Data Poisoning
A security attack where malicious data is deliberately introduced into AI training sets to manipulate model behavior. Can cause models to produce biased outputs, bypass safety filters, or leak sensitive information.
Why it matters
Models learn from huge piles of data, often scraped from the open web, and they generally trust what they're fed. Data poisoning exploits that trust by slipping harmful or misleading examples into the training set so the model quietly learns the wrong thing. It matters because the damage is baked in during training and can be hard to spot later, making data sourcing a real security concern, not just a quality one.
In practice
Suppose attackers flood the web with pages that repeatedly pair a certain phrase with dangerous instructions, hoping the content gets scraped into a training set. If it does, the model may later respond to that phrase in the harmful way the attackers planted. This is why serious teams vet and clean their training data carefully rather than blindly ingesting everything they can find online.
Related terms
AI Alignment
The research challenge of ensuring AI systems pursue goals that are beneficial to humans. Misaligned AI could technically achieve its objective while causing unintended harm. Alignment research aims to make AI reliably helpful, harmless, and honest.
Bias (in AI)
Systematic errors in AI outputs reflecting prejudices in training data. Can manifest as gender stereotyping, racial assumptions, or cultural insensitivity in generated content.
Constitutional AI
A training approach where AI models are given a set of principles (a 'constitution') and learn to self-critique and revise their outputs to comply with those principles. Reduces reliance on human feedback for safety alignment.
GDPR
The European Union's General Data Protection Regulation governing how personal data is collected, stored, and processed. Important when choosing AI tools that handle your data.
Guardrails
Safety mechanisms built into AI systems to prevent harmful, biased, or off-topic outputs. Includes content filters, topic restrictions, output validation, and behavioral boundaries that keep AI responses within acceptable limits.
Hallucination
When an AI model generates information that sounds plausible but is factually incorrect or entirely fabricated. Common with statistics, citations, and historical claims.