CVE-2022-23515 (GCVE-0-2022-23515)
Vulnerability from cvelistv5 – Published: 2022-12-14 13:23 – Updated: 2025-11-03 21:45
VLAI
EPSS
VEX
Title
Improper neutralization of data URIs may allow XSS in Loofah
Summary
Loofah is a general library for manipulating and transforming HTML/XML documents and fragments, built on top of Nokogiri. Loofah >= 2.1.0, < 2.19.1 is vulnerable to cross-site scripting via the image/svg+xml media type in data URIs. This issue is patched in version 2.19.1.
Severity
6.1 (Medium)
CWE
- CWE-79 - Improper Neutralization of Input During Web Page Generation ('Cross-site Scripting')
Assigner
References
5 references
| URL | Tags |
|---|---|
| https://github.com/flavorjones/loofah/security/ad… | x_refsource_CONFIRM |
| https://github.com/flavorjones/loofah/issues/101 | x_refsource_MISC |
| https://hackerone.com/reports/1694173 | x_refsource_MISC |
| https://lists.debian.org/debian-lts-announce/2023… | |
| https://lists.debian.org/debian-lts-announce/2024… |
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| flavorjones | loofah |
Affected:
>= 2.1.0, < 2.19.1
|
guessed |
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"redhat_vex": {
"aggregate_severity": "Moderate",
"current_release_date": "2026-04-01T13:14:06+00:00",
"cve": "CVE-2022-23515",
"id": "CVE-2022-23515",
"initial_release_date": "2022-12-13T00:00:00+00:00",
"product_status:fixed": "2",
"product_status:known_affected": "7",
"product_status:known_not_affected": "1971",
"source": "Red Hat CSAF VEX",
"status": "final",
"title": "rubygem-loofah: Improper neutralization of data URIs leading to Cross Site Scripting",
"url": "https://security.access.redhat.com/data/csaf/v2/vex/2022/cve-2022-23515.json",
"version": "3"
},
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"current_release_date": "2026-09-09T00:37:50Z",
"cve": "CVE-2022-23515",
"id": "CVE-2022-23515",
"initial_release_date": "2023-02-15T03:28:17Z",
"product_status:known_affected": "396",
"product_status:known_not_affected": "4",
"product_status:recommended": "21",
"source": "SUSE CSAF VEX",
"status": "interim",
"title": "SUSE CVE CVE-2022-23515",
"url": "https://ftp.suse.com/pub/projects/security/csaf-vex/cve-2022-23515.json",
"version": "33"
}
}
}
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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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