Combating Cyberattacks Targeting the AI Ecosystem : Assessing Threats, Risks, and Vulnerabilities.

Format:

Book

Author/Creator:

Sood, Aditya K.

Language:

English

Subjects (All):

Computer security.

Artificial intelligence.

Physical Description:

1 online resource (253 pages)

Edition:

1st ed.

Place of Publication:

Bloomfield : Mercury Learning & Information, 2024.

Summary:

This book provides an in-depth analysis of combating cyberattacks targeting the AI ecosystem. It explores the challenges and risks associated with artificial intelligence, focusing on the security aspects of large language models (LLMs), generative AI applications, and AI systems. The author discusses trust, compliance, and security frameworks to ensure the responsible use of AI technologies. Real-world case studies illustrate threats and attacks, highlighting the need for robust security measures. The book is intended for cybersecurity professionals, AI researchers, and industry practitioners seeking to understand and mitigate AI-related cyber threats. Generated by AI.

Contents:

Cover

Halftitle

Title

Copyright

Dedication

Contents

Preface

Acknowledgments

About the Author

Chapter 1: Introduction to AI: LLMs, GenAI Applications, and the AI Infrastructure

What is Artificial Intelligence?

History of Artificial Intelligence in Industry

Challenges in Artificial Intelligence

AI Taxonomy

Building Blocks of the AI System

AI Learning Methods

Collaborative AI Systems and Learning

AI Infrastructure and Components

Understanding Generative AI Taxonomy

Overview of Large Language Models (LLMs)

Components of LLMs

Classifying LLMs

LLM Code Examples for Learning

Case 1: Using BERT for Sentiment Analysis

Case 2: Using GPT for Text Generation

Case 3: RankGAN for Text Generation

Generative AI Applications and Design

GenAI/LLM Application Workflow

Generative AI Service Architecture

Conclusion

References

Chapter 2: AI Trust, Compliance, and Security

Trusted and Responsible AI

Ethical Frameworks

Societal Impact Assessment

Diverse Inclusive Development

Accountability Mechanisms

Fairness and Bias Mitigation

Transparent and Explainability

Data Protection

Continuous Learning and Improvement

User Centric Design

Embedding Privacy in AI Systems

Compliance in AI Systems

A Perspective into Securing the AI Ecosystem

LLM Security

GenAI Applications Security

AI Infrastructure Security

AI Guardrails

Trust, Compliance, and Security Frameworks

Chapter 3: The AI Threat Landscape: Dissecting the Risks and Attack Vectors

AI Threat Landscape: Dissecting the Main Challenges

Automated Malicious Code Generation

Autonomous Cyber Weapons

Adversarial Attacks Against AI/ML Systems

Prompt Injection Attacks

Jailbreaking Guardrail Routines

AI-generated Deepfake Attacks.

AI-powered Defense Evasion

AI-powered Social Engineering

AI-driven Targeted Attacks

AI-enabled Offensive Cyber Operations

Threats and Attacks: Practical Examples

AI Threat and Risk Frameworks

AI Infrastructure Attacks

Chapter 4: Threats and Attacks Targeting the AI Ecosystem: Real-World Case Studies

Harnessing the Power of AI Systems

AI-generated Customized CEO Spoof Email

AI-powered Malicious Code Generation

Generating Reverse Shell Code

Generating Domain Generation Algorithm Code

Generating DNS Tunneling Code Using the Amazon Retail Application

Security Issues in the AI Ecosystem: Real-World Case Studies

Exposed Jupyter Notebooks Web Interface

Exposed Docker Repositories Containing AI Model Packages

Security Flaws in Customized Gradio AI/ML Model Deployment Applications

Unsecured AI/ML Model Operations' Web Interfaces

Unsecured LLM Low Code Builder Software Interface

Unauthorized API Requests to AI Bot Node

Unsecured and Exposed AIOps Cloud Components

Leaked Datasets Used for AI Models

Access to Config Files via Unauthenticated APIs

Advanced LLMs: Guardrails Implementation

Ethical Guidelines Comparison for Different LLMs

Ethical and Private AI: Stateless Interactions

Examples of Real-world Attacks Targeting the AI Ecosystem

Chapter 5: Security Assessment of LLMs, GenAI Applications, and the AI Infrastructure

Threat Modeling of the AI Ecosystem

Penetration Testing of the AI Ecosystem

Prompt Injection: Testing Strategies

Dissecting Prompt Principles for Security Assessment

Jailbreaking Guardrails

Prompt Splitting

Ignoring Context and Response

Prompt Typosquatting

Prompt Error Interpretation to Execute Commands

Information Gathering from AI Chatbots.

Security Assessment of the AI Ecosystem

Directory Listing of AI Package Files

Assessing the Security of Vector Database API Endpoints

Data Pipelines: Unrestricted Access to API Endpoints

A Distributed Messaging Platform for GenAI Applications

Improper Error Handling Resulting in Unavailability and DoS

Insecure Handling of Prompt Responses

Assessing the Security of Inference Server Web and API Routes

Evaluating the Security of Federated Learning Framework

Assessing the Security of a Remote LLM Server Running RDP

Security Assessment of AI Models Hosted on a Code Repository Platform

Scanning for Malicious Code

Scanning for Unauthorized Code in Pickle Files

Scanning for Leaked Secrets

Secure Review: Practical Code Analysis

Model Access API Key Stored in the Environment Variable

Code Routine to Prevent Leakage of Sensitive Data via LLM Application

Assessing Security Tool Integration into CI/CD Pipelines

Reviewing API Rate Limiting and Throttling Configuration

Reviewing Security Rules for LLM Servers

AI Ecosystem Security Assessment Checklist

Chapter 6: Defending LLMs, GenAI Applications, and the AI Infrastructure Against Cyberattacks

Securing LLMs

Defending Against Adversarial Attacks on AI Systems

Securing Generative AI Applications

Securing AI Infrastructure

Secure Development Using AI Guardrails

AI Security Awareness and Training

Appendix: Machine Learning /AI Terms

Index.

Notes:

Description based on publisher supplied metadata and other sources.

Part of the metadata in this record was created by AI, based on the text of the resource.

ISBN:

9781501520549

1501520547

OCLC:

1463061530