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

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": ""
   }
   

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