CVE-2023-26149 (GCVE-0-2023-26149)
Vulnerability from cvelistv5 – Published: 2023-09-28 05:00 – Updated: 2024-09-23 19:01
VLAI
EPSS
VEX
Summary
Versions of the package quill-mention before 4.0.0 are vulnerable to Cross-site Scripting (XSS) due to improper user-input sanitization, via the renderList function.
**Note:**
If the mentions list is sourced from unsafe (user-sourced) data, this might allow an injection attack when a Quill user hits @.
Severity
6.1 (Medium)
SSVC
Exploitation: poc
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2024-09-23 19:01 UTC
CWE
- CWE-79 - Cross-site Scripting (XSS)
Assigner
References
6 references
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| n/a | quill-mention |
Affected:
0 , < 4.0.0
(semver)
|
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"vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:C/C:L/I:L/A:N/E:P",
"version": "3.1"
}
}
],
"problemTypes": [
{
"descriptions": [
{
"cweId": "CWE-79",
"description": "Cross-site Scripting (XSS)",
"lang": "en"
}
]
}
],
"providerMetadata": {
"dateUpdated": "2023-09-28T05:00:02.741Z",
"orgId": "bae035ff-b466-4ff4-94d0-fc9efd9e1730",
"shortName": "snyk"
},
"references": [
{
"url": "https://security.snyk.io/vuln/SNYK-JS-QUILLMENTION-5921549"
},
{
"url": "https://github.com/quill-mention/quill-mention/blob/0aa9847719257496b14ac5401872c4e2ffcbc3d1/src/quill.mention.js%23L391"
},
{
"url": "https://codepen.io/ALiangLiang/pen/mdQMJXK"
},
{
"url": "https://github.com/quill-mention/quill-mention/issues/255"
},
{
"url": "https://github.com/quill-mention/quill-mention/pull/341"
},
{
"url": "https://github.com/quill-mention/quill-mention/commit/e85262ddced0a7f0b6fc8350d236a68bd1e28385"
}
]
}
},
"cveMetadata": {
"assignerOrgId": "bae035ff-b466-4ff4-94d0-fc9efd9e1730",
"assignerShortName": "snyk",
"cveId": "CVE-2023-26149",
"datePublished": "2023-09-28T05:00:02.741Z",
"dateReserved": "2023-02-20T10:28:48.929Z",
"dateUpdated": "2024-09-23T19:01:41.735Z",
"state": "PUBLISHED"
},
"dataType": "CVE_RECORD",
"dataVersion": "5.1"
}
}
}
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Experimental. This forecast is provided for visualization only and may change without notice. Do not use it for operational decisions.
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.
- Published Proof of Concept: A public proof of concept is available for this vulnerability.
- Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
- Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
- Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
- Not confirmed: The user expressed doubt about the validity of the vulnerability.
- Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.
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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.
Browse all ATT&CK techniques and the vulnerabilities related to each.
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.
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