CVE-2025-31494
Published: 15 April 2025
Summary
CVE-2025-31494 is a low-severity Exposure of Sensitive Information to an Unauthorized Actor (CWE-200) vulnerability in Agpt Autogpt Platform. Its CVSS base score is 3.5 (Low).
Operationally, exploitation aligns with the MITRE ATT&CK technique Data from Information Repositories (T1213); ranked at the 44.1th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.
This vulnerability is AI-related — categorised as AI Agent Protocols and Integrations; in the Privacy and Disclosure risk domain.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-14753
Vulnerability details
AutoGPT is a platform that allows users to create, deploy, and manage continuous artificial intelligence agents that automate complex workflows. The AutoGPT Platform's WebSocket API transmitted node execution updates to subscribers based on the graph_id+graph_version. Additionally, there was no check…
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prohibiting users from subscribing with another user's graph_id+graph_version. As a result, node execution updates from one user's graph execution could be received by another user within the same instance. This vulnerability does not occur between different instances or between users and non-users of the platform. Single-user instances are not affected. In private instances with a user white-list, the impact is limited by the fact that all potential unintended recipients of these node execution updates must have been admitted by the administrator. This vulnerability is fixed in 0.6.1.
- CWE(s)
AI Security AnalysisAI
- AI Category
- AI Agent Protocols and Integrations
- Risk Domain
- Privacy and Disclosure
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- Matched keywords: artificial intelligence, autogpt
Related Threats
MITRE ATT&CK Enterprise TechniquesAI
Why these techniques?
The vulnerability enables unauthorized access to another user's node execution updates via WebSocket API subscription without proper authorization checks, facilitating collection of data from the platform acting as an information repository.
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.
Associating and retaining security attributes with data directly supports enforcement of access control decisions across storage, processing, and transmission.
Enforces rules governing access to the system and its data from external systems based on established trust relationships.
This control requires verifying that a sharing partner's access authorizations match the information's restrictions before sharing occurs.
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.