CVE-2024-10954 is a vulnerability classified under CWE-94 (Improper Control of Generation of Code) found in the manim plugin of the binary-husky/gpt_academic project. The core issue stems from the plugin executing code generated by a large language model (LLM) based on user-provided prompts without adequate sandboxing or validation. This design flaw allows an attacker to craft malicious prompts that result in arbitrary code execution on the backend server hosting the application. The vulnerability affects unspecified versions prior to the fix and was published on March 20, 2025. The CVSS v3.0 score is 8.8, indicating high severity, with attack vector network (AV:N), low attack complexity (AC:L), requiring privileges (PR:L), no user interaction (UI:N), unchanged scope (S:U), and high impact on confidentiality, integrity, and availability (C:H/I:H/A:H). The exploitation does not require user interaction but does require some level of privilege on the system, which might be a user or service account with access to the plugin. No known exploits have been reported in the wild yet. The vulnerability highlights the risks of executing dynamically generated code from LLMs without proper security controls such as sandboxing, input validation, or code signing. This issue is particularly relevant as AI-powered code generation becomes more integrated into software systems. The lack of patch links suggests that a fix may be pending or recently released, emphasizing the need for immediate attention from users of this software.

For European organizations, the impact of CVE-2024-10954 can be substantial. Successful exploitation leads to remote code execution on backend servers, potentially allowing attackers to fully compromise affected systems. This can result in data breaches, unauthorized access to sensitive information, disruption of services, and lateral movement within networks. Organizations relying on binary-husky/gpt_academic or similar LLM-based code execution frameworks in research, education, or AI development environments are particularly vulnerable. The confidentiality of proprietary AI models, academic research data, and user information could be at risk. Integrity of computational results and availability of services may also be severely impacted, causing operational downtime and reputational damage. Given the increasing adoption of AI tools in European tech sectors, the threat surface is expanding. Additionally, regulatory frameworks such as GDPR impose strict data protection requirements, and a breach exploiting this vulnerability could lead to significant legal and financial penalties.

1. Apply official patches or updates from the binary-husky project as soon as they become available to address this vulnerability. 2. Until patches are deployed, disable or restrict access to the manim plugin or any LLM-based code execution features in production environments. 3. Implement strict input validation and sanitization on all user-provided prompts to prevent injection of malicious code. 4. Employ sandboxing techniques such as containerization or virtual machines to isolate code execution environments, preventing malicious code from affecting the host system. 5. Use runtime monitoring and anomaly detection to identify unusual prompt patterns or execution behaviors indicative of exploitation attempts. 6. Enforce the principle of least privilege for accounts running the plugin to limit the potential impact of a compromise. 7. Conduct security audits and code reviews focusing on AI-generated code execution components. 8. Educate developers and administrators about the risks of executing untrusted code generated by AI models and best practices for secure integration.