CVE-2025-68477 is a Server-Side Request Forgery (SSRF) vulnerability identified in the langflow AI tool, specifically affecting versions prior to 1.7.0. Langflow provides an API Request component that allows users to specify URLs for HTTP requests executed server-side using the httpx client. The vulnerability stems from insufficient validation of user-supplied URLs: the component performs only normalization and basic format checks but does not block requests to private IP ranges (such as 127.0.0.1, 10.x.x.x, 172.x.x.x, 192.x.x.x) or cloud metadata service endpoints (169.254.169.254). Because the flow execution endpoints (/api/v1/run and /api/v1/run/advanced) can be invoked with just an API key, an attacker who can control the URL parameter in a flow can cause the server to issue arbitrary HTTP requests within its internal network context. This non-blind SSRF enables attackers to access internal administrative interfaces, cloud metadata services, and internal databases or services that are otherwise inaccessible externally. The vulnerability allows attackers to retrieve sensitive information, such as credentials or configuration data, which can be leveraged for further lateral movement or privilege escalation. The CVSS v3.1 base score is 7.7 (high), reflecting network attack vector, low complexity, low privileges required, no user interaction, and a scope change with high confidentiality impact but no integrity or availability impact. The vulnerability was published on December 19, 2025, and patched in langflow version 1.7.0. No known exploits in the wild have been reported yet. Organizations using langflow in AI automation workflows should prioritize upgrading to the patched version to prevent exploitation.

For European organizations, the impact of CVE-2025-68477 can be significant, especially for those leveraging langflow to automate AI-powered workflows that interact with internal systems. Exploitation could lead to unauthorized disclosure of sensitive internal data, including cloud metadata that may contain credentials or tokens for further access. This can compromise confidentiality and potentially enable attackers to pivot within the network, access internal administrative endpoints, or exfiltrate data. Given the increasing adoption of AI tools and cloud services in Europe, organizations running langflow on cloud infrastructure or within private networks are at risk of internal network reconnaissance and data leakage. The vulnerability could also undermine trust in AI automation platforms and lead to regulatory compliance issues under GDPR if personal or sensitive data is exposed. The absence of known exploits reduces immediate risk but does not eliminate the threat, as attackers may develop exploits rapidly once the vulnerability is public. The requirement of an API key limits exposure but does not eliminate risk, especially if API keys are leaked or insufficiently protected.

European organizations should take the following specific mitigation steps: 1) Immediately upgrade langflow installations to version 1.7.0 or later, which contains the patch that properly restricts SSRF vectors. 2) Audit and restrict API key distribution and usage to minimize the risk of unauthorized access to flow execution endpoints. 3) Implement network segmentation and firewall rules to limit the server's ability to access sensitive internal IP ranges and cloud metadata endpoints, adding a defense-in-depth layer. 4) Monitor API usage logs for unusual or unexpected requests targeting internal IP ranges or metadata endpoints. 5) Employ runtime application self-protection (RASP) or web application firewalls (WAFs) that can detect and block SSRF patterns. 6) Conduct internal security assessments and penetration tests focusing on SSRF and internal resource access via AI workflow tools. 7) Educate developers and administrators about the risks of SSRF in AI automation platforms and enforce secure coding and configuration practices. These steps go beyond generic advice by focusing on controlling API key access, network-level restrictions, and active monitoring tailored to the langflow environment.