CVE-2024-10100 in gpt_academic
Summary
by MITRE • 10/17/2024
A path traversal vulnerability exists in binary-husky/gpt_academic version 3.83. The vulnerability is due to improper handling of the file parameter, which is open to path traversal through URL encoding. This allows attackers to view any file on the host system, including sensitive files such as critical application files, SSH keys, API keys, and configuration values.
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Analysis
by VulDB Data Team • 07/12/2025
The vulnerability identified as CVE-2024-10100 represents a critical path traversal flaw within the binary-husky/gpt_academic application version 3.83. This security weakness stems from inadequate input validation and sanitization of the file parameter that processes user-supplied data through URL encoding mechanisms. The flaw creates an exploitable condition where malicious actors can manipulate file paths to access arbitrary files on the host system beyond the intended application boundaries. The vulnerability's severity is amplified by the fact that it allows access to sensitive system resources including critical application files, SSH private keys, API credentials, and configuration data that could lead to complete system compromise.
The technical implementation of this path traversal vulnerability occurs when the application fails to properly validate or sanitize the file parameter received through HTTP requests. Attackers can exploit this by crafting malicious URLs that contain encoded path traversal sequences such as ../ or %2e%2e%2f which bypass normal file access controls. When the application processes these malformed paths, it fails to properly resolve the intended file location, instead traversing the file system to access files outside the designated application directory. This improper handling of user input creates a direct pathway for unauthorized file access and data exfiltration, as the application does not implement proper path validation or canonicalization techniques to prevent such traversal attacks.
The operational impact of CVE-2024-10100 extends far beyond simple unauthorized file access, creating significant risk for organizations relying on the affected gpt_academic application. Successful exploitation could result in the disclosure of sensitive credentials, private keys, and configuration files that may contain database connection strings, API tokens, or other authentication mechanisms. The vulnerability exposes the entire file system to potential reconnaissance and exploitation, allowing attackers to map application directories and identify additional attack vectors. This could lead to privilege escalation, lateral movement within networks, and ultimately complete system compromise. The vulnerability aligns with CWE-22 Path Traversal and follows patterns consistent with ATT&CK technique T1083 File and Directory Discovery, where adversaries seek to understand the target system's file structure.
Mitigation strategies for CVE-2024-10100 should focus on implementing robust input validation and sanitization mechanisms within the application's file handling routines. Organizations should immediately upgrade to patched versions of the binary-husky/gpt_academic application where available, as this represents the most effective defense against the vulnerability. Additionally, implementing proper path validation through canonicalization techniques, restricting file access to specific directories, and employing principle of least privilege access controls can significantly reduce the attack surface. Network-level protections including web application firewalls and intrusion detection systems should be configured to monitor for suspicious path traversal patterns and URL encoding sequences. Regular security auditing and penetration testing should be conducted to identify similar vulnerabilities in other applications and ensure that proper input validation is consistently applied across all file handling operations within the organization's infrastructure.