testing environment with all necessary tools and frameworks.
- Python 3.8+ with ML libraries
- Jupyter Notebook environment
- GPU support for model training
- Adversarial Robustness Toolbox
- CleverHans library
- Foolbox framework
Target Models
Pre-trained models and datasets for security testing scenarios.
- Image classification models (CIFAR-10, ImageNet)
- Natural language processing models
- Malware detection classifiers
- Fraud detection systems
- Facial recognition models
Testing Frameworks
Specialized tools and frameworks for AI security assessment.
- Adversarial Robustness Toolbox (ART)
- CleverHans adversarial examples
- Foolbox attack implementations
- TextAttack for NLP models
- Custom attack implementations
šÆ Lab Exercises
Exercise 1: Adversarial Example Generation
Objective: Generate adversarial examples using multiple attack methods to fool image classification models.
Duration: 2-3 hours
Scenario: You're testing a facial recognition system used for access control. Generate adversarial examples that can bypass the system while maintaining visual similarity to the original image.
Tasks:
- Load a pre-trained image classification model
- Implement FGSM (Fast Gradient Sign Method) attacks
- Execute PGD (Projected Gradient Descent) attacks
- Generate Carlini & Wagner (C&W) adversarial examples
- Compare attack success rates and perturbation levels
- Test adversarial examples in physical world conditions
Expected Outcomes:
- Successfully generate adversarial examples for multiple attack methods
- Understand trade-offs between attack success and perturbation visibility
- Analyze model vulnerability to different attack types
Exercise 2: Model Extraction Attack
Objective: Extract a machine learning model through black-box queries and train a surrogate model.
Duration: 3-4 hours
Scenario: A company has deployed a proprietary malware detection API. Your task is to extract the underlying model without direct access to its parameters.
Tasks:
- Set up a black-box model API simulation
- Generate synthetic training data
- Query the target model to collect training samples
- Train a surrogate model using collected data
- Evaluate surrogate model fidelity
- Analyze extraction efficiency and data requirements
Expected Outcomes:
- Successfully extract a functional model replica
- Understand the relationship between query budget and extraction success
- Identify defense mechanisms against model extraction
Exercise 3: Data Poisoning Attack
Objective: Poison a machine learning model's training data to introduce backdoors or degrade performance.
Duration: 2-3 hours
Scenario: Test the resilience of a spam detection system against data poisoning attacks that could allow malicious emails to bypass filtering.
Tasks:
- Prepare clean training dataset
- Design backdoor triggers for email classification
- Inject poisoned samples into training data
- Train model with poisoned dataset
- Test backdoor activation with trigger patterns
- Analyze impact on overall model performance
Expected Outcomes:
- Successfully implement backdoor attacks
- Understand data poisoning attack vectors
- Evaluate model resilience to poisoning
Exercise 4: Privacy Attack Analysis
Objective: Perform membership inference and model inversion attacks to extract sensitive information.
Duration: 2-3 hours
Scenario: Assess the privacy risks of a machine learning model trained on sensitive healthcare data.
Tasks:
- Implement membership inference attacks
- Perform model inversion to reconstruct training data
- Analyze attribute inference capabilities
- Test differential privacy defenses
- Evaluate privacy-utility trade-offs
- Implement privacy-preserving techniques
Expected Outcomes:
- Understand privacy risks in machine learning
- Implement privacy attack techniques
- Evaluate privacy-preserving defenses
Exercise 5: AI Security Defense Implementation
Objective: Implement and test various AI security defense mechanisms.
Duration: 3-4 hours
Scenario: Hardening an AI system against the attacks learned in previous exercises.
Tasks:
- Implement adversarial training defense
- Deploy input preprocessing techniques
- Test model ensemble approaches
- Implement detection-based defenses
- Apply certified defense methods
- Evaluate defense effectiveness against multiple attacks
Expected Outcomes:
- Understand AI security defense mechanisms
- Implement robust AI security controls
- Evaluate defense trade-offs and limitations
š ļø Lab Tools & Resources
Attack Frameworks
- Adversarial Robustness Toolbox: IBM's comprehensive attack library
- CleverHans: TensorFlow adversarial examples library
- Foolbox: Python adversarial attacks framework
- TextAttack: NLP adversarial attacks
- ART: Adversarial Robustness Toolbox
Defense Tools
- Defense-GAN: Generative adversarial defense
- MADRY: Adversarial training framework
- Certified Defenses: Provably robust defenses
- Differential Privacy: Privacy-preserving ML
- Federated Learning: Distributed ML security
Analysis Tools
- MLflow: ML lifecycle management
- Weights & Biases: Experiment tracking
- TensorBoard: Model visualization
- SHAP: Model interpretability
- LIME: Local interpretable explanations
š Lab Assessment
Attack Success Metrics
Measuring the effectiveness of adversarial attacks and security assessments.
- Attack success rate percentage
- Perturbation magnitude (L2, Lā norms)
- Query efficiency for black-box attacks
- Transferability across model architectures
- Physical world attack success rates
Defense Evaluation
Assessing the robustness of implemented security defenses.
- Robust accuracy against attacks
- Clean accuracy preservation
- Computational overhead analysis
- Defense generalization across attack types
- Privacy-utility trade-off evaluation
Risk Assessment
Evaluating overall AI system security posture.
- Vulnerability severity classification
- Attack surface analysis
- Threat model completeness
- Security control effectiveness
- Compliance with AI security standards
šÆ Advanced Challenges
Challenge 1: Multi-Modal Attack
Develop adversarial examples that work across multiple input modalities (image + text).
- Cross-modal consistency requirements
- Multi-objective optimization
- Real-world deployment constraints
Challenge 2: Federated Learning Attack
Design attacks against federated learning systems with privacy constraints.
- Byzantine attack simulation
- Privacy budget exploitation
- Distributed system vulnerabilities
Challenge 3: Real-Time Defense
Implement real-time adversarial example detection and mitigation.
- Low-latency detection requirements
- Automated response mechanisms
- Performance optimization techniques
š Lab Deliverables
- Technical Report: Comprehensive analysis of AI security vulnerabilities and defenses
- Attack Implementations: Working code for all demonstrated attack methods
- Defense Strategies: Implemented security controls and their effectiveness
- Risk Assessment: Detailed security risk analysis of tested AI systems
- Recommendations: Best practices and security guidelines for AI deployment
š Additional Resources
- Adversarial Machine Learning - Comprehensive attack and defense guide
- AI Security Best Practices - OWASP ML Security guidelines
- Privacy-Preserving Machine Learning - Differential privacy techniques
- AI Risk Management Framework - NIST AI security guidelines
- Adversarial Examples in Computer Vision - Visual attack techniques