CVE-2021-43849 (GCVE-0-2021-43849)
Vulnerability from cvelistv5 – Published: 2021-12-23 16:20 – Updated: 2024-08-04 04:10
VLAI
EPSS
VEX
Title
DoS vulnerability
Summary
cordova-plugin-fingerprint-aio is a plugin provides a single and simple interface for accessing fingerprint APIs on both Android 6+ and iOS. In versions prior to 5.0.1 The exported activity `de.niklasmerz.cordova.biometric.BiometricActivity` can cause the app to crash. This vulnerability occurred because the activity didn't handle the case where it is requested with invalid or empty data which results in a crash. Any third party app can constantly call this activity with no permission. A 3rd party app/attacker using event listener can continually stop the app from working and make the victim unable to open it. Version 5.0.1 of the cordova-plugin-fingerprint-aio doesn't export the activity anymore and is no longer vulnerable. If you want to fix older versions change the attribute android:exported in plugin.xml to false. Please upgrade to version 5.0.1 as soon as possible.
Severity
6.2 (Medium)
CWE
- CWE-617 - Reachable Assertion
Assigner
References
3 references
| URL | Tags |
|---|---|
| https://github.com/NiklasMerz/cordova-plugin-fing… | x_refsource_CONFIRM |
| https://github.com/NiklasMerz/cordova-plugin-fing… | x_refsource_MISC |
| https://github.com/NiklasMerz/cordova-plugin-fing… | x_refsource_MISC |
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| NiklasMerz | cordova-plugin-fingerprint-aio |
Affected:
< 5.0.1
|
guessed |
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],
"sourceIdentifier": "security-advisories@github.com",
"vulnStatus": "Modified",
"weaknesses": [
{
"description": [
{
"lang": "en",
"value": "CWE-617"
}
],
"source": "security-advisories@github.com",
"type": "Secondary"
}
]
}
},
"redhat_vex": {
"current_release_date": "2026-03-27T13:21:16+00:00",
"cve": "CVE-2021-43849",
"id": "CVE-2021-43849",
"initial_release_date": "2021-01-01T00:00:00+00:00",
"product_status:known_not_affected": "1",
"source": "Red Hat CSAF VEX",
"status": "final",
"title": "DoS vulnerability",
"url": "https://security.access.redhat.com/data/csaf/v2/vex/2021/cve-2021-43849.json",
"version": "3"
}
}
}
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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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