CVE-2026-0764 in GPT Academic
Summary
by MITRE • 01/23/2026
GPT Academic upload Deserialization of Untrusted Data Remote Code Execution Vulnerability. This vulnerability allows remote attackers to execute arbitrary code on affected installations of GPT Academic. Authentication is not required to exploit this vulnerability.
The specific flaw exists within the upload endpoint. The issue results from the lack of proper validation of user-supplied data, which can result in deserialization of untrusted data. An attacker can leverage this vulnerability to execute code in the context of root. Was ZDI-CAN-27957.
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Analysis
by VulDB Data Team • 01/25/2026
The vulnerability identified as CVE-2026-0764 represents a critical deserialization flaw within the GPT Academic upload functionality that enables remote code execution without authentication requirements. This security weakness resides in the application's upload endpoint where user-supplied data undergoes deserialization processes without adequate validation mechanisms. The absence of proper input sanitization creates an exploitable condition that allows attackers to craft malicious payloads that can be executed within the target system's context. The vulnerability specifically targets the deserialization of untrusted data, a well-documented weakness that falls under the CWE-502 category, which encompasses insecure deserialization vulnerabilities that can lead to remote code execution. This flaw is particularly dangerous because it operates without requiring any authentication credentials, making it accessible to any remote attacker who can reach the vulnerable application. The attack vector leverages the application's handling of uploaded files, where the system processes user-submitted data through deserialization routines that fail to validate the integrity and origin of the incoming data. The consequences of successful exploitation extend to full system compromise, as the vulnerability allows execution of arbitrary code with root privileges, effectively granting attackers complete control over the affected system.
The operational impact of this vulnerability extends beyond simple code execution to encompass complete system compromise and potential lateral movement within network environments. Attackers can leverage this vulnerability to establish persistent access, escalate privileges, and potentially use the compromised system as a foothold for further attacks against other networked systems. The lack of authentication requirements significantly amplifies the risk, as this vulnerability can be exploited by anyone with network access to the vulnerable application. From an attack perspective, this vulnerability aligns with the MITRE ATT&CK framework under the T1203 (Exploitation for Client Execution) and T1059 (Command and Scripting Interpreter) techniques, as attackers can execute arbitrary commands through the deserialization process. The vulnerability's severity is compounded by the fact that it operates at the application layer, making it difficult to detect through traditional network-based security measures. Organizations running affected versions of GPT Academic face significant risk exposure, as the vulnerability can be exploited from external networks without any prior authentication or authorization.
Mitigation strategies for CVE-2026-0764 must address both the immediate vulnerability and implement broader security controls to prevent similar issues. The primary recommendation involves implementing proper input validation and sanitization for all user-supplied data, particularly within upload endpoints that process external inputs. Organizations should deploy secure deserialization practices that avoid processing untrusted data through dangerous deserialization methods. This includes implementing strict type checking, using safe serialization formats, and avoiding the use of dangerous deserialization libraries. Security measures should also incorporate proper access controls, network segmentation, and monitoring of upload activities to detect potential exploitation attempts. Additionally, organizations should consider implementing web application firewalls and intrusion detection systems that can identify and block malicious deserialization attempts. The vulnerability's classification as a remote code execution flaw necessitates immediate patching of affected systems, as well as comprehensive security assessments to identify other potential deserialization vulnerabilities within the application stack. Regular security testing including penetration testing and vulnerability scanning should be conducted to ensure that similar issues do not exist in other components of the system. The implementation of defense-in-depth strategies including regular security updates, secure coding practices, and continuous monitoring will help protect against exploitation of this and similar vulnerabilities. Organizations should also establish incident response procedures specifically designed to handle remote code execution vulnerabilities, ensuring rapid response capabilities when such attacks are detected.