Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:HSummary
CVE-2024-31224 is a critical-severity Deserialization of Untrusted Data (CWE-502) vulnerability in Binary-Husky Gpt Academic. Its CVSS base score is 9.8 (Critical).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked in the top 34% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog.
This vulnerability is AI-related — categorised as LLM Application Platforms; in the Other ATLAS/OWASP Terms risk domain.
The strongest mitigations our analysis identified map to SA-11 (Developer Testing and Evaluation) and SI-10 (Information Input Validation) — see the control section below for these in your framework.
Deeper analysis AI-assisted summary
Synthesised by an AI model from the NVD description and linked references — a reading aid, not an authoritative source.
CVE-2024-31224 is a remote code execution vulnerability caused by unsafe deserialization of untrusted data (CWE-502) in GPT Academic, an application that provides interactive web interfaces for large language models. The flaw affects versions 3.64 through 3.73; any instance exposing the service to the network is impacted. The CVSS 3.1 base score is 9.8, reflecting network attack vector, no required credentials or user interaction, and full confidentiality, integrity, and availability impact.
An unauthenticated remote attacker can supply a malicious serialized payload to the server, resulting in arbitrary code execution on the host. Because the service is typically deployed as a user-facing web application, exposure to the internet directly enables this vector without additional prerequisites.
The official GitHub Security Advisory and accompanying patch in version 3.74 address the issue by correcting the deserialization logic. No other workarounds are documented; administrators must upgrade to the fixed release.
GPT Academic is an LLM-interfacing tool, placing the vulnerability in an AI/ML-adjacent software category. The associated EPSS score reached a modest peak of 0.0583 before receding to the current value of 0.0332, indicating limited and non-persistent exploitation interest after disclosure.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2024-29120
Vulnerability Data
GPT Academic provides interactive interfaces for large language models. A vulnerability was found in gpt_academic versions 3.64 through 3.73. The server deserializes untrustworthy data from the client, which may risk remote code execution. Any device that exposes the GPT Academic…
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service to the Internet is vulnerable. Version 3.74 contains a patch for the issue. There are no known workarounds aside from upgrading to a patched version.
- CWE(s)
AI Security AnalysisAI
- AI Category
- LLM Application Platforms
- Risk Domain
- Other ATLAS/OWASP Terms
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- GPT Academic is an interactive interface for large language models (LLMs), fitting the Enterprise AI Assistants category as it provides user-facing tools for AI model interaction.
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Mitigating Controls (NIST 800-53 r5) AI
Developer testing and evaluation can uncover deserialization flaws before deployment.
Input validation directly stops deserialization of untrusted data by ensuring inputs are valid before processing.
Engineering principles such as safe deserialization and input sanitization structurally prevent the weakness from being introduced.
Integrity verification tools can detect malformed or tampered serialized data after the fact.
Mitigating Controls (NIST CSF 2.0) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→CSF cross-walk (authority under review) — links open the control.
PR.PS-02 addresses only post-deployment updates/patching and cannot prevent introduction of unsafe deserialization code, yet it can remediate some instances when the flaw exists in outdated libraries or components.
Mitigating Controls (ISO/IEC 27001:2022 Annex A) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→ISO cross-walk (authority under review) — links open the control.
Security testing includes validation of deserialization routines and the use of untrusted data, reducing the likelihood that unsafe object reconstruction will be deployed.
Requiring vetted libraries, regular updates and SAST before release reduces the likelihood that deserialization logic will accept and act on attacker-controlled serialized objects.
Regular scanning of third-party libraries and timely patching reduce the likelihood that unsafe deserialization vulnerabilities remain active.
Mandatory malware scanning of data received over networks or storage media intercepts malicious serialized payloads before they are deserialized by the target application.