CVE-2024-30252 (GCVE-0-2024-30252)
Vulnerability from cvelistv5 – Published: 2024-04-04 18:57 – Updated: 2025-08-27 21:12
VLAI
EPSS
VEX
Title
GitHub Security Lab (GHSL) Vulnerability Report, livemarks: `GHSL-2024-015`
Summary
Livemarks is a browser extension that provides RSS feed bookmark folders. Versions of Livemarks prior to 3.7 are vulnerable to cross-site request forgery. A malicious website may be able to coerce the extension to send an authenticated GET request to an arbitrary URL. An authenticated request is a request where the cookies of the browser are sent along with the request. The `subscribe.js` script uses the first parameter from the current URL location as the URL of the RSS feed to subscribe to and checks that the RSS feed is valid XML. `subscribe.js` is accessible by an attacker website due to its use in `subscribe.html`, an HTML page that is declared as a `web_accessible_resource` in `manifest.json`. This issue may lead to `Privilege Escalation`. A CSRF breaks the integrity of servers running on a private network. A user of the browser extension may have a private server with dangerous functionality, which is assumed to be safe due to network segmentation. Upon receiving an authenticated request instantiated from an attacker, this integrity is broken. Version 3.7 fixes this issue by removing subscribe.html from `web_accessible_resources`.
Severity
SSVC
Exploitation: poc
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2024-07-15 15:48 UTC
CWE
- CWE-352 - Cross-Site Request Forgery (CSRF)
Assigner
GitHub_M
CNA
References
5 references
| URL | Tags |
|---|---|
| https://github.com/nt1m/livemarks/security/adviso… | x_refsource_CONFIRM |
| https://github.com/nt1m/livemarks/commit/1bcf39f6… | x_refsource_MISC |
| https://github.com/nt1m/livemarks/blob/9c5233c82a… | x_refsource_MISC |
| https://github.com/nt1m/livemarks/blob/9c5233c82a… | x_refsource_MISC |
| https://github.com/nt1m/livemarks/blob/9c5233c82a… | x_refsource_MISC |
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The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the
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The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
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