GHSA-234F-WM58-6QQV
Vulnerability from github – Published: 2022-05-01 07:37 – Updated: 2022-05-01 07:37
VLAI
Details
Directory traversal vulnerability in SAP Internet Graphics Service (IGS) 6.40 Patchlevel 16 and earlier, and 7.00 Patchlevel 6 and earlier, allows remote attackers to delete arbitrary files via directory traversal sequences in an HTTP request. NOTE: This information is based upon an initial disclosure. Details will be updated after the grace period has ended. This issue is different from CVE-2006-4133 and CVE-2006-4134.
{
"affected": [],
"aliases": [
"CVE-2006-6345"
],
"database_specific": {
"cwe_ids": [],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2006-12-07T01:28:00Z",
"severity": "HIGH"
},
"details": "Directory traversal vulnerability in SAP Internet Graphics Service (IGS) 6.40 Patchlevel 16 and earlier, and 7.00 Patchlevel 6 and earlier, allows remote attackers to delete arbitrary files via directory traversal sequences in an HTTP request. NOTE: This information is based upon an initial disclosure. Details will be updated after the grace period has ended. This issue is different from CVE-2006-4133 and CVE-2006-4134.",
"id": "GHSA-234f-wm58-6qqv",
"modified": "2022-05-01T07:37:14Z",
"published": "2022-05-01T07:37:14Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2006-6345"
},
{
"type": "WEB",
"url": "https://exchange.xforce.ibmcloud.com/vulnerabilities/30765"
},
{
"type": "WEB",
"url": "http://secunia.com/advisories/23262"
},
{
"type": "WEB",
"url": "http://securityreason.com/securityalert/1986"
},
{
"type": "WEB",
"url": "http://securitytracker.com/id?1017342"
},
{
"type": "WEB",
"url": "http://www.cybsec.com/vuln/CYBSEC-Security_Pre-Advisory_SAP_IGS_Remote_Arbitrary_File_Removal.pdf"
},
{
"type": "WEB",
"url": "http://www.securityfocus.com/archive/1/453561/100/0/threaded"
},
{
"type": "WEB",
"url": "http://www.securityfocus.com/bid/21449"
},
{
"type": "WEB",
"url": "http://www.vupen.com/english/advisories/2006/4863"
}
],
"schema_version": "1.4.0",
"severity": []
}
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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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