CVE-2024-10950: 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, 14:05 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
Affected software
| Product | Affected | Fixed in |
|---|---|---|
GitHubbinary-husky/gpt_academicpkg:github/binary-husky/gpt_academicOther threats | Affected
| Fixed in– |
Check your own hosts for these packages with threat-finder (opens in a new tab), our free open-source scanner.
AI-suggested
Organizations should immediately assess their use of binary-husky/gpt_academic and disable the CodeInterpreter plugin if possible. Implement strict input validation and sanitization to prevent malicious prompt injection. Employ sandboxing or containerization techniques to isolate code execution environments and prevent unauthorized system access. Monitor logs and network traffic for unusual activity indicative of exploitation attempts. Apply any vendor patches or updates as soon as they become available. Restrict access to the application backend to trusted users and networks, and enforce the principle of least privilege for accounts interacting with the vulnerable component. Consider deploying web application firewalls (WAFs) with custom rules to detect and block suspicious input patterns. Regularly review and update incident response plans to include scenarios involving LLM-based code execution vulnerabilities.
Description
In binary-husky/gpt_academic version <= 3.83, the plugin `CodeInterpreter` is vulnerable to code injection caused by prompt injection. The root cause is the execution of user-provided prompts that generate untrusted code without a sandbox, allowing the execution of parts of the LLM-generated code. This vulnerability can be exploited by an attacker to achieve remote code execution (RCE) on the application backend server, potentially gaining full control of the server.