Data Poisoning

Data poisoning is an attack in which adversaries insert crafted or corrupted data into the training set of a machine learning model. The goal is to manipulate the model’s learned behavior, often in ways that benefit the attacker or degrade system performance.

This manipulation can result in:

• Performance degradation: Models fail to generalize properly or misclassify common inputs.
• Targeted misclassification: Certain inputs are consistently misidentified (e.g., labeling malware as benign).
• Backdoors: Specific triggers cause the model to behave incorrectly but only when activated.
• Bias reinforcement: Manipulating sensitive labels to amplify harmful or unethical outcomes.

Data poisoning is a stealthy and effective threat, especially when:

• Training data is sourced from public or unverified repositories.
• Models are trained using automated pipelines (e.g., continuous learning).
• Data labeling is outsourced or crowdsourced.

Detection is difficult because poisoned data often looks normal. Preventing poisoning requires:

• Dataset validation and versioning.
• Outlier and influence detection during training.
• Robust learning techniques such as differential privacy or adversarial training.

In regulated environments, poisoning can lead to compliance violations or legal liability—particularly when AI decisions affect individuals or critical systems.

How PointGuard AI Addresses This:
PointGuard AI detects poisoning indicators in both training and inference environments to help organizations prevent silent model corruption and ensures training integrity at scale.

Resources:

OWASP LLM04:2025 Data and Model Poisoning

Exposing the AI Security Blind Spot