CVE-2024-12388 identifies a Regular Expression Denial of Service (ReDoS) vulnerability in the binary-husky/gpt_academic software, specifically in version 310122f and potentially other unspecified versions. The vulnerability stems from the use of a regular expression that exhibits polynomial time complexity when processing certain crafted user inputs. An attacker can exploit this by sending a small malicious payload designed to trigger catastrophic backtracking in the regex engine, causing the server to consume excessive CPU resources and become unresponsive. This results in a denial of service condition, impacting the availability of the application to legitimate users. The vulnerability requires network access and low privileges (PR:L), but no user interaction (UI:N) is necessary, making it easier to exploit remotely. The CVSS 3.0 vector (AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H) reflects that the attack can be launched remotely with low complexity and no impact on confidentiality or integrity, but high impact on availability. No patches or exploit code are currently publicly available, and no known active exploitation has been reported. The vulnerability is classified under CWE-1333, which relates to inefficient regular expression complexity leading to performance degradation. This type of vulnerability is particularly dangerous in server-side applications that parse user input with regex, as it can be triggered with minimal data but cause disproportionate resource consumption.

For European organizations, the primary impact of CVE-2024-12388 is the potential for denial of service attacks against systems running binary-husky/gpt_academic. This could disrupt academic, research, or other institutional services relying on this software, leading to downtime and loss of productivity. Since the vulnerability affects availability only, confidentiality and integrity remain intact. However, service outages can indirectly affect operational continuity and user trust. Organizations with public-facing instances of the software are at higher risk, as attackers can exploit the vulnerability remotely without user interaction. The lack of known exploits reduces immediate risk, but the medium CVSS score and ease of exploitation suggest that attackers could develop exploits. Additionally, the vulnerability could be leveraged as part of a larger attack chain or to cause distraction during other malicious activities. European academic institutions and research centers using this software may face targeted attacks aiming to disrupt their operations.

To mitigate CVE-2024-12388, organizations should first identify all instances of binary-husky/gpt_academic in their environment. Since no official patches are currently available, immediate steps include: 1) Reviewing and refactoring the vulnerable regular expressions to use more efficient patterns or alternative parsing methods that avoid catastrophic backtracking. 2) Implementing input validation to reject or sanitize inputs that could trigger the ReDoS condition before they reach the regex engine. 3) Applying rate limiting and connection throttling on network interfaces to reduce the impact of repeated malicious requests. 4) Monitoring application performance and logs for signs of abnormal CPU usage or request patterns indicative of ReDoS attempts. 5) Isolating the vulnerable service behind web application firewalls (WAFs) or reverse proxies that can detect and block suspicious payloads. 6) Engaging with the vendor or open-source maintainers for updates or patches and applying them promptly once available. 7) Considering temporary disabling or restricting access to the vulnerable functionality if feasible until a fix is deployed.