Data Poisoning
Attack on AI systems by manipulating training data to cause incorrect or harmful model behavior.
Definitions (2)
The intentional introduction or manipulation of training or validation data to cause a model to learn incorrect associations or vulnerabilities, degrading performance or enabling specific adversarial behaviours. The Playbook addresses data poisoning through supplier due diligence, provenance checks, and validation/testing controls.
A type of attack where an adversary injects corrupted or misleading data into an AI model's training dataset, causing the model to learn incorrect patterns or behave in unintended ways, potentially leading to security vulnerabilities or biased outputs.
Related Terms
Training Data
Data used to develop and train AI models to perform their intended functions....
Data Governance
Policies, roles and processes that ensure data used across the AI lifecycle is fit for purpose, traceable, secure and managed in line with legal and operational requirements....
Cybersecurity Requirements
Technical and organisational measures that ensure AI systems resist, detect, respond to, and recover from cyber threats across their lifecycle....