GSD-2016-9909
Vulnerability from gsd - Updated: 2023-12-13 01:21Details
The serializer in html5lib before 0.99999999 might allow remote attackers to conduct cross-site scripting (XSS) attacks by leveraging mishandling of the < (less than) character in attribute values.
Aliases
Aliases
{
"GSD": {
"alias": "CVE-2016-9909",
"description": "The serializer in html5lib before 0.99999999 might allow remote attackers to conduct cross-site scripting (XSS) attacks by leveraging mishandling of the \u003c (less than) character in attribute values.",
"id": "GSD-2016-9909",
"references": [
"https://www.suse.com/security/cve/CVE-2016-9909.html",
"https://advisories.mageia.org/CVE-2016-9909.html",
"https://security.archlinux.org/CVE-2016-9909"
]
},
"gsd": {
"metadata": {
"exploitCode": "unknown",
"remediation": "unknown",
"reportConfidence": "confirmed",
"type": "vulnerability"
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"id": "GSD-2016-9909",
"modified": "2023-12-13T01:21:21.972573Z",
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"advisories": [
{
"affected_range": "\u003c=0.99999999",
"affected_versions": "All versions up to 0.99999999",
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"cwe_ids": [
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"CWE-79",
"CWE-937"
],
"date": "2017-02-23",
"description": "The serializer in html5lib might allow remote attackers to conduct cross-site scripting (XSS) attacks by leveraging mishandling of the `\u003c` (less than) character in attribute values.",
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"identifier": "CVE-2016-9909",
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"not_impacted": "All versions after 0.99999999",
"package_slug": "pypi/html5lib",
"pubdate": "2017-02-22",
"solution": "Upgrade to version 1.0 or above.",
"title": "Cross-site Scripting",
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"versionEndIncluding": "0.99999999",
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"impact": {
"baseMetricV2": {
"cvssV2": {
"accessComplexity": "MEDIUM",
"accessVector": "NETWORK",
"authentication": "NONE",
"availabilityImpact": "NONE",
"baseScore": 4.3,
"confidentialityImpact": "NONE",
"integrityImpact": "PARTIAL",
"vectorString": "AV:N/AC:M/Au:N/C:N/I:P/A:N",
"version": "2.0"
},
"exploitabilityScore": 8.6,
"impactScore": 2.9,
"obtainAllPrivilege": false,
"obtainOtherPrivilege": false,
"obtainUserPrivilege": false,
"severity": "MEDIUM",
"userInteractionRequired": true
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"attackVector": "NETWORK",
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"baseScore": 6.1,
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"confidentialityImpact": "LOW",
"integrityImpact": "LOW",
"privilegesRequired": "NONE",
"scope": "CHANGED",
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},
"lastModifiedDate": "2017-02-23T18:56Z",
"publishedDate": "2017-02-22T16:59Z"
}
}
}
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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.
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- 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
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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.
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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