GHSA-9H6G-6MXG-VVP4
Vulnerability from github – Published: 2021-04-19 14:47 – Updated: 2021-10-08 21:24
VLAI
Summary
Timing side channel vulnerability in endpoint request handler in Vaadin 15-19
Details
Non-constant-time comparison of CSRF tokens in endpoint request handler in com.vaadin:flow-server versions 3.0.0 through 5.0.3 (Vaadin 15.0.0 through 18.0.6), and com.vaadin:fusion-endpoint version 6.0.0 (Vaadin 19.0.0) allows attacker to guess a security token for Fusion endpoints via timing attack.
- https://vaadin.com/security/cve-2021-31406
Severity
4.0 (Medium)
{
"affected": [
{
"package": {
"ecosystem": "Maven",
"name": "com.vaadin:vaadin-bom"
},
"ranges": [
{
"events": [
{
"introduced": "19.0.0"
},
{
"fixed": "19.0.1"
}
],
"type": "ECOSYSTEM"
}
],
"versions": [
"19.0.0"
]
},
{
"package": {
"ecosystem": "Maven",
"name": "com.vaadin:vaadin-bom"
},
"ranges": [
{
"events": [
{
"introduced": "15.0.0"
},
{
"fixed": "18.0.7"
}
],
"type": "ECOSYSTEM"
}
]
}
],
"aliases": [],
"database_specific": {
"cwe_ids": [
"CWE-208"
],
"github_reviewed": true,
"github_reviewed_at": "2021-04-16T23:13:06Z",
"nvd_published_at": null,
"severity": "MODERATE"
},
"details": "Non-constant-time comparison of CSRF tokens in endpoint request handler in `com.vaadin:flow-server` versions 3.0.0 through 5.0.3 (Vaadin 15.0.0 through 18.0.6), and com.vaadin:fusion-endpoint version 6.0.0 (Vaadin 19.0.0) allows attacker to guess a security token for Fusion endpoints via timing attack.\n\n- https://vaadin.com/security/cve-2021-31406",
"id": "GHSA-9h6g-6mxg-vvp4",
"modified": "2021-10-08T21:24:35Z",
"published": "2021-04-19T14:47:18Z",
"references": [
{
"type": "WEB",
"url": "https://github.com/vaadin/platform/security/advisories/GHSA-9h6g-6mxg-vvp4"
},
{
"type": "WEB",
"url": "https://github.com/vaadin/platform"
},
{
"type": "WEB",
"url": "https://vaadin.com/security/cve-2021-31406"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.1/AV:L/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N",
"type": "CVSS_V3"
}
],
"summary": "Timing side channel vulnerability in endpoint request handler in Vaadin 15-19"
}
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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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