GHSA-65pg-qhhw-mxwg Open WebUI Vulnerable to Unauthenticated RAG Configuration Disclosure

• PyPI open-webui: < 0.9.5 → fixed in 0.9.5

Vulnerability Type: Information Disclosure / Missing Authentication Severity: Medium Component: `backend/open_webui/routers/retrieval.py` — `get_status()` (`GET /`) Affected Endpoint: `GET /api/v1/retrieval/` Affected Version: Open WebUI `main` branch — confirmed unpatched through v0.9.2 Authentication Required: None — internet-facing with zero credentials CVSSv3.1 Score: 5.3 (AV:N/AC:L/PR:N/UI:N/S:U/C:L/I:N/A:N) --- Summary `GET /api/v1/retrieval/` returns live RAG pipeline configuration to any unauthenticated HTTP client. No `Authorization` header, cookie, or API key is required. Every adjacent endpoint on the same router (`/embedding`, `/config`) is correctly guarded by `get_admin_user` making this a targeted omission. --- Root Cause `backend/open_webui/routers/retrieval.py:262` ```python @router.get('/') async def get_status(request: Request): # ← no Depends(get_verified_user) return { 'status': True, 'CHUNK_SIZE': request.app.state.config.CHUNK_SIZE, 'CHUNK_OVERLAP': request.app.state.config.CHUNK_OVERLAP, 'RAG_TEMPLATE': request.app.state.config.RAG_TEMPLATE, 'RAG_EMBEDDING_ENGINE': request.app.state.config.RAG_EMBEDDING_ENGINE, 'RAG_EMBEDDING_MODEL': request.app.state.config.RAG_EMBEDDING_MODEL, 'RAG_RERANKING_MODEL': request.app.state.config.RAG_RERANKING_MODEL, 'RAG_EMBEDDING_BATCH_SIZE': request.app.state.config.RAG_EMBEDDING_BATCH_SIZE, 'ENABLE_ASYNC_EMBEDDING': request.app.state.config.ENABLE_ASYNC_EMBEDDING, 'RAG_EMBEDDING_CONCURRENT_REQUESTS': request.app.state.config.RAG_EMBEDDING_CONCURRENT_REQUESTS, } ``` Compare with every adjacent endpoint on the same router: ```python @router.get('/embedding') async def get_embedding_config(request: Request, user=Depends(get_admin_user)): # ✅ @router.get('/config') async def get_rag_config(request: Request, user=Depends(get_admin_user)): # ✅ ``` --- Proof Of Concept — No Token Required ```bash curl -s http://TARGET/api/v1/retrieval/ ``` ```json { "status": true, "CHUNK_SIZE": 1000, "CHUNK_OVERLAP": 100, "RAG_TEMPLATE": "### Task:\nRespond to the user query using the provided context...\n\n{{CONTEXT}}\n", "RAG_EMBEDDING_ENGINE": "", "RAG_EMBEDDING_MODEL": "sentence-transformers/all-MiniLM-L6-v2", "RAG_RERANKING_MODEL": "", "RAG_EMBEDDING_BATCH_SIZE": 1, "ENABLE_ASYNC_EMBEDDING": true, "RAG_EMBEDDING_CONCURRENT_REQUESTS": 0 } ``` --- Disclosed Information and Its Value to an Attacker | Field | What it reveals | |---|---| | `RAG_EMBEDDING_ENGINE` | Backend type (OpenAI, Ollama, Azure, etc.) | | `RAG_EMBEDDING_MODEL` | Exact model name — reveals embedding model | | `RAG_RERANKING_MODEL` | Reranker in use — reveals reranker | | `RAG_TEMPLATE` | RAG template — exposes the RAG template | | `CHUNK_SIZE` / `CHUNK_OVERLAP` | Chunking parameters — enables exact reconstruction of how documents are split and retrieved | --- Attack Scenario 1. Attacker sends one unauthenticated HTTP GET to `/api/v1/retrieval/`. 2. Response reveals the embedding model and chunking parameters. 3. Attacker uses the exact chunk size/overlap to craft RAG poisoning payloads that are guaranteed to be retrieved. --- Impact 1. RAG template disclosure 2. Infrastructure fingerprinting — embedding engine and model name reveal the AI stack to an internet scanner 3. RAG attack surface mapping — chunk parameters enable precise calculation of retrieval boundaries 4. Zero-effort recon — no brute force, no credentials, no rate-limit concern. Single request from any IP. --- Recommended Fix Add `get_verified_user` dependency (or `get_admin_user` for stricter control): ```python BEFORE (vulnerable) @router.get('/') async def get_status(request: Request): AFTER @router.get('/') async def get_status(request: Request, user=Depends(get_verified_user)): ```