High-Severity Flaws in Langflow and vLLM Expose Secrets and Enable RCE

Two high-severity vulnerabilities were disclosed in widely used AI application components, affecting Langflow and vLLM. In Langflow, CVE-2026-33497 impacts versions before 1.7.1 and stems from improper filtering of folder_name and file_name in the /profile_pictures/{folder_name}/{file_name} endpoint. The path traversal flaw (CWE-22) allows unauthenticated attackers to read files across directories, including the application's secret_key, creating a direct risk of secret exposure and follow-on compromise. The issue is addressed in Langflow 1.7.1 and tracked in GitHub advisory GHSA-ph9w-r52h-28p7.

A separate flaw in vLLM, CVE-2026-27893, can lead to remote code execution by bypassing a user's attempt to disable remote code trust. In versions from 0.10.1 up to but not including 0.18.0, two model implementation files hardcoded trust_remote_code=True, overriding the safer --trust-remote-code=False setting and allowing malicious model repositories to run code during model use. The vulnerability, classified as CWE-693, was patched in vLLM 0.18.0, underscoring supply-chain and configuration-bypass risks in AI infrastructure components.