Data Poisoning
A form of adversarial attack where malicious actors deliberately corrupt AI training data to manipulate model behavior, causing it to produce incorrect or harmful outputs.
Data poisoning attacks can compromise AI systems by injecting as little as 0.1% corrupted data into training sets. Researchers have demonstrated attacks where poisoned data causes image classifiers to misidentify objects and language models to produce biased outputs. As AI training increasingly relies on internet-scraped data, the attack surface expands. Nightshade, a tool released by University of Chicago researchers, allows artists to poison images so they disrupt AI training. Defense measures include data validation, anomaly detection, and training data provenance tracking, but no comprehensive solution exists.
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Related Terms
Artificial General Intelligence (AGI)
A hypothetical form of AI that can understand, learn, and apply knowledge across any intellectual task at or above human level, rather than being specialized for specific tasks.
AI Alignment
The research field focused on ensuring AI systems behave in accordance with human values and intentions, particularly as systems become more capable.
AI Safety
The interdisciplinary field focused on preventing AI systems from causing harm, encompassing alignment, robustness, interpretability, and governance of AI technologies.
Deepfake
AI-generated synthetic media — images, video, or audio — that realistically depict events or statements that never occurred, created using deep learning techniques.
Foundation Model
A large AI model trained on broad data that can be adapted to a wide range of downstream tasks — examples include GPT-4, Claude, Gemini, and Llama.
Hallucination
When an AI model generates plausible-sounding but factually incorrect or fabricated information, presenting it with the same confidence as accurate responses.
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