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AI agent vulnerability · Open WebUI

CVE-2026-44554: Open WebUI has Knowledge Base Destruction and RAG Poisoning via Unauthorized Collection Overwrite

highCVSS 3.1 8.1GHSA-7r82-qhg4-6wvj

Knowledge Base Destruction and RAG Poisoning via Unauthorized Collection Overwrite Affected Component Retrieval web/YouTube processing endpoints: - backend/openwebui/routers/retrieval.py (lines 1810-1837, processweb) - backend/openwebui/routers/retrieval.py (the parallel processyoutube endpoint) - backend/openwebui/routers/retrieval.py (line 1445, savedocstovectordb call chain) Affected Versions Current main branch…

Published May 8, 2026 · Updated May 15, 2026

Affected packages

open-webui

Ecosystem
PyPI
Affected
<= 0.8.12
Fixed in
0.9.0
Check
pip show open-webui

Advisory details

From the GitHub Advisory Database (CC BY 4.0).

Knowledge Base Destruction and RAG Poisoning via Unauthorized Collection Overwrite

Affected Component

Retrieval web/YouTube processing endpoints:

  • backend/open_webui/routers/retrieval.py (lines 1810-1837, process_web)
  • backend/open_webui/routers/retrieval.py (the parallel process_youtube endpoint)
  • backend/open_webui/routers/retrieval.py (line 1445, save_docs_to_vector_db call chain)

Affected Versions

Current main branch (commit 6fdd19bf1) and likely all versions with RAG/knowledge base functionality.

Description

The POST /api/v1/retrieval/process/web endpoint accepts a user-supplied collection_name and an overwrite query parameter (default: True). It performs no authorization check on whether the calling user owns or has write access to the target collection. When overwrite=True, save_docs_to_vector_db calls VECTOR_DB_CLIENT.delete_collection() on the target collection before writing new content.

Combined with the knowledge base enumeration vulnerability (separate report), an attacker can trivially discover any user's knowledge base UUID and then destroy or poison it.

# retrieval.py:1810-1837 — no collection authorization check
@router.post('/process/web')
async def process_web(
    request: Request,
    form_data: ProcessUrlForm,
    user=Depends(get_verified_user),
    ...
):
    # ... fetch and process the URL ...
    save_docs_to_vector_db(
        request=request,
        docs=docs,
        collection_name=form_data.collection_name,  # attacker-controlled, unchecked
        overwrite=overwrite,                        # defaults to True
        ...
    )

CVSS 3.1 Breakdown

| Metric | Value | Rationale |
| Attack Vector | Network (N) | Exploited remotely via API call |
| Attack Complexity | Low (L) | Single API call with a known KB UUID |
| Privileges Required | Low (L) | Requires any authenticated user account |
| User Interaction | None (N) | No victim interaction required |
| Scope | Unchanged (U) | Impact within the knowledge base authorization boundary |
| Confidentiality | None (N) | No data disclosure from this vulnerability directly |
| Integrity | High (H) | Complete replacement of victim's KB content with attacker-controlled data |
| Availability | High (H) | Victim's original KB embeddings are deleted; KB effectively destroyed |

Attack Scenario

  1. Attacker discovers victim's KB UUID via the knowledge-bases meta-collection (separate finding) or other enumeration.
  2. Attacker sends: `` POST /api/v1/retrieval/process/web?overwrite=true { "url": "https://attacker.com/poison", "collection_name": "<victim_kb_uuid>" } ``
  3. The endpoint fetches content from the attacker's URL.
  4. save_docs_to_vector_db deletes the entire vector collection belonging to the victim's knowledge base.
  5. The attacker's fetched content is embedded and written as the new collection content.
  6. Victim's RAG queries against their KB now return attacker-controlled content instead of their original documents.

Impact

  • Data destruction: Victim's original KB embeddings are permanently deleted from the vector store
  • RAG poisoning: Attacker-controlled content replaces legitimate knowledge, causing the LLM to return misleading or malicious answers to the victim
  • Indirect prompt injection: Poisoned content can contain crafted prompts that manipulate the victim's LLM behavior when queried
  • Persistence: The poisoned content persists until the KB is rebuilt from source files

Preconditions

  • Attacker must have a valid user account
  • Attacker must know the target collection name (KB UUID) — easily obtained via the knowledge-bases enumeration finding

Credited to Classic298.

References

How this page is made: the data panel comes from the GitHub Advisory Database, CISA’s KEV catalog, the NVD and FIRST EPSS, and updates twice a day. No analysis has been written for this entry yet. Methodology

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