CVE-2021-22918 (GCVE-0-2021-22918)
Vulnerability from cvelistv5 – Published: 2021-07-12 00:00 – Updated: 2025-04-30 22:24
VLAI
EPSS
VEX
Summary
Node.js before 16.4.1, 14.17.2, 12.22.2 is vulnerable to an out-of-bounds read when uv__idna_toascii() is used to convert strings to ASCII. The pointer p is read and increased without checking whether it is beyond pe, with the latter holding a pointer to the end of the buffer. This can lead to information disclosures or crashes. This function can be triggered via uv_getaddrinfo().
Severity
5.3 (Medium)
CWE
- CWE-125 - Out-of-bounds Read (CWE-125)
Assigner
References
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.* (semver) Affected: 11.0 , < 11.* (semver) Affected: 12.0 , < 12.22.2 (semver) Affected: 13.0 , < 13.* (semver) Affected: 14.0 , < 14.17.2 (semver) Affected: 15.0 , < 15.* (semver) Affected: 16.0 , < 16.4.1 (semver) |
guessed |
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Nomenclature
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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.