CVE-2026-33682: Streamlit: SSRF leaks NTLMv2 creds via UNC path

Streamlit is a data oriented application development framework for python. Streamlit Open Source versions prior to 1.54.0 running on Windows hosts have an unauthenticated Server-Side Request Forgery (SSRF) vulnerability. The vulnerability arises from improper validation of attacker-supplied filesystem paths. In certain code paths, including within the `ComponentRequestHandler`, filesystem paths are resolved using `os.path.realpath()` or `Path.resolve()` before sufficient validation occurs. On Windows systems, supplying a malicious UNC path (e.g., `\\attacker-controlled-host\share`) can cause the Streamlit server to initiate outbound SMB connections over port 445. When Windows attempts to authenticate to the remote SMB server, NTLMv2 challenge-response credentials of the Windows user running the Streamlit process may be transmitted. This behavior may allow an attacker to perform NTLM relay attacks against other internal services and/or identify internally reachable SMB hosts via timing analysis. The vulnerability has been fixed in Streamlit Open Source version 1.54.0.

An attacker with access to the same internal network segment as a Windows Streamlit server—e.g., a malicious insider, a compromised workstation, or an attacker who has already pivoted into the environment—sets up a rogue SMB server using Responder. They submit a crafted HTTP request to the Streamlit ComponentRequestHandler containing a UNC path (e.g., \\attacker-ip\share) as a filesystem parameter. Streamlit calls os.path.realpath() on the input before validation, causing the Windows host to initiate an SMB connection to the attacker's server. Windows performs NTLMv2 authentication, transmitting the credential hash of the Streamlit service account. The attacker captures the hash and relays it via Impacket's ntlmrelayx to authenticate against another internal service—Active Directory, SQL Server, or a file share—without knowing the plaintext password, achieving lateral movement across the ML infrastructure.