CVE-2022-25854 (GCVE-0-2022-25854)
Vulnerability from cvelistv5 – Published: 2022-04-29 20:00 – Updated: 2024-09-17 04:19
VLAI
EPSS
VEX
Title
Cross-site Scripting (XSS)
Summary
This affects the package @yaireo/tagify before 4.9.8. The package is used for rendering UI components inside the input or text fields, and an attacker can pass a malicious placeholder value to it to fire the XSS payload.
Severity
5.4 (Medium)
CWE
- Cross-site Scripting (XSS)
Assigner
References
5 references
| URL | Tags |
|---|---|
| https://snyk.io/vuln/SNYK-JS-YAIREOTAGIFY-2404358 | x_refsource_MISC |
| https://github.com/yairEO/tagify/issues/988 | x_refsource_MISC |
| https://github.com/yairEO/tagify/releases/tag/v4.9.8 | x_refsource_MISC |
| https://github.com/yairEO/tagify/commit/198c0451f… | x_refsource_MISC |
| https://bsg.tech/blog/cve-2022-25854-stored-xss-i… | x_refsource_MISC |
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| n/a | @yaireo/tagify |
Affected:
unspecified , < 4.9.8
(custom)
|
Date Public
2022-04-29 00:00
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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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