CVE-2023-33979
Published: 31 May 2023
Summary
CVE-2023-33979 is a medium-severity Exposure of Sensitive Information to an Unauthorized Actor (CWE-200) vulnerability in Binary-Husky Gpt Academic. Its CVSS base score is 6.5 (Medium).
Operationally, ranked in the top 29.1% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2023-38103
Vulnerability details
gpt_academic provides a graphical interface for ChatGPT/GLM. A vulnerability was found in gpt_academic 3.37 and prior. This issue affects some unknown processing of the component Configuration File Handler. The manipulation of the argument file leads to information disclosure. Since no…
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sensitive files are configured to be off-limits, sensitive information files in some working directories can be read through the `/file` route, leading to sensitive information leakage. This affects users that uses file configurations via `config.py`, `config_private.py`, `Dockerfile`. A patch is available at commit 1dcc2873d2168ad2d3d70afcb453ac1695fbdf02. As a workaround, one may use environment variables instead of `config*.py` files to configure this project, or use docker-compose installation to configure this project.
- CWE(s)
Related Threats
No named actor attribution yet. ATT&CK technique mapping in progress for this CVE.
Affected Assets
Mitigating Controls
Likely Mitigating Controls AI
Per-CVE control mapping for this CVE has not run yet; the list below is derived from the weakness types (CWEs) cited in the NVD entry.
Automated marking applies security attributes to system outputs, making it harder for attackers to exploit unmarked sensitive information leading to unauthorized exposure.
Proper attribute retention and permitted-value enforcement limits unauthorized actors from accessing sensitive information lacking correct labels.
Prevents unauthorized exposure of sensitive information by prohibiting untrusted external systems from processing or storing it.
By enforcing authorization matching prior to sharing, the control reduces the risk of exposing sensitive information to unauthorized actors.
Review and removal of nonpublic information from publicly accessible systems directly prevents exposure of sensitive data to unauthorized actors.
Data mining protection mechanisms detect and block unauthorized bulk extraction of sensitive data, directly mitigating exposure to unauthorized actors.
Literacy training teaches users to recognize and avoid actions that result in unauthorized exposure of sensitive information.
Retaining and monitoring training records confirms personnel have completed privacy and security awareness training on handling sensitive data, reducing the chance of unauthorized exposure due to lack of knowledge.