CVE-2024-10954: binary-husky/gpt_academic · Code Injection
Affects binary-husky/gpt_academicSource CVE Database V5
Not known to be exploited · CVSS 8.8 High.
Data as of 26 Oct 2026, 17:16 UTC
- EPSS
- 1.5% chance of exploitation in 30 days – higher than 74% of CVEsExploit Prediction Scoring System: the likelihood attackers will use this flaw soon.
CVSS:3.0/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:HExploitation evidence
Disclosed: evidence found.
CVE published 20 Mar 2025
Detectable: no evidence on record.
No public nuclei template indexed
Weaponised: no evidence on record.
No public exploit code indexed in Metasploit or Exploit-DB
Exploited in the wildExploited: no evidence on record.
Not in CISA KEV and no source reports exploitation
Observed by OffSeq MirageSeen by Mirage: no evidence on record.
No attack traffic recorded by OffSeq Mirage sensors
A missing step is not evidence of safety. Sources: cve.org (opens in a new tab), the Metasploit, Exploit-DB and nuclei-templates index as of 26 Oct 2026.
What to do
AI-suggested
- Apply official patches or updates from the binary-husky project as soon as they become available to address this vulnerability.
- Until patches are deployed, disable or restrict access to the manim plugin or any LLM-based code execution features in production environments.
- Implement strict input validation and sanitization on all user-provided prompts to prevent injection of malicious code.
- Employ sandboxing techniques such as containerization or virtual machines to isolate code execution environments, preventing malicious code from affecting the host system.
- Use runtime monitoring and anomaly detection to identify unusual prompt patterns or execution behaviors indicative of exploitation attempts.
- Enforce the principle of least privilege for accounts running the plugin to limit the potential impact of a compromise.
- Conduct security audits and code reviews focusing on AI-generated code execution components.
- Educate developers and administrators about the risks of executing untrusted code generated by AI models and best practices for secure integration.
Description
In the `manim` plugin of binary-husky/gpt_academic, versions prior to the fix, a vulnerability exists due to improper handling of user-provided prompts. The root cause is the execution of untrusted code generated by the LLM without a proper sandbox. This allows an attacker to perform remote code execution (RCE) on the app backend server by injecting malicious code through the prompt.