---
language: en
title: "Retrieval poisoning"
description: "Retrieval poisoning introduces content to distort answers when retrieved. Checking provenance and claims helps investigate the source of the deviation."
canonical: "https://tropk.ai/en/resources/glossary/retrieval-poisoning"
last_modified: "2026-10-12T16:09:35.386Z"
---

# Retrieval poisoning

Reviewed on 2026-10-07.

## What it means

[Retrieval](/en/resources/glossary/information-retrieval) poisoning is the deliberate introduction of content into a searchable database or source to influence answers when that material is retrieved. The target is the context provided to the system, not necessarily its training parameters. The PoisonedRAG research demonstrates this possibility under studied conditions, without proving that any published page will alter the answers of all assistants.

## Why it matters for marketing

For marketing, this risk calls for attention to the integrity of sources used in comparisons, descriptions, and customer service. Material incorporated into the database may appear informative and steer a conclusion about a product or brand that is false. An unexpected answer warrants an investigation into provenance, but does not justify accusing a competitor of an attack based solely on a discrepancy, error, or the presence of an external source.

## Practical example

Imagine a distributor finding a warranty term in an internal assistant that does not exist in its catalog. The investigation identifies a recently added document in the database, with no confirmed owner or origin, that supports the same false term. The team removes this document from retrieval and reviews how it was incorporated. It keeps the suspicion of manipulation separate from the intent, which has not yet been proven.

## How to apply or verify

Record the origins and versions of incorporated materials, along with those responsible for approval and review. Compare the answer’s claim with the context actually retrieved and with verifiable references. Isolate suspicious sources in the system under your control and monitor the effect on authorized queries. Preserve evidence of changes without treating a single result as sufficient to determine authorship or intent behind contamination.

## Important distinctions

Poisoning retrieval differs from altering [training data](/en/resources/glossary/training-data) or [model fine-tuning](/en/resources/glossary/fine-tuning). It may involve false facts, instructions, or both, overlapping with [indirect prompt injection](/en/resources/glossary/indirect-prompt-injection) in some cases. Retrieving old content without adversarial intent calls for a different diagnosis. Success in a lab depends on the database, access, and system; it does not guarantee an effect in the public environment or prove an observed attack.

## Sources and references

- [PoisonedRAG: Knowledge Corruption Attacks to Retrieval-Augmented Generation of Large Language Models](<https://arxiv.org/html/2402.07867v3>) — Wei Zou, Runpeng Geng, Binghui Wang e Jinyuan Jia
- [LLM04:2025 Data and Model Poisoning](<https://genai.owasp.org/llmrisk/llm04-data-and-model-poisoning/>) — OWASP GenAI Security Project

## Continue a leitura

- [Recommendation poisoning](</en/resources/glossary/recommendation-poisoning>)
- [Indirect prompt injection](</en/resources/glossary/indirect-prompt-injection>)
- [RAG](</en/resources/glossary/rag>)

## About the author

[Vinicius Pacheco](</en/team/vinicius-pacheco>)

Responsible for Tropk’s technical content, focusing on crawling, indexing, structured data, and agent access to content.

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