CVE-2024-8072
Published: 22 August 2024
Summary
CVE-2024-8072 is a medium-severity Exposure of Sensitive Information to an Unauthorized Actor (CWE-200) vulnerability in Mage Mage-Ai. Its CVSS base score is 5.3 (Medium).
Operationally, exploitation aligns with the MITRE ATT&CK technique Shell History (T1552.003); ranked at the 35.7th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog; a public proof-of-concept is referenced.
This vulnerability is AI-related — categorised as Other Platforms; in the Privacy and Disclosure risk domain; MITRE ATLAS techniques in scope: Valid Accounts (AML.T0012), Discover AI Model Ontology (AML.T0013), Discover AI Model Family (AML.T0014).
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2024-2559
Vulnerability details
Mage AI allows remote unauthenticated attackers to leak the terminal server command history of arbitrary users
- CWE(s)
AI Security AnalysisAI
- AI Category
- Other Platforms
- Risk Domain
- Privacy and Disclosure
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- Mage AI is a data pipeline orchestration platform used for AI/ML workflows, featuring components like terminal servers for development and management, making it an AI-related platform; the vulnerability is an infoleak in its terminal server websocket endpoint.
Related Threats
MITRE ATT&CK Enterprise TechniquesAI
Why these techniques?
The vulnerability enables remote unauthenticated attackers to leak terminal server command history of arbitrary users, directly facilitating the retrieval of unsecured credentials or sensitive information from shell history (T1552.003).
MITRE ATLAS TechniquesAI
MITRE ATLAS techniques
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.