CVE-2026-41272

Deeper analysis AI-assisted summary

Synthesised by an AI model from the NVD description and linked references — a reading aid, not an authoritative source.

CVE-2026-41272 is a Server-Side Request Forgery (SSRF) vulnerability affecting Flowise, an open-source drag-and-drop user interface for building customized large language model (LLM) flows. In versions prior to 3.1.0, the core security wrappers—secureAxiosRequest and secureFetch—designed to prevent SSRF through allow/deny lists contain multiple logic flaws. These include bypasses via DNS rebinding exploiting a Time-of-Check Time-of-Use (TOCTOU) condition, as well as a default configuration that fails to enforce any deny list. The issue is classified under CWE-918 with a CVSS v3.1 base score of 7.1 (AV:N/AC:H/PR:L/UI:N/S:U/C:H/I:H/A:L).

An attacker with low privileges, such as an authenticated user, can exploit this vulnerability over the network with high attack complexity and no user interaction required. Successful exploitation allows high-impact confidentiality and integrity violations—such as unauthorized access to internal network resources or sensitive data—along with low availability impact, potentially enabling actions like reading internal services or modifying data via forged requests.

The official GitHub security advisory (GHSA-2x8m-83vc-6wv4) confirms the vulnerability is fully fixed in Flowise version 3.1.0, recommending immediate upgrades for all prior installations to mitigate the SSRF bypass risks.

Flowise's focus on LLM workflow orchestration introduces AI/ML relevance, as exploited SSRF could potentially target internal AI model endpoints or data pipelines in deployed environments. No public evidence of real-world exploitation has been reported as of the CVE publication on 2026-04-23.