FKIE_CVE-2020-24897
Vulnerability from fkie_nvd - Published: 2020-08-29 20:15 - Updated: 2026-06-17 03:06
Severity
8.9 (High) - CVSS:3.1/AV:N/AC:L/PR:L/UI:R/S:C/C:H/I:H/A:L
8.9 (High) - CVSS:3.1/AV:N/AC:L/PR:L/UI:R/S:C/C:H/I:H/A:L
8.9 (High) - CVSS:3.1/
Summary
The Table Filter and Charts for Confluence Server app before 5.3.25 (for Atlassian Confluence) allow remote attackers to inject arbitrary HTML or JavaScript via cross site scripting (XSS) through the provided Markdown markup to the "Table from CSV" macro.
References
| URL | Tags | ||
|---|---|---|---|
| cve@mitre.org | https://stiltsoft.atlassian.net/browse/VD-2 | Vendor Advisory | |
| af854a3a-2127-422b-91ae-364da2661108 | https://stiltsoft.atlassian.net/browse/VD-2 | Vendor Advisory |
Impacted products
| Vendor | Product | Version | |
|---|---|---|---|
| stiltsoft | table_filter_and_charts_for_confluence_server | * |
{
"affected": [
{
"affectedData": [
{
"product": "n/a",
"vendor": "n/a",
"versions": [
{
"status": "affected",
"version": "n/a"
}
]
}
],
"source": "cve@mitre.org"
}
],
"configurations": [
{
"nodes": [
{
"cpeMatch": [
{
"criteria": "cpe:2.3:a:stiltsoft:table_filter_and_charts_for_confluence_server:*:*:*:*:*:*:*:*",
"matchCriteriaId": "118B1C63-1585-4F75-ACD8-B4126C5BBFB2",
"versionEndExcluding": "5.3.25",
"vulnerable": true
}
],
"negate": false,
"operator": "OR"
}
]
}
],
"cveTags": [],
"descriptions": [
{
"lang": "en",
"value": "The Table Filter and Charts for Confluence Server app before 5.3.25 (for Atlassian Confluence) allow remote attackers to inject arbitrary HTML or JavaScript via cross site scripting (XSS) through the provided Markdown markup to the \"Table from CSV\" macro."
},
{
"lang": "es",
"value": "La aplicaci\u00f3n Table Filter y Charts para Confluence Server versiones anteriores a 5.3.25 (para Atlassian Confluence), permite a atacantes remotos inyectar HTML o JavaScript arbitrario por medio de un ataque de tipo cross site scripting (XSS) mediante el marcado Markdown proporcionado en la macro \"Table from CSV\""
}
],
"id": "CVE-2020-24897",
"lastModified": "2026-06-17T03:06:07.967",
"metrics": {
"cvssMetricV2": [
{
"acInsufInfo": false,
"baseSeverity": "LOW",
"cvssData": {
"accessComplexity": "MEDIUM",
"accessVector": "NETWORK",
"authentication": "SINGLE",
"availabilityImpact": "NONE",
"baseScore": 3.5,
"confidentialityImpact": "NONE",
"integrityImpact": "PARTIAL",
"vectorString": "AV:N/AC:M/Au:S/C:N/I:P/A:N",
"version": "2.0"
},
"exploitabilityScore": 6.8,
"impactScore": 2.9,
"obtainAllPrivilege": false,
"obtainOtherPrivilege": false,
"obtainUserPrivilege": false,
"source": "nvd@nist.gov",
"type": "Primary",
"userInteractionRequired": true
}
],
"cvssMetricV31": [
{
"cvssData": {
"attackComplexity": "LOW",
"attackVector": "NETWORK",
"availabilityImpact": "LOW",
"baseScore": 8.9,
"baseSeverity": "HIGH",
"confidentialityImpact": "HIGH",
"integrityImpact": "HIGH",
"privilegesRequired": "LOW",
"scope": "CHANGED",
"userInteraction": "REQUIRED",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:L/UI:R/S:C/C:H/I:H/A:L",
"version": "3.1"
},
"exploitabilityScore": 2.3,
"impactScore": 6.0,
"source": "cve@mitre.org",
"type": "Secondary"
},
{
"cvssData": {
"attackComplexity": "LOW",
"attackVector": "NETWORK",
"availabilityImpact": "LOW",
"baseScore": 8.9,
"baseSeverity": "HIGH",
"confidentialityImpact": "HIGH",
"integrityImpact": "HIGH",
"privilegesRequired": "LOW",
"scope": "CHANGED",
"userInteraction": "REQUIRED",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:L/UI:R/S:C/C:H/I:H/A:L",
"version": "3.1"
},
"exploitabilityScore": 2.3,
"impactScore": 6.0,
"source": "nvd@nist.gov",
"type": "Primary"
}
]
},
"published": "2020-08-29T20:15:16.353",
"references": [
{
"source": "cve@mitre.org",
"tags": [
"Vendor Advisory"
],
"url": "https://stiltsoft.atlassian.net/browse/VD-2"
},
{
"source": "af854a3a-2127-422b-91ae-364da2661108",
"tags": [
"Vendor Advisory"
],
"url": "https://stiltsoft.atlassian.net/browse/VD-2"
}
],
"sourceIdentifier": "cve@mitre.org",
"vulnStatus": "Modified",
"weaknesses": [
{
"description": [
{
"lang": "en",
"value": "CWE-79"
}
],
"source": "nvd@nist.gov",
"type": "Primary"
}
]
}
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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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