FKIE_CVE-2018-1126
Vulnerability from fkie_nvd - Published: 2018-05-23 13:29 - Updated: 2026-06-17 01:50
Severity
4.8 (Medium) - CVSS:3.0/AV:L/AC:L/PR:L/UI:R/S:U/C:L/I:L/A:L
9.8 (Critical) - CVSS:3.0/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H
9.8 (Critical) - CVSS:3.0/
Summary
procps-ng before version 3.3.15 is vulnerable to an incorrect integer size in proc/alloc.* leading to truncation/integer overflow issues. This flaw is related to CVE-2018-1124.
References
Impacted products
| Vendor | Product | Version | |
|---|---|---|---|
| procps-ng_project | procps-ng | * | |
| canonical | ubuntu_linux | 14.04 | |
| canonical | ubuntu_linux | 16.04 | |
| canonical | ubuntu_linux | 17.10 | |
| canonical | ubuntu_linux | 18.04 | |
| debian | debian_linux | 7.0 | |
| debian | debian_linux | 8.0 | |
| debian | debian_linux | 9.0 | |
| redhat | enterprise_linux | 7.0 | |
| redhat | enterprise_linux_desktop | 7.0 | |
| redhat | enterprise_linux_server | 7.0 | |
| redhat | enterprise_linux_server | 7.5 | |
| redhat | enterprise_linux_server_aus | 6.6 | |
| redhat | enterprise_linux_server_tus | 6.6 | |
| redhat | enterprise_linux_workstation | 7.0 | |
| schneider-electric | struxureware_data_center_expert | * |
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"vendor": "[UNKNOWN]",
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"cveTags": [],
"descriptions": [
{
"lang": "en",
"value": "procps-ng before version 3.3.15 is vulnerable to an incorrect integer size in proc/alloc.* leading to truncation/integer overflow issues. This flaw is related to CVE-2018-1124."
},
{
"lang": "es",
"value": "procps-ng en versiones anteriores a la 3.3.15 es vulnerable a un tama\u00f1o de entero incorrecto en proc/alloc.* que conduce a problemas de truncado/desbordamiento de enteros. Este error est\u00e1 relacionado con CVE-2018-1124."
}
],
"id": "CVE-2018-1126",
"lastModified": "2026-06-17T01:50:32.410",
"metrics": {
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"baseSeverity": "HIGH",
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"accessVector": "NETWORK",
"authentication": "NONE",
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"version": "2.0"
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"impactScore": 6.4,
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"obtainOtherPrivilege": false,
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"timestamp": "2025-12-17T20:45:38.465029Z",
"version": "2.0.3"
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"published": "2018-05-23T13:29:00.343",
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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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