GSD-2016-9000
Vulnerability from gsd - Updated: 2023-12-13 01:21Details
IBM InfoSphere DataStage is vulnerable to cross-frame scripting, caused by insufficient HTML iframe protection. A remote attacker could exploit this vulnerability using a specially-crafted URL to navigate to a web page the attacker controls. An attacker could use this vulnerability to conduct clickjacking or other client-side browser attacks.
Aliases
Aliases
{
"GSD": {
"alias": "CVE-2016-9000",
"description": "IBM InfoSphere DataStage is vulnerable to cross-frame scripting, caused by insufficient HTML iframe protection. A remote attacker could exploit this vulnerability using a specially-crafted URL to navigate to a web page the attacker controls. An attacker could use this vulnerability to conduct clickjacking or other client-side browser attacks.",
"id": "GSD-2016-9000"
},
"gsd": {
"metadata": {
"exploitCode": "unknown",
"remediation": "unknown",
"reportConfidence": "confirmed",
"type": "vulnerability"
},
"osvSchema": {
"aliases": [
"CVE-2016-9000"
],
"details": "IBM InfoSphere DataStage is vulnerable to cross-frame scripting, caused by insufficient HTML iframe protection. A remote attacker could exploit this vulnerability using a specially-crafted URL to navigate to a web page the attacker controls. An attacker could use this vulnerability to conduct clickjacking or other client-side browser attacks.",
"id": "GSD-2016-9000",
"modified": "2023-12-13T01:21:21.494775Z",
"schema_version": "1.4.0"
}
},
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"STATE": "PUBLIC"
},
"affects": {
"vendor": {
"vendor_data": [
{
"product": {
"product_data": [
{
"product_name": "InfoSphere Information Server",
"version": {
"version_data": [
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"version_value": "8.1"
},
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},
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}
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}
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},
"problemtype": {
"problemtype_data": [
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"description": [
{
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"value": "Cross-Site Scripting"
}
]
}
]
},
"references": {
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},
{
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"url": "http://www.securityfocus.com/bid/95324"
},
{
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"refsource": "CONFIRM",
"url": "http://www.ibm.com/support/docview.wss?uid=swg21995257"
}
]
}
},
"nvd.nist.gov": {
"configurations": {
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"nodes": [
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"cpe_match": [
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"cpe_name": [],
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"cpe_name": [],
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{
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}
]
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"tags": [
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},
{
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},
"impact": {
"baseMetricV2": {
"cvssV2": {
"accessComplexity": "MEDIUM",
"accessVector": "NETWORK",
"authentication": "NONE",
"availabilityImpact": "NONE",
"baseScore": 4.3,
"confidentialityImpact": "NONE",
"integrityImpact": "PARTIAL",
"vectorString": "AV:N/AC:M/Au:N/C:N/I:P/A:N",
"version": "2.0"
},
"exploitabilityScore": 8.6,
"impactScore": 2.9,
"obtainAllPrivilege": false,
"obtainOtherPrivilege": false,
"obtainUserPrivilege": false,
"severity": "MEDIUM",
"userInteractionRequired": true
},
"baseMetricV3": {
"cvssV3": {
"attackComplexity": "LOW",
"attackVector": "NETWORK",
"availabilityImpact": "NONE",
"baseScore": 6.1,
"baseSeverity": "MEDIUM",
"confidentialityImpact": "LOW",
"integrityImpact": "LOW",
"privilegesRequired": "NONE",
"scope": "CHANGED",
"userInteraction": "REQUIRED",
"vectorString": "CVSS:3.0/AV:N/AC:L/PR:N/UI:R/S:C/C:L/I:L/A:N",
"version": "3.0"
},
"exploitabilityScore": 2.8,
"impactScore": 2.7
}
},
"lastModifiedDate": "2017-07-27T01:29Z",
"publishedDate": "2017-02-01T22:59Z"
}
}
}
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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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