CVE-2018-17100 (GCVE-0-2018-17100)
Vulnerability from cvelistv5 – Published: 2018-09-16 21:00 – Updated: 2024-08-05 10:39
VLAI
EPSS
VEX
Summary
An issue was discovered in LibTIFF 4.0.9. There is a int32 overflow in multiply_ms in tools/ppm2tiff.c, which can cause a denial of service (crash) or possibly have unspecified other impact via a crafted image file.
Severity
8.8 (High)
CWE
- n/a
Assigner
References
7 references
| URL | Tags |
|---|---|
| https://usn.ubuntu.com/3864-1/ | vendor-advisoryx_refsource_UBUNTU |
| https://lists.debian.org/debian-lts-announce/2018… | mailing-listx_refsource_MLIST |
| http://bugzilla.maptools.org/show_bug.cgi?id=2810 | x_refsource_MISC |
| https://gitlab.com/libtiff/libtiff/merge_requests… | x_refsource_MISC |
| https://usn.ubuntu.com/3906-2/ | vendor-advisoryx_refsource_UBUNTU |
| https://access.redhat.com/errata/RHSA-2019:2053 | vendor-advisoryx_refsource_REDHAT |
| https://www.debian.org/security/2020/dsa-4670 | vendor-advisoryx_refsource_DEBIAN |
Date Public
2018-09-16 00:00
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}
},
"redhat_vex": {
"aggregate_severity": "Low",
"current_release_date": "2025-11-21T16:59:51+00:00",
"cve": "CVE-2018-17100",
"id": "CVE-2018-17100",
"initial_release_date": "2018-09-16T00:00:00+00:00",
"product_status:fixed": "264",
"product_status:known_affected": "12",
"product_status:known_not_affected": "4",
"source": "Red Hat CSAF VEX",
"status": "final",
"title": "libtiff: Integer overflow in multiply_ms in tools/ppm2tiff.c",
"url": "https://security.access.redhat.com/data/csaf/v2/vex/2018/cve-2018-17100.json",
"version": "3"
},
"suse_vex": {
"aggregate_severity": "low",
"current_release_date": "2026-09-02T02:40:00Z",
"cve": "CVE-2018-17100",
"id": "CVE-2018-17100",
"initial_release_date": "2023-02-15T04:23:42Z",
"product_status:known_affected": "463",
"product_status:known_not_affected": "1",
"product_status:recommended": "287",
"source": "SUSE CSAF VEX",
"status": "interim",
"title": "SUSE CVE CVE-2018-17100",
"url": "https://ftp.suse.com/pub/projects/security/csaf-vex/cve-2018-17100.json",
"version": "51"
}
}
}
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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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