CVE-2021-22883 (GCVE-0-2021-22883)
Vulnerability from cvelistv5 – Published: 2021-03-03 17:38 – Updated: 2025-04-30 22:24
VLAI
EPSS
VEX
Summary
Node.js before 10.24.0, 12.21.0, 14.16.0, and 15.10.0 is vulnerable to a denial of service attack when too many connection attempts with an 'unknownProtocol' are established. This leads to a leak of file descriptors. If a file descriptor limit is configured on the system, then the server is unable to accept new connections and prevent the process also from opening, e.g. a file. If no file descriptor limit is configured, then this lead to an excessive memory usage and cause the system to run out of memory.
Severity
7.5 (High)
CWE
- CWE-400 - Denial of Service (CWE-400)
Assigner
References
10 references
| URL | Tags |
|---|---|
| https://nodejs.org/en/blog/vulnerability/february… | x_refsource_MISC |
| https://hackerone.com/reports/1043360 | x_refsource_MISC |
| https://lists.fedoraproject.org/archives/list/pac… | vendor-advisoryx_refsource_FEDORA |
| https://lists.fedoraproject.org/archives/list/pac… | vendor-advisoryx_refsource_FEDORA |
| https://lists.fedoraproject.org/archives/list/pac… | vendor-advisoryx_refsource_FEDORA |
| https://www.oracle.com/security-alerts/cpuApr2021.html | x_refsource_MISC |
| https://security.netapp.com/advisory/ntap-2021041… | x_refsource_CONFIRM |
| https://www.oracle.com//security-alerts/cpujul2021.html | x_refsource_MISC |
| https://www.oracle.com/security-alerts/cpuoct2021.html | x_refsource_MISC |
| https://cert-portal.siemens.com/productcert/pdf/s… | x_refsource_CONFIRM |
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| NodeJS | Node |
Affected:
4.0 , < 4.*
(semver)
Affected: 5.0 , < 5.* (semver) Affected: 6.0 , < 6.* (semver) Affected: 7.0 , < 7.* (semver) Affected: 8.0 , < 8.* (semver) Affected: 9.0 , < 9.* (semver) Affected: 10.0 , < 10.24.0 (semver) Affected: 11.0 , < 11.* (semver) Affected: 12.0 , < 12.21.0 (semver) Affected: 13.0 , < 13.* (semver) Affected: 14.0 , < 14.16.0 (semver) Affected: 15.0 , < 15.10.0 (semver) |
guessed |
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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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