CVE-2018-12122 (GCVE-0-2018-12122)
Vulnerability from cvelistv5 – Published: 2018-11-28 17:00 – Updated: 2024-12-13 13:09
VLAI
EPSS
VEX
Summary
Node.js: All versions prior to Node.js 6.15.0, 8.14.0, 10.14.0 and 11.3.0: Slowloris HTTP Denial of Service: An attacker can cause a Denial of Service (DoS) by sending headers very slowly keeping HTTP or HTTPS connections and associated resources alive for a long period of time.
Severity
7.5 (High)
CWE
- CWE-400 - Uncontrolled Resource Consumption / Denial of Service
Assigner
References
5 references
| URL | Tags |
|---|---|
| https://nodejs.org/en/blog/vulnerability/november… | x_refsource_CONFIRM |
| http://www.securityfocus.com/bid/106043 | vdb-entryx_refsource_BID |
| https://access.redhat.com/errata/RHSA-2019:1821 | vendor-advisoryx_refsource_REDHAT |
| https://security.gentoo.org/glsa/202003-48 | vendor-advisoryx_refsource_GENTOO |
| https://security.netapp.com/advisory/ntap-2024121… |
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| The Node.js Project | Node.js |
Affected:
All versions prior to Node.js 6.15.0, 8.14.0, 10.14.0 and 11.3.0
|
guessed |
Date Public
2018-11-28 00:00
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"initial_release_date": "2018-11-27T00:00:00+00:00",
"product_status:fixed": "206",
"product_status:known_affected": "40",
"product_status:known_not_affected": "15",
"source": "Red Hat CSAF VEX",
"status": "final",
"title": "nodejs: Slowloris HTTP Denial of Service",
"url": "https://security.access.redhat.com/data/csaf/v2/vex/2018/cve-2018-12122.json",
"version": "3"
},
"suse_vex": {
"aggregate_severity": "moderate",
"current_release_date": "2026-08-31T08:43:55Z",
"cve": "CVE-2018-12122",
"id": "CVE-2018-12122",
"initial_release_date": "2023-02-15T04:26:55Z",
"product_status:known_affected": "25",
"product_status:known_not_affected": "199",
"product_status:recommended": "295",
"source": "SUSE CSAF VEX",
"status": "interim",
"title": "SUSE CVE CVE-2018-12122",
"url": "https://ftp.suse.com/pub/projects/security/csaf-vex/cve-2018-12122.json",
"version": "16"
}
}
}
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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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