CVE-2025-59434
Published: 22 September 2025
Summary
CVE-2025-59434 is a critical-severity Exposure of Sensitive Information to an Unauthorized Actor (CWE-200) vulnerability. Its CVSS base score is 9.6 (Critical).
Operationally, exploitation aligns with the MITRE ATT&CK technique Steal Application Access Token (T1528); ranked at the 23.9th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.
This vulnerability is AI-related — categorised as LLM Application Platforms; in the Privacy and Disclosure risk domain.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-30835
Vulnerability details
Flowise is a drag & drop user interface to build a customized large language model flow. Prior to August 2025 Cloud-Hosted Flowise, an authenticated vulnerability in Flowise Cloud allows any user on the free tier to access sensitive environment variables…
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from other tenants via the Custom JavaScript Function node. This includes secrets such as OpenAI API keys, AWS credentials, Supabase tokens, and Google Cloud secrets — resulting in a full cross-tenant data exposure. This issue has been patched in the August 2025 Cloud-Hosted Flowise.
- CWE(s)
AI Security AnalysisAI
- AI Category
- LLM Application Platforms
- Risk Domain
- Privacy and Disclosure
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- Matched keywords: flowise, large language model, openai
Related Threats
MITRE ATT&CK Enterprise TechniquesAI
Why these techniques?
The vulnerability enables free tier users to access other tenants' environment variables containing unsecured cloud credentials (e.g., API keys, AWS credentials), facilitating discovery and theft via T1552 (Unsecured Credentials) and specifically T1528 (Steal Application Access Token).
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.