CVE-2020-28440 in corenlp-js-interface
Summary
by MITRE • 12/11/2020
All versions of package corenlp-js-interface are vulnerable to Command Injection via the main function.
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Analysis
by VulDB Data Team • 12/16/2020
The corenlp-js-interface package presents a critical command injection vulnerability that affects all its versions, creating a significant security risk for applications that rely on this JavaScript interface for processing natural language processing tasks. This vulnerability specifically targets the main function within the package, which improperly handles user input without adequate sanitization or validation mechanisms. The flaw allows malicious actors to inject arbitrary commands that execute within the context of the application, potentially leading to complete system compromise. The vulnerability stems from insufficient input validation and improper command construction, where user-provided data is directly incorporated into system execution calls without proper escaping or encoding.
The technical implementation of this vulnerability occurs when the main function processes input parameters that should be treated as data rather than executable commands. Attackers can exploit this by crafting malicious input that includes shell command injection payloads, enabling them to execute arbitrary code on the target system. This type of vulnerability falls under CWE-78, which specifically addresses "Improper Neutralization of Special Elements used in an OS Command" and aligns with the broader category of command injection flaws that have been consistently identified in security frameworks. The attack vector typically involves sending specially crafted requests through the package's interface, where the vulnerable code path concatenates user input directly into system commands without proper sanitization.
The operational impact of this vulnerability extends beyond simple code execution, as it can enable attackers to gain unauthorized access to system resources, escalate privileges, and potentially move laterally within network environments. Applications using this package may become compromised hosts that can be used as launching points for further attacks, making this vulnerability particularly dangerous in enterprise environments where natural language processing systems are commonly deployed. The vulnerability affects not only individual system integrity but also data confidentiality and availability, as attackers could potentially delete files, modify system configurations, or exfiltrate sensitive information. This risk is compounded by the fact that the vulnerability exists in all versions of the package, meaning that even patched applications may still be vulnerable if they continue to use older versions or if the update process fails.
Mitigation strategies for this vulnerability require immediate remediation through updating to a patched version of the corenlp-js-interface package where the command injection flaw has been addressed. Organizations should implement comprehensive input validation and sanitization measures, ensuring that all user-provided data is properly escaped or encoded before being processed by system commands. The implementation of secure coding practices, including parameterized command execution and the principle of least privilege, should be enforced to minimize the potential impact of such vulnerabilities. Additionally, network segmentation and monitoring should be implemented to detect and respond to suspicious command execution patterns. This vulnerability demonstrates the importance of adhering to security best practices as outlined in the MITRE ATT&CK framework, particularly in the execution and privilege escalation phases where command injection attacks commonly occur. Organizations should also conduct thorough security assessments of their dependency management processes to ensure that vulnerable packages are identified and remediated promptly. The vulnerability serves as a reminder of the critical need for continuous security monitoring and the implementation of automated vulnerability scanning tools to identify and address similar issues in third-party dependencies.