PT-2026-59371 · Pypi · Open-Webui
Published
2026-07-13
·
Updated
2026-07-13
CVSS v3.1
6.5
Medium
| Vector | AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:N/A:N |
Unauthorized File and Knowledge Base Content Access via RAG Vector Search
Affected Component
RAG source resolution in chat completion pipeline:
backend/open webui/retrieval/utils.py(lines 963-965, 1063-1068, 1126-1131 inget sources from items)
Affected Versions
Current main branch (commit
6fdd19bf1) and likely all versions with RAG functionality.Description
The
get sources from items function resolves file and knowledge base references into vector search queries during chat completion. Three of the five code paths perform vector store queries without any authorization check, allowing users to extract content from files and knowledge bases they do not have access to.| Path | Lines | Access Check |
|---|---|---|
type: "file", full-context | 1044-1050 | ✅ has access to file |
type: "file", non-full-context (default) | 1063-1068 | ❌ None |
type: "collection" | 1070-1118 | ✅ Present |
type: "text" with collection name | 963-965 | ❌ None |
Bare collection name/collection names | 1126-1131 | ❌ None |
The three unprotected paths pass user-supplied collection names directly to
query collection(), which queries the vector store without any authorization. Collection names follow predictable formats: file-<file id> for files and the knowledge base UUID for knowledge bases.CVSS 3.1 Breakdown
| Metric | Value | Rationale |
|---|---|---|
| Attack Vector | Network (N) | Exploited remotely via chat completion API |
| Attack Complexity | Low (L) | Single API call with a known resource ID |
| Privileges Required | Low (L) | Requires a valid user account |
| User Interaction | None (N) | No victim interaction required |
| Scope | Unchanged (U) | Impact within the application's data boundary |
| Confidentiality | High (H) | Full content of private files/knowledge bases extractable |
| Integrity | None (N) | No data modification |
| Availability | None (N) | No denial of service |
Attack Scenario
- User A uploads a private document and uses it in RAG (the document is embedded into the vector store as collection
file-<file id>). - User A shares a chat or model referencing the file with User B, or User B otherwise obtains the file ID through a legitimate interaction.
- User A later revokes User B's access to the file.
- User B sends a chat completion request referencing the revoked file:
json
POST /api/chat/completions
{
"model": "any-accessible-model",
"messages": [{"role": "user", "content": "What does this document say about pricing?"}],
"files": [{"type": "file", "id": "<revoked file id>"}]
}- The non-full-context path (default) constructs collection name
file-<id>and queries the vector store with no access check. - Matching chunks are injected into the LLM context, and the response contains the victim's private file content.
The same attack works via
{"type": "text", "collection name": "<knowledge base id>"} for knowledge bases.Impact
- Access revocation is ineffective for RAG content — users who previously had access can continue extracting file and knowledge base content indefinitely
- Private document content can be systematically extracted through targeted queries
- Breaks the access control model for files and knowledge bases at the RAG layer
Preconditions
- Attacker must know the file ID or knowledge base ID (UUID) of the target resource
- The target file/knowledge base must have been processed into the vector store
- Attacker must have a valid user account
Fix
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Related Identifiers
Affected Products
Open-Webui