CVE-2017-14173 (GCVE-0-2017-14173)
Vulnerability from cvelistv5 – Published: 2017-09-07 06:00 – Updated: 2024-08-05 19:20
VLAI
EPSS
VEX
Summary
In the function ReadTXTImage() in coders/txt.c in ImageMagick 7.0.6-10, an integer overflow might occur for the addition operation "GetQuantumRange(depth)+1" when "depth" is large, producing a smaller value than expected. As a result, an infinite loop would occur for a crafted TXT file that claims a very large "max_value" value.
Severity
6.5 (Medium)
CWE
- n/a
Assigner
References
6 references
| URL | Tags |
|---|---|
| https://usn.ubuntu.com/3681-1/ | vendor-advisoryx_refsource_UBUNTU |
| https://security.gentoo.org/glsa/201711-07 | vendor-advisoryx_refsource_GENTOO |
| https://github.com/ImageMagick/ImageMagick/issues/713 | x_refsource_CONFIRM |
| https://lists.debian.org/debian-lts-announce/2019… | mailing-listx_refsource_MLIST |
| https://lists.debian.org/debian-lts-announce/2020… | mailing-listx_refsource_MLIST |
| https://github.com/ImageMagick/ImageMagick/commit… | x_refsource_CONFIRM |
Date Public
2017-09-07 00:00
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"value": "CWE-190"
},
{
"lang": "en",
"value": "CWE-835"
}
],
"source": "nvd@nist.gov",
"type": "Primary"
}
]
}
},
"redhat_vex": {
"aggregate_severity": "Low",
"current_release_date": "2026-05-13T02:20:56+00:00",
"cve": "CVE-2017-14173",
"id": "CVE-2017-14173",
"initial_release_date": "2017-09-07T00:00:00+00:00",
"product_status:known_affected": "15",
"source": "Red Hat CSAF VEX",
"status": "final",
"title": "ImageMagick: Integer overflow in the function ReadTXTImage()",
"url": "https://security.access.redhat.com/data/csaf/v2/vex/2017/cve-2017-14173.json",
"version": "3"
},
"suse_vex": {
"aggregate_severity": "low",
"current_release_date": "2025-10-07T10:18:10Z",
"cve": "CVE-2017-14173",
"id": "CVE-2017-14173",
"initial_release_date": "2023-02-15T04:39:44Z",
"product_status:known_affected": "75",
"product_status:recommended": "166",
"source": "SUSE CSAF VEX",
"status": "interim",
"title": "SUSE CVE CVE-2017-14173",
"url": "https://ftp.suse.com/pub/projects/security/csaf-vex/cve-2017-14173.json",
"version": "13"
}
}
}
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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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