FKIE_CVE-2016-3074
Vulnerability from fkie_nvd - Published: 2016-04-26 14:59 - Updated: 2026-06-17 00:44
Severity
Summary
Integer signedness error in GD Graphics Library 2.1.1 (aka libgd or libgd2) allows remote attackers to cause a denial of service (crash) or potentially execute arbitrary code via crafted compressed gd2 data, which triggers a heap-based buffer overflow.
References
Impacted products
| Vendor | Product | Version | |
|---|---|---|---|
| libgd | libgd | 2.1.1 | |
| debian | debian_linux | 7.0 | |
| debian | debian_linux | 8.0 | |
| fedoraproject | fedora | 23 | |
| fedoraproject | fedora | 24 | |
| canonical | ubuntu_linux | 12.04 | |
| canonical | ubuntu_linux | 14.04 | |
| canonical | ubuntu_linux | 15.10 | |
| canonical | ubuntu_linux | 16.04 | |
| opensuse | opensuse | 13.2 | |
| php | php | * | |
| php | php | * | |
| php | php | * |
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"cveTags": [],
"descriptions": [
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"lang": "en",
"value": "Integer signedness error in GD Graphics Library 2.1.1 (aka libgd or libgd2) allows remote attackers to cause a denial of service (crash) or potentially execute arbitrary code via crafted compressed gd2 data, which triggers a heap-based buffer overflow."
},
{
"lang": "es",
"value": "Error de entero sin signo en GD Graphics Library 2.1.1 (tambi\u00e9n conocida como libgd o libgd2) permite a atacantes remotos provocar una denegaci\u00f3n de servicio (ca\u00edda) o potencialmente ejecutar c\u00f3digo arbitrario a trav\u00e9s de datos gd2 comprimidos manipulados, lo que desencadena un desbordamiento de buffer basado en memoria din\u00e1mica."
}
],
"id": "CVE-2016-3074",
"lastModified": "2026-06-17T00:44:55.123",
"metrics": {
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"baseSeverity": "HIGH",
"cvssData": {
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"accessVector": "NETWORK",
"authentication": "NONE",
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"vectorString": "AV:N/AC:L/Au:N/C:P/I:P/A:P",
"version": "2.0"
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"impactScore": 6.4,
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"integrityImpact": "HIGH",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H",
"version": "3.1"
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"impactScore": 5.9,
"source": "nvd@nist.gov",
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"published": "2016-04-26T14:59:01.207",
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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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