FKIE_CVE-2019-10897
Vulnerability from fkie_nvd - Published: 2019-04-09 04:29 - Updated: 2026-06-17 02:11
Severity
Summary
In Wireshark 3.0.0, the IEEE 802.11 dissector could go into an infinite loop. This was addressed in epan/dissectors/packet-ieee80211.c by detecting cases in which the bit offset does not advance.
References
Impacted products
| Vendor | Product | Version | |
|---|---|---|---|
| wireshark | wireshark | 3.0.0 | |
| fedoraproject | fedora | 29 | |
| fedoraproject | fedora | 30 |
{
"affected": [
{
"affectedData": [
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"product": "n/a",
"vendor": "n/a",
"versions": [
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],
"source": "cve@mitre.org"
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],
"configurations": [
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"descriptions": [
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"value": "In Wireshark 3.0.0, the IEEE 802.11 dissector could go into an infinite loop. This was addressed in epan/dissectors/packet-ieee80211.c by detecting cases in which the bit offset does not advance."
},
{
"lang": "es",
"value": "En Wireshark 3.0.0, el disector IEEE 802.11 pod\u00eda entrar en un bucle infinito. Esto se abord\u00f3 en epan/disectores/packet-ieee80211.c mediante la detecci\u00f3n de casos en los que el desplazamiento de bits no avanza."
}
],
"id": "CVE-2019-10897",
"lastModified": "2026-06-17T02:11:50.907",
"metrics": {
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"baseSeverity": "MEDIUM",
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"accessVector": "NETWORK",
"authentication": "NONE",
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"baseScore": 5.0,
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"integrityImpact": "NONE",
"vectorString": "AV:N/AC:L/Au:N/C:N/I:N/A:P",
"version": "2.0"
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"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.0/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H",
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"published": "2019-04-09T04:29:01.187",
"references": [
{
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"url": "http://lists.opensuse.org/opensuse-security-announce/2020-03/msg00027.html"
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"url": "https://code.wireshark.org/review/gitweb?p=wireshark.git%3Ba=commit%3Bh=00d5e9e9fb377f52ab7696f25c1dbc011ef0244d"
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},
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},
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},
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],
"url": "https://www.wireshark.org/security/wnpa-sec-2019-11.html"
}
],
"sourceIdentifier": "cve@mitre.org",
"vulnStatus": "Modified",
"weaknesses": [
{
"description": [
{
"lang": "en",
"value": "CWE-835"
}
],
"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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