Details

Subject(s)

• Artificial intelligence [Browse]

Summary note

Understand how adversarial attacks work against predictive and generative AI, and learn how to safeguard AI and LLM projects with practical examples leveraging OWASP, MITRE, and NIST Key Features Understand the connection between AI and security by learning about adversarial AI attacks Discover the latest security challenges in adversarial AI by examining GenAI, deepfakes, and LLMs Implement secure-by-design methods and threat modeling, using standards and MLSecOps to safeguard AI systems Purchase of the print or Kindle book includes a free PDF eBook Book Description Adversarial attacks trick AI systems with malicious data, creating new security risks by exploiting how AI learns. This challenges cybersecurity as it forces us to defend against a whole new kind of threat. This book demystifies adversarial attacks and equips cybersecurity professionals with the skills to secure AI technologies, moving beyond research hype or business-as-usual strategies. The strategy-based book is a comprehensive guide to AI security, presenting a structured approach with practical examples to identify and counter adversarial attacks. This book goes beyond a random selection of threats and consolidates recent research and industry standards, incorporating taxonomies from MITRE, NIST, and OWASP. Next, a dedicated section introduces a secure-by-design AI strategy with threat modeling to demonstrate risk-based defenses and strategies, focusing on integrating MLSecOps and LLMOps into security systems. To gain deeper insights, you'll cover examples of incorporating CI, MLOps, and security controls, including open-access LLMs and ML SBOMs. Based on the classic NIST pillars, the book provides a blueprint for maturing enterprise AI security, discussing the role of AI security in safety and ethics as part of Trustworthy AI. By the end of this book, you'll be able to develop, deploy, and secure AI systems effectively. What you will learn Understand poisoning, evasion, and privacy attacks and how to mitigate them Discover how GANs can be used for attacks and deepfakes Explore how LLMs change security, prompt injections, and data exposure Master techniques to poison LLMs with RAG, embeddings, and fine-tuning Explore supply-chain threats and the challenges of open-access LLMs Implement MLSecOps with CIs, MLOps, and SBOMs Who this book is for This book tackles AI security from both angles - offense and defense. AI builders (developers and engineers) will learn how to create secure systems, while cybersecurity professionals, such as security architects, analysts, engineers, ethical hackers, penetration testers, and incident responders will discover methods to combat threats and mitigate risks posed by attackers. The book also provides a secure-by-design approach for leaders to build AI with security in mind. To get the most out of this book, you'll need a basic understanding of security, ML concepts, and Python.

Source of description

• Description based on publisher supplied metadata and other sources.
• Description based on print version record.

Contents

• Cover
• Title Page
• Copyright
• Dedication
• Contributors
• Table of Contents
• Preface
• Part 1: Introduction to Adversarial AI
• Chapter 1: Getting Started with AI
• 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
• Further reading
• Chapter 2: Building Our Adversarial Playground
• Technical requirements
• Setting up your development environment
• Python installation
• Creating your virtual environment
• Installing packages
• Registering your virtual environment with Jupyter notebooks
• Verifying your installation
• Hands-on basic baseline ML
• Simple NNs
• Developing our target AI service with CNNs
• Setup and data collection
• Data exploration
• Data preprocessing
• Algorithm selection and building the model
• Model training
• Model evaluation
• Model deployment
• Inference service
• ML development at scale
• Google Colab
• AWS SageMaker
• Azure Machine Learning services
• Lambda Labs Cloud
• Chapter 3: Security and Adversarial AI
• Security fundamentals
• Threat modeling
• Risks and mitigations
• DevSecOps
• Securing our adversarial playground
• Host security
• Network protection
• Authentication
• Data protection
• Access control
• Securing code and artifacts
• Secure code
• Securing dependencies with vulnerability scanning
• Secret scanning
• Securing Jupyter Notebooks
• Securing models from malicious code
• Integrating with DevSecOps and MLOps pipelines
• Bypassing security with adversarial AI
• Our first adversarial AI attack
• Traditional cybersecurity and adversarial AI
• Adversarial AI landscape
• Part 2: Model Development Attacks
• Chapter 4: Poisoning Attacks
• Basics of poisoning attacks
• Definition and examples.
• Types of poisoning attacks
• Poisoning attack examples
• Why it matters
• Staging a simple poisoning attack
• Creating poisoned samples
• Backdoor poisoning attacks
• Creating backdoor triggers with ART
• Poisoning data with ART
• Hidden-trigger backdoor attacks
• Clean-label attacks
• Advanced poisoning attacks
• Mitigations and defenses
• Cybercity defenses with MLOps
• Anomaly detection
• Robustness tests against poisoning
• Advanced poisoning defenses with ART
• Adversarial training
• Creating a defense strategy
• Chapter 5: Model Tampering with Trojan Horses and Model Reprogramming
• Injecting backdoors using pickle serialization
• Attack scenario
• Defenses and mitigations
• Injecting Trojan horses with Keras Lambda layers
• Trojan horses with custom layers
• Neural payload injection
• Attacking edge AI
• Model hijacking
• Trojan horse code injection
• Model reprogramming
• Chapter 6: Supply Chain Attacks and Adversarial AI
• Traditional supply chain risks and AI
• Risks from outdated and vulnerable components
• Risks from AI's dependency on live data
• Securing AI from vulnerable components
• Enhanced security - allow approved-only packages
• Client configuration for private PyPI repositories
• Additional private PyPI security
• Use of SBOMs
• AI supply chain risks
• The double-edged sword of transfer learning
• Model poisoning
• Model tampering
• Secure model provenance and governance for pre-trained models
• MLOps and private model repositories
• Data poisoning
• Using data poisoning to affect sentiment analysis
• AI/ML SBOMs
• Summary.
• Part 3: Attacks on Deployed AI
• Chapter 7: Evasion Attacks against Deployed AI
• Fundamentals of evasion attacks
• Importance of understanding evasion attacks
• Reconnaissance techniques for evasion attacks
• Perturbations and image evasion attack techniques
• Evasion attack scenarios
• One-step perturbation with FGSM
• Basic Iterative Method (BIM)
• Jacobian-based Saliency Map Attack (JSMA)
• Carlini and Wagner (C&
• W) attack
• Projected Gradient Descent (PGD)
• Adversarial patches - bridging digital and physical evasion techniques
• NLP evasion attacks with BERT using TextAttack
• Attack scenario - sentiment analysis
• Attack example
• Attack scenario - natural language inference
• Universal Adversarial Perturbations (UAPs)
• Black-box attacks with transferability
• Defending against evasion attacks
• Mitigation strategies overview
• Input preprocessing
• Model hardening techniques
• Model ensembles
• Certified defenses
• Chapter 8: Privacy Attacks - Stealing Models
• Understanding privacy attacks
• Stealing models with model extraction attacks
• Functionally equivalent extraction
• Learning-based model extraction attacks
• Generative student-teacher extraction (distillation) attacks
• Attack example against our CIFAR-10 CNN
• Prevention measures
• Detection measures
• Model ownership identification and recovery
• Chapter 9: Privacy Attacks - Stealing Data
• Understanding model inversion attacks
• Types of model inversion attacks
• Exploitation of model confidence scores
• GAN-assisted model inversion
• Example model inversion attack
• Understanding inference attacks
• Attribute inference attacks
• Meta-classifiers.
• Poisoning-assisted inference
• Attack scenarios
• Mitigations
• Example attribute inference attack
• Membership inference attacks
• Statistical thresholds for ML leaks
• Label-only data transferring attack
• Blind membership inference attacks
• White box attacks
• Example membership inference attack using the ART
• Chapter 10: Privacy-Preserving AI
• Privacy-preserving ML and AI
• Simple data anonymization
• Advanced anonymization
• K-anonymity
• Anonymization and geolocation data
• Anonymizing rich media
• Differential privacy (DP)
• Federated learning (FL)
• Split learning
• Advanced encryption options for privacy-preserving ML
• Secure multi-party computation (secure MPC)
• Homomorphic encryption
• Advanced ML encryption techniques in practice
• Applying privacy-preserving ML techniques
• Part 4: Generative AI and Adversarial Attacks
• Chapter 11: Generative AI - A New Frontier
• A brief introduction to generative AI
• A brief history of the evolution of generative AI
• Generative AI technologies
• Using GANs
• Developing a GAN from scratch
• WGANs and custom loss functions
• Using pre-trained GANs
• Pix2Pix
• CycleGAN
• Pix2PixHD
• Progressive Growing of GANs (PGGAN)
• BigGAN
• StarGAN v2
• StyleGAN series
• Chapter 12: Weaponizing GANs for Deepfakes and Adversarial Attacks
• Use of GANs for deepfakes and deepfake detection
• Using StyleGAN to generate convincing fake images
• Creating simple deepfakes with GANs using existing images
• Making direct changes to an existing image
• Using Pix2PixHD to synthesize images
• Fake videos and animations
• Other AI deepfake technologies
• Voice deepfakes
• Deepfake detection
• Using GANs in cyberattacks and offensive security
• Evading face verification.
• Compromising biometric authentication
• Password cracking with GANs
• Malware detection evasion
• GANs in cryptography and stenography
• Generating web attack payloads with GANs
• Generating adversarial attack payloads
• Securing GANs
• GAN-assisted adversarial attacks
• Deepfakes, malicious content, and misinformation
• Chapter 13: LLM Foundations for Adversarial AI
• 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
• Chapter 14: Adversarial Attacks with Prompts
• Adversarial inputs and prompt injection
• Direct prompt injection
• Prompt override
• Style injection
• Role-playing
• Impersonation
• Other jailbreaking techniques
• 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
• LLM platform defenses
• Application-level defenses
• Chapter 15: Poisoning Attacks and LLMs
• Poisoning embeddings in RAG
• Poisoning during embedding generation
• Direct embeddings poisoning
• Advanced embeddings poisoning
• Query embeddings manipulation
• Poisoning attacks on fine-tuning LLMs
• Introduction to fine-tuning LLMs
• Fine-tuning poisoning attack scenarios
• Fine-tuning attack vectors
• Poisoning ChatGPT 3.5 with fine-tuning
• Defenses and mitigations against poisoning attacks in fine-tuning
• Chapter 16: Advanced Generative AI Scenarios
• Supply-chain attacks in LLMs
• Publishing a poisoned LLM on Hugging Face
• Publishing a tampered LLM on Hugging Face.
• Other supply-chain risks for LLMs.

Show 258 more Contents items

ISBN

9781835088678 ((electronic bk.))

OCLC

1446416572

Statement on language in description

Princeton University Library aims to describe library materials in a manner that is respectful to the individuals and communities who create, use, and are represented in the collections we manage. Read more...