Malicious Code Poisoning in Digital Systems — Digital Life Threats Analysis

# **Malicious Code Poisoning in Digital Systems: A Threat Analysis of AI Manipulation and OAuth Exploits** ## **Executive Summary** Digital systems increasingly rely on artificial intelligence (AI) and machine learning (ML) models for decision-making, authentication, and behavioral analysis. However, these systems are vulnerable to **malicious code poisoning**—a sophisticated attack vector where adversaries manipulate training data, model inputs, or authentication flows to subvert system integrity. This report examines three critical threats: 1. **Malicious Code Poisoning in AI/ML Systems** 2. **OAuth Redirect Exploits for Unauthorized Access** 3. **Keystroke & Mouse Dynamics-Based User Identification Bypass** These attacks undermine trust in AI-driven systems, enable account takeovers, and facilitate data exfiltration. This analysis provides actionable intelligence on detection, mitigation, and defensive strategies. --- ## **1. Malicious Code Poisoning in AI/ML Systems** ### **Technical Overview** Malicious code poisoning (also known as **data poisoning** or **model poisoning**) occurs when an attacker injects tainted data into a training dataset, altering the behavior of an AI/ML model. Unlike traditional malware, this attack targets the **foundation of AI systems**—their training data—leading to compromised predictions, misclassifications, or adversarial behavior. #### **Attack Vectors** 1. **Data Poisoning** - Adversaries inject malicious samples into training datasets to skew model outputs (e.g., misclassifying malware as