Data Poisoning Overview — Silent Investor Threats Analysis

# **Data Poisoning: The Silent Threat to AI-Driven Systems – 2026 Intelligence Report** ## **Executive Summary** Data poisoning represents one of the most insidious and underappreciated threats to modern AI systems. Unlike traditional cyberattacks that rely on direct exploitation, data poisoning undermines machine learning (ML) models by subtly corrupting the data used for training, fine-tuning, or retrieval augmentation. Recent intelligence reveals that adversaries are increasingly weaponizing this technique, with botnets like **RondoDox** exploiting vulnerabilities in enterprise infrastructure (e.g., **HPE OneView CVE-2023-28131**) to inject malicious data into AI pipelines. This report examines the growing sophistication of data poisoning attacks, their real-world implications, and defensive strategies to mitigate this silent but devastating threat. --- ## **1. Data Poisoning: Definition and Evolution** Data poisoning occurs when an attacker manipulates training data to degrade model performance, introduce bias, or trigger malicious behavior. Unlike adversarial examples (which target inference-time inputs), data poisoning corrupts the foundational dataset, leading to long-term systemic compromise. ### **Key Phases of Data Poisoning** 1. **Training Data Poisoning** – Adversaries inject malicious samples into datasets, causing models to learn incorrect patterns. 2. **Fine-Tuning Poisoning** – Attackers manipulate fine-tuning datasets, skewing model behavior in production. 3. **Retrieval-Augmented Poisoning** – Malicious data is inserted