Vulnerability-Lookup

GHSA-9JH4-HWP3-HCGV

Vulnerability from github – Published: 2022-05-01 07:19 – Updated: 2025-04-03 04:39

Details

CRLF injection vulnerability in links.php in NX5Linx 1.0 allows remote attackers to inject arbitrary HTTP headers and conduct HTTP response splitting attacks via a CRLF sequence in the url parameter.

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{
  "affected": [],
  "aliases": [
    "CVE-2006-4505"
  ],
  "database_specific": {
    "cwe_ids": [],
    "github_reviewed": false,
    "github_reviewed_at": null,
    "nvd_published_at": "2006-08-31T22:04:00Z",
    "severity": "HIGH"
  },
  "details": "CRLF injection vulnerability in links.php in NX5Linx 1.0 allows remote attackers to inject arbitrary HTTP headers and conduct HTTP response splitting attacks via a CRLF sequence in the url parameter.",
  "id": "GHSA-9jh4-hwp3-hcgv",
  "modified": "2025-04-03T04:39:40Z",
  "published": "2022-05-01T07:19:17Z",
  "references": [
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2006-4505"
    },
    {
      "type": "WEB",
      "url": "https://exchange.xforce.ibmcloud.com/vulnerabilities/28634"
    },
    {
      "type": "WEB",
      "url": "http://secunia.com/advisories/21922"
    },
    {
      "type": "WEB",
      "url": "http://www.evuln.com/vulns/138"
    },
    {
      "type": "WEB",
      "url": "http://www.securityfocus.com/archive/1/445916/100/0/threaded"
    },
    {
      "type": "WEB",
      "url": "http://www.securityfocus.com/bid/20011"
    },
    {
      "type": "WEB",
      "url": "http://www.vupen.com/english/advisories/2006/3631"
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  "schema_version": "1.4.0",
  "severity": []
}

CVE-2006-4505 (GCVE-0-2006-4505)

Vulnerability from cvelistv5 – Published: 2006-08-31 22:00 – Updated: 2024-08-07 19:14

Summary

CRLF injection vulnerability in links.php in NX5Linx 1.0 allows remote attackers to inject arbitrary HTTP headers and conduct HTTP response splitting attacks via a CRLF sequence in the url parameter.

Severity

No CVSS data available.

CWE

Assigner

mitre CNA under the mitre root

CNA scorecard E 28/100 over 2260 records in the last 180 days details

References

6 references

URL	Tags
http://secunia.com/advisories/21922	third-party-advisoryx_refsource_SECUNIA
http://www.evuln.com/vulns/138/	x_refsource_MISC
http://www.securityfocus.com/bid/20011	vdb-entryx_refsource_BID
https://exchange.xforce.ibmcloud.com/vulnerabilit…	vdb-entryx_refsource_XF
http://www.securityfocus.com/archive/1/445916/100…	mailing-listx_refsource_BUGTRAQ
http://www.vupen.com/english/advisories/2006/3631	vdb-entryx_refsource_VUPEN

Date Public

2006-08-26 00:00

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            "url": "https://exchange.xforce.ibmcloud.com/vulnerabilities/28634"
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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.

Vulnerability timeline

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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.
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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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