CVE-2024-10986 is a Local File Read vulnerability classified under CWE-59 (Improper Link Resolution Before File Access) affecting the binary-husky GPT Academic software, version 3.83. The vulnerability resides in the HotReload function, which is designed to download and extract tar.gz archives from arxiv.org to update or load academic content dynamically. While the application attempts to prevent path traversal attacks, it fails to address the Tarslip attack vector, where symbolic links inside the tar archive can redirect file extraction paths to arbitrary locations on the local filesystem. This flaw allows an attacker with local privileges to craft malicious tar.gz files that, when processed by HotReload, cause the application to read and potentially disclose sensitive files from the server. The vulnerability does not require user interaction but does require some level of local privilege (PR:L). The CVSS 3.0 score of 8.8 reflects the high impact on confidentiality, integrity, and availability, with network attack vector and low attack complexity. Although no public exploits are known, the risk is significant due to the sensitive nature of academic data and potential for lateral movement or further exploitation. The lack of patch links suggests that a fix may not yet be available, increasing urgency for mitigation.

For European organizations, particularly universities, research centers, and institutions relying on GPT Academic for automated content updates, this vulnerability poses a substantial risk. Exploitation could lead to unauthorized disclosure of sensitive research data, intellectual property, or internal configuration files, undermining confidentiality. Integrity and availability could also be compromised if attackers manipulate or disrupt the HotReload process. Given the academic sector's importance in Europe and the increasing reliance on AI-driven tools, data breaches could have reputational and regulatory consequences, including GDPR violations. Additionally, attackers gaining local file read capabilities might leverage this to escalate privileges or move laterally within networks. The impact is heightened in environments where multiple users share access or where the software runs with elevated permissions.

Immediate mitigation steps include disabling the HotReload feature until a patch is available. Organizations should audit and restrict the sources from which tar.gz files are downloaded, limiting them strictly to trusted repositories. Implementing stricter validation and sanitization of archive contents before extraction is critical, including rejecting archives containing symbolic links or enforcing extraction within sandboxed directories. Employing filesystem access controls to limit the application's read permissions can reduce exposure. Monitoring logs for unusual archive extraction activity or file access patterns can help detect exploitation attempts. Finally, organizations should engage with the vendor or community to obtain patches or updates addressing the Tarslip vulnerability and apply them promptly once released.