🤖 AI Security

Adversarial Examples

Creating adversarial inputs to fool machine learning models.

• Fast Gradient Sign Method (FGSM)
• Projected Gradient Descent (PGD)
• Carlini & Wagner (C&W) attacks
• DeepFool attack methodology

Evasion Attacks

Bypassing AI security systems through adversarial inputs.

• Malware detection evasion
• Spam filter bypassing
• Intrusion detection evasion
• Image recognition fooling

Physical World Attacks

Creating adversarial examples that work in physical environments.

• Adversarial patches and stickers
• 3D adversarial objects
• Lighting and camera angle attacks
• Real-world robustness testing

Training Data Poisoning

Corrupting training data to compromise AI model behavior.

• Label flipping attacks
• Backdoor data injection
• Feature poisoning techniques
• Clean-label poisoning

Backdoor Attacks

Embedding hidden triggers in AI models for malicious activation.

• Backdoor trigger design
• Model backdoor injection
• Transfer learning backdoors
• Backdoor detection techniques

Supply Chain Attacks

Compromising AI systems through supply chain vulnerabilities.

• Malicious dataset injection
• Pre-trained model backdoors
• Framework vulnerability exploitation
• Model marketplace attacks

Model Extraction Attacks

Stealing AI models through query-based attacks.

• Black-box model extraction
• API-based model stealing
• Membership inference attacks
• Model inversion techniques

Privacy Attacks

Extracting sensitive information from AI models and training data.

• Differential privacy attacks
• Federated learning attacks
• Model parameter inference
• Training data reconstruction

Inference Attacks

Extracting information about training data from model outputs.

• Property inference attacks
• Attribute inference attacks
• Model memorization testing
• Gradient-based attacks

Model Integrity

Protecting AI models from unauthorized modifications.

• Model watermarking techniques
• Digital signatures for models
• Model versioning and integrity
• Secure model deployment

Model Robustness

Building AI models resistant to adversarial attacks.

• Adversarial training methods
• Robust optimization techniques
• Certified defenses
• Ensemble defense strategies

Model Monitoring

Detecting attacks and anomalies in AI systems.

• Anomaly detection for AI
• Attack pattern recognition
• Model drift detection
• Real-time security monitoring

Differential Privacy

Implementing privacy-preserving AI techniques.

• Differential privacy fundamentals
• Privacy budget management
• Noise calibration techniques
• Privacy-utility trade-offs

Federated Learning Security

Securing distributed machine learning systems.

• Federated learning vulnerabilities
• Byzantine-robust aggregation
• Secure aggregation protocols
• Privacy attacks in federated learning

Homomorphic Encryption

Computing on encrypted data in AI systems.

• Homomorphic encryption basics
• Privacy-preserving computations
• Secure multi-party computation
• Encrypted neural networks

Deepfake Detection

Identifying and preventing AI-generated synthetic media.

• Deepfake generation techniques
• Detection algorithm development
• Forensic analysis methods
• Real-time detection systems

Generative AI Security

Securing large language models and generative AI systems.

• Prompt injection attacks
• Jailbreaking techniques
• Model alignment attacks
• Content filtering bypass

Synthetic Data Security

Protecting privacy in synthetic data generation.

• Synthetic data generation methods
• Privacy-preserving synthesis
• Synthetic data quality assessment
• Utility vs. privacy trade-offs

ML Pipeline Security

Securing machine learning development and deployment pipelines.

• CI/CD security for ML
• Model registry security
• Feature store security
• MLOps security best practices

Cloud AI Security

Securing AI workloads in cloud environments.

• Cloud AI service security
• GPU cluster security
• Data lake security for AI
• Edge AI security

AI Hardware Security

Securing specialized AI hardware and accelerators.

• GPU security vulnerabilities
• TPU security assessment
• AI chip side-channel attacks
• Hardware trojans in AI chips

AI Risk Assessment

Evaluating security risks in AI systems and applications.

• AI risk frameworks
• Threat modeling for AI
• Risk quantification methods
• AI security metrics

AI Compliance

Ensuring AI systems meet regulatory and compliance requirements.

• AI regulation compliance
• Algorithmic accountability
• AI audit procedures
• Transparency requirements

AI Security Standards

Implementing security standards and best practices for AI.

• AI security frameworks
• Security by design for AI
• AI security guidelines
• Industry best practices

Adversarial Attack Tools

• Adversarial Robustness Toolbox: Comprehensive attack library
• Foolbox: Python adversarial attacks
• CleverHans: TensorFlow adversarial library
• TextAttack: NLP adversarial attacks

Defense Tools

• Defense-GAN: Generative adversarial defense
• MADRY: Adversarial training framework
• 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