CVE-2025-58751
Published: 08 September 2025
Summary
CVE-2025-58751 is a low-severity Path Traversal (CWE-22) vulnerability in Vitejs Vite. Its CVSS base score is 2.3 (Low).
Operationally, exploitation aligns with the MITRE ATT&CK technique Data from Local System (T1005); ranked in the top 18.9% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog; a public proof-of-concept is referenced.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-27181
Vulnerability details
Vite is a frontend tooling framework for JavaScript. Prior to versions 7.1.5, 7.0.7, 6.3.6, and 5.4.20, files starting with the same name with the public directory were served bypassing the `server.fs` settings. Only apps that explicitly expose the Vite dev…
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server to the network (using --host or `server.host` config option), use the public directory feature (enabled by default), and have a symlink in the public directory are affected. Versions 7.1.5, 7.0.7, 6.3.6, and 5.4.20 fix the issue.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise TechniquesAI
Why these techniques?
Path traversal vulnerability via symlinks in Vite dev server enables remote file access outside public directory (T1005 Data from Local System, T1083 File and Directory Discovery) and exploits public-facing web server (T1190 Exploit Public-Facing Application).
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.
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.