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VulnerabilityPublished Updated

CVE-2024-10954: binary-husky/gpt_academic · Code Injection

Affects binary-husky/gpt_academicSource CVE Database V5

0

Not known to be exploited · CVSS 8.8 High.

Data as of 26 Oct 2026, 17:16 UTC

CVSS 3.0 base score
8.8High
How severe the flaw is on the 0–10 CVSS scale.
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:H

Exploitation evidence

  1. Disclosed: evidence found.

    CVE published 20 Mar 2025

  2. Detectable: no evidence on record.

    No public nuclei template indexed

  3. Weaponised: no evidence on record.

    No public exploit code indexed in Metasploit or Exploit-DB

  4. Exploited in the wild: no evidence on record.

    Not in CISA KEV and no source reports exploitation

  5. Observed by OffSeq 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

  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.

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.

References

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