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Attackers can poison a retrieval-augmented generation (RAG) system by getting malicious or altered material into its knowledge pipeline, then arranging for that material to be retrieved and passed to the model. The risk is not limited to documents: ingestion connectors, metadata, chunks, embeddings, vector indexes, permissions, model context and connected tools can all affect what the system retrieves or does.
What RAG poisoning means
RAG pairs a generative model with a separate information-retrieval system, such as a knowledge base. For a user query, the system retrieves relevant information and supplies it to the model as context. That lets an organization update the model’s working knowledge by changing its external corpus rather than retraining the model. NIST’s glossary definition of RAG describes this model-plus-retrieval arrangement.
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Poisoning is an integrity problem: someone changes, introduces or misroutes knowledge so the system retrieves material that can distort an answer or influence a downstream action. The material can be malicious, inaccurate, or simply unauthorized. It becomes a model-level risk when it reaches the model’s context.
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Where an attacker can target the pipeline
RAG security depends on the full path from source material to response. OWASP’s RAG security guidance identifies risks across that path, including document poisoning, embedding manipulation and index integrity.
- Documents and sources: An attacker may upload a document containing hidden instructions, compromise an upstream source, or exploit an insider’s ability to edit trusted material.
- Ingestion and extraction: A compromised connector or unsafe import process can introduce content without proper review. Invisible Unicode or zero-width characters may survive extraction and make hostile text harder for reviewers to notice.
- Chunking and metadata: How a document is divided and labeled can change which passages are retrieved and how their source or permissions are interpreted.
- Embeddings and ranking: OWASP describes adversarial text crafted to rank near target queries despite being semantically unrelated. This can make a malicious passage more likely to enter the model’s context.
- Vector index and access controls: Unauthorized index writes, incorrect tenant isolation, or permissions lost when a document is split into chunks can expose or alter retrieved material.
- Model context, outputs and tools: Retrieved content can steer a response, while weakly controlled tools can turn a misleading answer into an external action.
A useful security distinction is between persistent knowledge alteration and context-time influence. The former changes material or retrieval behavior so later requests may be affected; the latter may target one query or one model interaction. Attack access, consequences and recovery options vary by system, so these categories do not imply a universal severity ranking.
What the PoisonedRAG result does—and does not—show
In a study presented at USENIX Security 25 in 2025, authors Wei Zou, Runpeng Geng, Binghui Wang and Jinyuan Jia reported a 90% attack success rate after injecting five malicious texts per target question into a knowledge database containing millions of texts. The authors also reported that the defenses they evaluated were insufficient. These are findings under the study’s conditions, not a measure of how often real-world RAG systems are poisoned or a guaranteed success rate against any particular deployment. Read the PoisonedRAG paper summary from USENIX.
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The reviewed sources do not establish a representative prevalence rate for real-world RAG poisoning incidents. The experimental result is useful as evidence that a small number of carefully constructed texts can matter in a large corpus under tested conditions, but it should not be read as an industry-wide incident statistic.
How to secure a RAG knowledge base
OWASP’s core advice is to protect each stage of the data path, rather than treating the model prompt as the only security boundary. Its guidance includes provenance and integrity checks, per-chunk permissions, bounded context, output validation, monitoring and fail-closed behavior.
1. Control what enters the corpus
- Maintain an allowlist of trusted sources and vet ingestion connectors before connecting them.
- Stage new sources for review or approval; scan extracted content and record who submitted it, where it came from, when it arrived and who approved it.
- Check provenance and integrity against a baseline protected separately from the ingestion system. A matching hash or digest proves consistency with that baseline, not that the baseline content is safe; review and approve baseline changes.
- Preserve source identifiers and document versions so questionable content can be traced and removed.
2. Protect retrieval and authorization
- Carry each document’s permissions onto every chunk created from it, and enforce those permissions at query time—not only when a document is first ingested.
- Isolate tenants and data classifications. Test that one user or tenant cannot retrieve another’s material through search, caches or shared indexes.
- Restrict index write access and monitor changes to index contents, metadata and retrieval configuration.
3. Treat retrieved text as untrusted context
Retrieved passages are data, not authority. Delimit them clearly in the prompt and instruct the model not to treat their contents as higher-priority instructions. Limit the number and total size of retrieved chunks, and test the prompt arrangement with the model versions you deploy.
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OWASP offers 3–5 chunks totaling 2,000–4,000 tokens as a reasonable starting default for limiting context-window flooding. This is practitioner guidance, not a universal or independently validated optimum; tune it to the task, corpus and model, then test for both retrieval quality and attack exposure.
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4. Keep outputs and actions behind independent controls
- Validate model outputs against application rules outside the model; do not rely on the model to enforce its own policy.
- Authorize every tool call independently of what a retrieved passage or model response requests.
- Use stronger approval or verification for consequential actions, such as changing records, disclosing protected data or executing transactions.
- Do not let a claimed source or citation substitute for validating that the source was authorized and actually supports the answer.
5. Observe the path and prepare to recover
For each request, record enough structured information to reconstruct what happened: request ID, retrieved document IDs, authorization decisions, model version and tool outcomes. OWASP advises against logging raw queries and model content by default because they may contain secrets or personal information; use a deliberate privacy and retention policy if such content must be captured for a specific purpose.
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Test the pipeline with red-team scenarios that cover poisoned retrieval, indirect injection, cross-tenant leakage, stale permissions, cache leakage, unauthorized tool calls, citation or attribution tampering, and deletion. Have a response plan to quarantine suspect content, rebuild or repair affected index entries, invalidate caches that may hold tainted results, and identify users who received affected responses.
6. Fail closed when trust checks fail
If retrieval, access checks, source attribution or document-integrity checks fail, do not silently fall back to an unrestricted answer from model memory or serve an unsafe cached result. Return a safe failure or ask the user to retry after the system can verify its inputs.
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There is no single poisoning scenario. When assessing a RAG system, ask what an attacker can change, when the change takes effect, what access it requires, and what the system can do as a result. A modified upstream document may persist across many requests; a malicious user query may affect only one interaction. A retrieval flaw may expose confidential information, while an injected instruction may steer a tool-enabled agent. Those risks call for different controls, and the sources cited here do not establish a universal ranking of their severity.
The practical security boundary is the whole pipeline: trusted sources and ingestion, permission-aware retrieval, constrained model context, independently authorized actions, and observable recovery. As OWASP puts it, “RAG does not reduce risk — it redistributes it across the data pipeline, creating new attack surfaces at every stage from ingestion to generation to output.”
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