JVNDB-2016-000160
Vulnerability from jvndb - Published: 2016-09-20 06:19 - Updated:2017-11-27 09:01
Severity
Summary
Money Forward Apps for Android vulnerable in the WebView class
Details
Money Forward Apps for Android contain a vulnerability in the WebView class.
Kenta Suefusa, Akinori Konishi and Tomonori Shiomi of Sprout Inc. reported this vulnerability to IPA.
JPCERT/CC coordinated with the developer under Information Security Early Warning Partnership.
References
Impacted products
| Vendor | Product | |
|---|---|---|
{
"@rdf:about": "https://jvndb.jvn.jp/en/contents/2016/JVNDB-2016-000160.html",
"dc:date": "2017-11-27T18:01+09:00",
"dcterms:issued": "2016-09-20T15:19+09:00",
"dcterms:modified": "2017-11-27T18:01+09:00",
"description": "Money Forward Apps for Android contain a vulnerability in the WebView class.\r\n\r\nKenta Suefusa, Akinori Konishi and Tomonori Shiomi of Sprout Inc. reported this vulnerability to IPA.\r\nJPCERT/CC coordinated with the developer under Information Security Early Warning Partnership.",
"link": "https://jvndb.jvn.jp/en/contents/2016/JVNDB-2016-000160.html",
"sec:cpe": [
{
"#text": "cpe:/a:money_forward_inc:money_forward",
"@product": "Money Forward",
"@vendor": "Money Forward, Inc.",
"@version": "2.2"
},
{
"#text": "cpe:/a:sourcenext:money_forward",
"@product": "Money Forward",
"@vendor": "SOURCENEXT CORPORATION",
"@version": "2.2"
}
],
"sec:cvss": [
{
"@score": "4.0",
"@severity": "Medium",
"@type": "Base",
"@vector": "AV:N/AC:H/Au:N/C:P/I:P/A:N",
"@version": "2.0"
},
{
"@score": "4.4",
"@severity": "Medium",
"@type": "Base",
"@vector": "CVSS:3.0/AV:L/AC:L/PR:N/UI:R/S:U/C:L/I:L/A:N",
"@version": "3.0"
}
],
"sec:identifier": "JVNDB-2016-000160",
"sec:references": [
{
"#text": "http://jvn.jp/en/jp/JVN61297210/index.html",
"@id": "JVN#61297210",
"@source": "JVN"
},
{
"#text": "https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2016-4839",
"@id": "CVE-2016-4839",
"@source": "CVE"
},
{
"#text": "https://nvd.nist.gov/vuln/detail/CVE-2016-4839",
"@id": "CVE-2016-4839",
"@source": "NVD"
},
{
"#text": "https://www.ipa.go.jp/en/security/vulnerabilities/cwe.html",
"@id": "CWE-Other",
"@title": "No Mapping(CWE-Other)"
}
],
"title": "Money Forward Apps for Android vulnerable in the WebView class"
}
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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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