FKIE_CVE-2024-31581
Vulnerability from fkie_nvd - Published: 2024-04-17 19:15 - Updated: 2026-06-17 07:28
Severity
Summary
FFmpeg version n6.1 was discovered to contain an improper validation of array index vulnerability in libavcodec/cbs_h266_syntax_template.c. This vulnerability allows attackers to cause undefined behavior within the application.
References
Impacted products
| Vendor | Product | Version | |
|---|---|---|---|
| ffmpeg | ffmpeg | 6.1 | |
| fedoraproject | fedora | 38 | |
| fedoraproject | fedora | 39 | |
| fedoraproject | fedora | 40 |
{
"affected": [
{
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"vendor": "n/a",
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"source": "cve@mitre.org"
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{
"affectedData": [
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"cpes": [
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"product": "ffmpeg",
"vendor": "ffmpeg",
"versions": [
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"version": "6.1"
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"source": "134c704f-9b21-4f2e-91b3-4a467353bcc0"
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"configurations": [
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"cveTags": [],
"descriptions": [
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"lang": "en",
"value": "FFmpeg version n6.1 was discovered to contain an improper validation of array index vulnerability in libavcodec/cbs_h266_syntax_template.c. This vulnerability allows attackers to cause undefined behavior within the application."
},
{
"lang": "es",
"value": "Se descubri\u00f3 que la versi\u00f3n n6.1 de FFmpeg conten\u00eda una validaci\u00f3n incorrecta de la vulnerabilidad del \u00edndice de matriz en libavcodec/cbs_h266_syntax_template.c. Esta vulnerabilidad permite a los atacantes provocar un comportamiento indefinido dentro de la aplicaci\u00f3n."
}
],
"id": "CVE-2024-31581",
"lastModified": "2026-06-17T07:28:36.347",
"metrics": {
"cvssMetricV31": [
{
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"attackComplexity": "LOW",
"attackVector": "NETWORK",
"availabilityImpact": "HIGH",
"baseScore": 9.8,
"baseSeverity": "CRITICAL",
"confidentialityImpact": "HIGH",
"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,
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"role": "CISA Coordinator",
"timestamp": "2024-07-29T19:25:09.747019Z",
"version": "2.0.3"
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"published": "2024-04-17T19:15:07.840",
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"sourceIdentifier": "cve@mitre.org",
"vulnStatus": "Modified",
"weaknesses": [
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}
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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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