Langflow RCE Exploited to Deploy Monero Miner on Exposed AI App Endpoints

Threat actors are continuing to exploit a critical Langflow vulnerability as part of fresh attacks designed to deliver a Monero cryptocurrency miner. The activity has been found to weaponize CVE-2026-33017 (CVSS score: 9.3), an unauthenticated remote code execution (RCE) vulnerability in Langflow, indicating threat actors are scanning and targeting exposed artificial intelligence (AI)

The article reports that threat actors are exploiting CVE-2026-33017, a critical unauthenticated remote code execution vulnerability (CVSS ~9.3–9.8) in Langflow, an open‑source platform used to build and deploy AI agents and workflows, to deploy a Monero cryptocurrency miner on exposed Langflow endpoints.[1][8] The flaw arises in the public flow build endpoint, where attacker‑controlled flow data containing arbitrary Python code is passed directly to exec() without sandboxing, enabling full server compromise, environment variable exfiltration, and arbitrary command execution on AI app infrastructure.[1][3][8] From a CyberSE.AI perspective, this is primarily an AI supply chain risk: organizations are compromised via a third‑party AI framework dependency rather than via model logic or prompts, and exploitation can lead to broader cloud and data exposure across AI pipelines.[3][5] Security implications include the need for rigorous SBOM-driven tracking of AI components, rapid patching or replacement of vulnerable Langflow versions (pre‑1.9.0), network and WAF controls around AI orchestration endpoints, and continuous monitoring for anomalous process activity such as unauthor