CVE-2025-14116 (GCVE-0-2025-14116)
Vulnerability from cvelistv5 – Published: 2025-12-05 22:32 – Updated: 2025-12-08 17:14 X_Open Source
VLAI
EPSS
VEX
Title
xerrors Yuxi-Know embed.py OtherEmbedding.aencode server-side request forgery
Summary
A vulnerability was detected in xerrors Yuxi-Know up to 0.4.0. This vulnerability affects the function OtherEmbedding.aencode of the file /src/models/embed.py. Performing manipulation of the argument health_url results in server-side request forgery. The attack can be initiated remotely. The exploit is now public and may be used. The patch is named 0ff771dc1933d5a6b78f804115e78a7d8625c3f3. To fix this issue, it is recommended to deploy a patch. The vendor responded with a vulnerability confirmation and a list of security measures they have established already (e.g. disabled URL parsing, disabled URL upload mode, removed URL-to-markdown conversion).
Severity
SSVC
Exploitation: none
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2025-12-08 17:07 UTC
CWE
- CWE-918 - Server-Side Request Forgery
Assigner
VulDB
CNA
References
5 references
| URL | Tags |
|---|---|
| https://vuldb.com/?id.334492 | vdb-entrytechnical-description |
| https://vuldb.com/?ctiid.334492 | signaturepermissions-required |
| https://vuldb.com/?submit.697380 | third-party-advisory |
| https://www.notion.so/SSRF-vulnerablity-in-Yuxi-K… | exploit |
| https://github.com/xerrors/Yuxi-Know/commit/0ff77… | patch |
Impacted products
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Forecast uses a logistic model when the trend is rising, or an exponential decay model when the trend is falling. Fitted via linearized least squares.
Sightings
| Author | Source | Type | Date | Other |
|---|
Nomenclature
- Seen: The vulnerability was mentioned, discussed, or observed by the user.
- Confirmed: The vulnerability has been validated from an analyst's perspective.
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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
model, served locally by ML-Gateway.
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
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