GSD-2021-3575
Vulnerability from gsd - Updated: 2023-12-13 01:23Details
A heap-based buffer overflow was found in openjpeg in color.c:379:42 in sycc420_to_rgb when decompressing a crafted .j2k file. An attacker could use this to execute arbitrary code with the permissions of the application compiled against openjpeg.
Aliases
Aliases
{
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"alias": "CVE-2021-3575",
"description": "A heap-based buffer overflow was found in openjpeg in color.c:379:42 in sycc420_to_rgb when decompressing a crafted .j2k file. An attacker could use this to execute arbitrary code with the permissions of the application compiled against openjpeg.",
"id": "GSD-2021-3575",
"references": [
"https://www.suse.com/security/cve/CVE-2021-3575.html",
"https://access.redhat.com/errata/RHSA-2021:4251",
"https://advisories.mageia.org/CVE-2021-3575.html",
"https://security.archlinux.org/CVE-2021-3575",
"https://linux.oracle.com/cve/CVE-2021-3575.html"
]
},
"gsd": {
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"remediation": "unknown",
"reportConfidence": "confirmed",
"type": "vulnerability"
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"id": "GSD-2021-3575",
"modified": "2023-12-13T01:23:34.897493Z",
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"cwe_ids": [
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"CWE-937"
],
"date": "2023-02-12",
"description": "A heap-based buffer overflow was found in openjpeg in color.c:379:42 in sycc420_to_rgb when decompressing a crafted .j2k file. An attacker could use this to execute arbitrary code with the permissions of the application compiled against openjpeg.",
"fixed_versions": [
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"identifier": "CVE-2021-3575",
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"not_impacted": "All versions after 2.4.0",
"package_slug": "conan/openjpeg",
"pubdate": "2022-03-04",
"solution": "Upgrade to version 2.5.0 or above.",
"title": "Out-of-bounds Write",
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"impact": {
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"accessVector": "NETWORK",
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"availabilityImpact": "PARTIAL",
"baseScore": 6.8,
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"vectorString": "AV:N/AC:M/Au:N/C:P/I:P/A:P",
"version": "2.0"
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"obtainAllPrivilege": false,
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"severity": "MEDIUM",
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"attackVector": "LOCAL",
"availabilityImpact": "HIGH",
"baseScore": 7.8,
"baseSeverity": "HIGH",
"confidentialityImpact": "HIGH",
"integrityImpact": "HIGH",
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"scope": "UNCHANGED",
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"lastModifiedDate": "2023-02-12T23:41Z",
"publishedDate": "2022-03-04T18:15Z"
}
}
}
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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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