CVE-2022-0198 in corenlp
Summary
by MITRE • 01/13/2022
corenlp is vulnerable to Improper Restriction of XML External Entity Reference
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Analysis
by VulDB Data Team • 01/16/2022
The vulnerability identified as CVE-2022-0198 affects the corenlp library, which is a popular natural language processing toolkit developed by Stanford University. This security flaw represents a critical improper restriction of XML external entity reference vulnerability that can be exploited by malicious actors to manipulate the processing of XML data within the application. The corenlp library is widely used for various NLP tasks including sentence parsing, named entity recognition, and sentiment analysis, making this vulnerability particularly concerning for organizations that rely on its functionality for processing untrusted input data.
The technical flaw stems from the library's insufficient validation and sanitization of XML input data, specifically when handling external entity references during XML parsing operations. When corenlp processes XML documents that contain maliciously crafted external entity declarations, it fails to properly restrict or disable the resolution of external references, allowing attackers to inject arbitrary XML entities. This behavior creates a potential attack vector where an adversary could leverage the vulnerability to perform server-side request forgery attacks, read arbitrary files from the server filesystem, or even execute remote code depending on the underlying XML processing implementation and the environment in which corenlp operates. The vulnerability is classified under CWE-611 as Improper Restriction of XML External Entity Reference, which is a well-known weakness pattern that has been exploited in numerous high-profile security incidents.
The operational impact of this vulnerability extends beyond simple data corruption or information disclosure. Organizations using corenlp for processing user-generated content or third-party XML data may face significant security risks including unauthorized access to sensitive system resources, data exfiltration, and potential service disruption. Attackers could exploit this weakness to gain access to internal network resources through server-side request forgery, or to perform denial of service attacks by consuming excessive system resources through malicious XML entity expansion. The vulnerability is particularly dangerous in web applications where corenlp is used to process XML data submitted by users, as it can be leveraged to bypass traditional security controls and gain unauthorized access to backend systems. This weakness aligns with ATT&CK technique T1059.007 for XML External Entity Processing and T1071.004 for Application Layer Protocol: XML, making it a significant concern for threat actors targeting enterprise applications.
Mitigation strategies for CVE-2022-0198 should focus on implementing proper XML parsing configurations that disable external entity resolution and DTD processing entirely. Organizations should upgrade to patched versions of the corenlp library where available, as the maintainers have released updates that address the XML external entity handling vulnerability. Additionally, developers should implement input validation and sanitization measures for all XML data processed by the application, ensuring that external entity references are properly restricted or eliminated during parsing operations. Security teams should consider implementing network-level controls and monitoring for suspicious XML processing patterns, while also conducting thorough code reviews to identify any other potential XML processing vulnerabilities within their applications. The remediation efforts should align with industry best practices for XML security, including the implementation of secure XML parsers that enforce strict validation rules and prevent the resolution of external entities. Organizations should also establish proper incident response procedures to address potential exploitation attempts and maintain detailed logging of XML processing activities for forensic analysis purposes.