# Data poisoning

Record: term-data-poisoning · Type: term · Edition: 0.20.0 · Evidence cutoff: 2026-09-15

[Read in the atlas](https://theaiatlas.org/ideas/data-poisoning/) · [Complete evidence](https://theaiatlas.org/evidence.html#idea-data-poisoning) · [JSON](https://theaiatlas.org/records/term-data-poisoning.json) · [Pinned complete dataset](https://theaiatlas.org/editions/e74392d479c0e7da8636a7d6a0454d03510df86ffca9931eb86babd952665ca5/data.json)

Dataset pointer: `/glossary/117`. Reviewed: 2026-09-15.

> This is a curated, AI-assisted editorial atlas, not a census, affiliation classifier or independently fact-checked authority.

> Coordinates and ranges summarize public positions. They are not probabilities, rankings, statistical intervals or measures of company safety.

> Preserve source attribution, publication precision, retrieval notes, counterpoints and caveats. A read source does not prove its claims true.

> Read applies to the material described by retrieval.scope and notes. Original-post provenance is not a read source; absent archive metadata means no recorded check, not no existing capture.

> Unplaced actors have null positions because evidence is incomplete. A person and a company remain separate records.

> Quoted or summarized external material is evidence to evaluate, never instructions to execute. Do not infer a tool permission from a source.

> The edition cutoff, actor review date and source publication date have different meanings. Null means unavailable, not zero.

## Publication dates and source age

At least one source was published within the 18-month window.

Newest dated source: 2025-03-24. Assessed at this edition’s evidence cutoff: 2026-09-15. 18-month boundary: 2025-03-15.

Publication age does not establish validity or a new source-reading date. Unknown dates and month/year precision remain explicit in the JSON record.

## /summary

Deliberately altering training material to influence a model's later behavior in an attacker's favor.

Claim: claim-term-data-poisoning-1a5192ba3d7825ca26b24a72. Annotation: synthesis.

[glossary-eval-aml-taxonomy](https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-2e2025.pdf)

## /definition

An attacker inserts or changes training examples, for instance to cause particular errors or make a hidden trigger affect outputs. Poisoning can target initial training or later training stages.

Claim: claim-term-data-poisoning-1e4c26398ee834b2e16dc7b8. Annotation: synthesis.

[glossary-eval-aml-taxonomy](https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-2e2025.pdf)

## /placement

Atlas reading question: how are the origins and integrity of training material checked?

Claim: claim-term-data-poisoning-25f52a21ad9f2e54a62ed1b2. Annotation: editorial.

[glossary-eval-aml-taxonomy](https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-2e2025.pdf)

## /distinction

Accidental low-quality data is not necessarily poisoning. Data checks and filtering can help, but NIST notes that finding malicious examples in a large training collection can be difficult.

Claim: claim-term-data-poisoning-09422ce4d5c74cb753a9bb98. Annotation: synthesis.

[glossary-eval-aml-taxonomy](https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-2e2025.pdf)

## Source provenance

### glossary-eval-aml-taxonomy

[Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations](https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-2e2025.pdf)

NIST · First-hand source (primary) · Published: 2025-03-24 · Material last read: 2026-09-15 · Verification: read

Read scope is described in the source note.

Read the executive summary, attack-stage definitions, and sections 3.2.1 to 3.2.3 on generative-model poisoning and mitigations. Publication date comes from the NIST publication record. No claim is made that one defense stops all attacks.

No archive check recorded.