CVE-2025-6984 is a high-severity vulnerability affecting the langchain-ai/langchain project, specifically the EverNoteLoader component in version 0.3.63. The vulnerability is an XML External Entity (XXE) attack vector caused by insecure XML parsing practices. The root cause lies in the use of the etree.iterparse() function without disabling external entity references. This allows an attacker to craft malicious XML payloads that include external entity declarations referencing local files on the system where the parser runs. When processed, these external references can cause the parser to disclose sensitive local files such as /etc/passwd. This is classified under CWE-200, indicating exposure of sensitive information to unauthorized actors. The vulnerability requires no authentication or user interaction and can be exploited remotely over the network, as indicated by the CVSS vector (AV:N/AC:L/PR:N/UI:N). While no known exploits are currently reported in the wild, the vulnerability's nature and ease of exploitation make it a significant risk. The impact is limited to confidentiality, with no direct integrity or availability effects. The affected product, langchain-ai/langchain, is a framework used for building applications with language models, often integrated into various software solutions that process XML data. Without proper mitigation, attackers can leverage this vulnerability to extract sensitive configuration files or other critical data from affected systems, potentially leading to further compromise or information leakage.

For European organizations, the impact of CVE-2025-6984 could be substantial, especially for those leveraging langchain-ai/langchain in their AI-driven applications or services that parse XML data. Exposure of sensitive files can lead to leakage of credentials, configuration details, or other private information, which may facilitate lateral movement or targeted attacks. Organizations in sectors such as finance, healthcare, and government, which often handle sensitive data and may use advanced AI tools, are at particular risk. Additionally, the breach of confidentiality could result in regulatory penalties under GDPR if personal or sensitive data is exposed. The lack of required authentication and user interaction means that attackers can exploit this vulnerability remotely and silently, increasing the risk of undetected data breaches. Furthermore, the integration of langchain components into larger software ecosystems means that the vulnerability could serve as an entry point for more complex attack chains.

To mitigate CVE-2025-6984, organizations should immediately update langchain-ai/langchain to a patched version once available. In the absence of an official patch, developers should disable external entity processing in XML parsers by configuring etree.iterparse() with secure parser settings that prohibit external entity resolution. Specifically, use XML parser libraries or configurations that explicitly disable DTD processing and external entity expansion. Code audits should be conducted to identify all XML parsing instances and ensure secure parsing practices are enforced. Additionally, implement strict input validation and sanitization for any XML data processed by the application. Network-level controls such as restricting outbound connections from application servers can limit the ability of attackers to exploit XXE for out-of-band data exfiltration. Monitoring and logging XML parsing errors or unusual file access patterns can help detect exploitation attempts. Finally, organizations should review and minimize the exposure of sensitive files on systems running langchain components to reduce the potential impact of data disclosure.