GSD-2016-6809
Vulnerability from gsd - Updated: 2023-12-13 01:21Details
Apache Tika before 1.14 allows Java code execution for serialized objects embedded in MATLAB files. The issue exists because Tika invokes JMatIO to do native deserialization.
Aliases
Aliases
{
"GSD": {
"alias": "CVE-2016-6809",
"description": "Apache Tika before 1.14 allows Java code execution for serialized objects embedded in MATLAB files. The issue exists because Tika invokes JMatIO to do native deserialization.",
"id": "GSD-2016-6809"
},
"gsd": {
"metadata": {
"exploitCode": "unknown",
"remediation": "unknown",
"reportConfidence": "confirmed",
"type": "vulnerability"
},
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"details": "Apache Tika before 1.14 allows Java code execution for serialized objects embedded in MATLAB files. The issue exists because Tika invokes JMatIO to do native deserialization.",
"id": "GSD-2016-6809",
"modified": "2023-12-13T01:21:23.327531Z",
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"cwe_ids": [
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"CWE-78",
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"date": "2021-09-14",
"description": "Apache Tika before 1.14 allows Java code execution for serialized objects embedded in MATLAB files. The issue exists because Tika invokes JMatIO to do native deserialization.",
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"package_slug": "maven/org.apache.tika/tika-core",
"pubdate": "2018-10-17",
"solution": "Upgrade to version 1.14 or above.",
"title": "Deserialization of Untrusted Data",
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"date": "2019-10-15",
"description": "Apache Tika allows Java code execution for serialized objects embedded in MATLAB files. The issue exists because Tika invokes `JMatIO` to do native deserialization.",
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"pubdate": "2017-04-06",
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"vectorString": "AV:N/AC:L/Au:N/C:P/I:P/A:P",
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"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",
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},
"lastModifiedDate": "2020-08-19T19:17Z",
"publishedDate": "2017-04-06T21:59Z"
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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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