CVE-2026-3683 (GCVE-0-2026-3683)
Vulnerability from cvelistv5 – Published: 2026-03-07 23:32 – Updated: 2026-03-11 16:27
VLAI
EPSS
VEX
Title
bufanyun HotGo Endpoint upload.go ImageTransferStorage server-side request forgery
Summary
A vulnerability was detected in bufanyun HotGo up to 2.0. This issue affects the function ImageTransferStorage of the file /server/internal/logic/common/upload.go of the component Endpoint. The manipulation results in server-side request forgery. The attack may be launched remotely. The exploit is now public and may be used. The vendor was contacted early about this disclosure but did not respond in any way.
Severity
SSVC
Exploitation: poc
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-03-11 16:22 UTC
CWE
- CWE-918 - Server-Side Request Forgery
Assigner
References
4 references
| URL | Tags |
|---|---|
| https://vuldb.com/?id.349585 | vdb-entrytechnical-description |
| https://vuldb.com/?ctiid.349585 | signaturepermissions-required |
| https://vuldb.com/?submit.765588 | third-party-advisory |
| https://github.com/CC-T-454455/Vulnerabilities/tr… | exploit |
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Sightings
| Author | Source | Type | Date | Other |
|---|
Nomenclature
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How durations are calculated
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The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the
vulnerability by the CIRCL/vulnerability-attack-technique-classification-roberta-base
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They have not been verified by an analyst and are provided for guidance only.
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.