CVE-2025-64327 (GCVE-0-2025-64327)
Vulnerability from cvelistv5 – Published: 2025-11-06 21:07 – Updated: 2025-11-06 21:29
VLAI
EPSS
VEX
Title
ThinkDashboard: Blind Server-Side Request Forgery (SSRF) vulnerability in /api/ping Endpoint
Summary
ThinkDashboard is a self-hosted bookmark dashboard built with Go and vanilla JavaScript. Versions 0.6.7 and below contain a Blind Server-Side Request Forgery (SSRF) vulnerability, in its `/api/ping?url= endpoint`. This allows an attacker to make arbitrary requests to internal or external hosts. This can include discovering ports open on the local machine, hosts on the local network, and ports open on the hosts on the internal network. This issue is fixed in version 0.6.8.
Severity
5.3 (Medium)
SSVC
Exploitation: poc
Automatable: yes
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2025-11-06 21:29 UTC
CWE
- CWE-918 - Server-Side Request Forgery (SSRF)
Assigner
References
3 references
| URL | Tags |
|---|---|
| https://github.com/MatiasDesuu/ThinkDashboard/sec… | x_refsource_CONFIRM |
| https://github.com/MatiasDesuu/ThinkDashboard/com… | x_refsource_MISC |
| https://github.com/MatiasDesuu/ThinkDashboard/rel… | x_refsource_MISC |
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| MatiasDesuu | ThinkDashboard |
Affected:
< 0.6.8
|
guessed |
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Sightings
| Author | Source | Type | Date | Other |
|---|
Nomenclature
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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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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.
Browse all ATT&CK techniques and the vulnerabilities related to each.
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Related by attack behaviour
Vulnerabilities whose description is nearest to this one in the vector space of the CIRCL/vulnerability-attack-technique-biencoder model. This is a similarity search over the bi-encoder space (plain cosine), not a classification, and it has no measured accuracy.