FKIE_CVE-2017-6891
Vulnerability from fkie_nvd - Published: 2017-05-22 19:29 - Updated: 2026-06-17 01:23
Severity
Summary
Two errors in the "asn1_find_node()" function (lib/parser_aux.c) within GnuTLS libtasn1 version 4.10 can be exploited to cause a stacked-based buffer overflow by tricking a user into processing a specially crafted assignments file via the e.g. asn1Coding utility.
References
Impacted products
| Vendor | Product | Version | |
|---|---|---|---|
| gnu | libtasn1 | 4.10 | |
| debian | debian_linux | 8.0 | |
| apache | bookkeeper | 4.12.1 |
{
"affected": [
{
"affectedData": [
{
"product": "GnuTLS libtasn1",
"vendor": "Flexera Software LLC",
"versions": [
{
"status": "affected",
"version": "4.10. Other versions may also be affected."
}
]
}
],
"source": "PSIRT-CNA@flexerasoftware.com"
}
],
"configurations": [
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"vulnerable": true
}
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],
"cveTags": [],
"descriptions": [
{
"lang": "en",
"value": "Two errors in the \"asn1_find_node()\" function (lib/parser_aux.c) within GnuTLS libtasn1 version 4.10 can be exploited to cause a stacked-based buffer overflow by tricking a user into processing a specially crafted assignments file via the e.g. asn1Coding utility."
},
{
"lang": "es",
"value": "Se pueden explotar dos errores en la funci\u00f3n \\\"asn1_find_node()\\\" (lib/parser_aux.c) en GnuTLS libtasn1 versi\u00f3n 4.10 para provocar un desbordamiento de b\u00fafer basado en pila enga\u00f1ando a un usuario para que procese un archivo de asignaciones especialmente manipulado mediante la utilidad de ejemplo asn1Coding."
}
],
"id": "CVE-2017-6891",
"lastModified": "2026-06-17T01:23:17.003",
"metrics": {
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"baseSeverity": "MEDIUM",
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"accessVector": "NETWORK",
"authentication": "NONE",
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"version": "2.0"
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"source": "nvd@nist.gov",
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},
"published": "2017-05-22T19:29:00.250",
"references": [
{
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},
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},
{
"source": "PSIRT-CNA@flexerasoftware.com",
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},
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],
"sourceIdentifier": "PSIRT-CNA@flexerasoftware.com",
"vulnStatus": "Modified",
"weaknesses": [
{
"description": [
{
"lang": "en",
"value": "CWE-787"
}
],
"source": "nvd@nist.gov",
"type": "Primary"
}
]
}
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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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