Vulnerability-Lookup

CNVD-2020-01159

Vulnerability from cnvd - Published: 2020-01-08

Title

Intelbras IWR 3000N拒绝服务漏洞

Description

Intelbras IWR 3000N是波兰Intelbras公司的一款无线路由器。 Intelbras IWR 3000N 1.8.7版本中存在安全漏洞。攻击者可借助畸形的登录请求利用该漏洞造成拒绝服务（重启）。

Severity

高

Formal description

目前厂商尚未提供相关漏洞补丁，请关注厂商主页随时更新： https://www.intelbras.com

Reference

https://nvd.nist.gov/vuln/detail/CVE-2019-19996

Impacted products

Name
Intelbras IWR 3000N 1.8.7

Show details on source website

JSON

To clipboard

{
  "cves": {
    "cve": {
      "cveNumber": "CVE-2019-19996"
    }
  },
  "description": "Intelbras IWR 3000N\u662f\u6ce2\u5170Intelbras\u516c\u53f8\u7684\u4e00\u6b3e\u65e0\u7ebf\u8def\u7531\u5668\u3002\n\nIntelbras IWR 3000N 1.8.7\u7248\u672c\u4e2d\u5b58\u5728\u5b89\u5168\u6f0f\u6d1e\u3002\u653b\u51fb\u8005\u53ef\u501f\u52a9\u7578\u5f62\u7684\u767b\u5f55\u8bf7\u6c42\u5229\u7528\u8be5\u6f0f\u6d1e\u9020\u6210\u62d2\u7edd\u670d\u52a1\uff08\u91cd\u542f\uff09\u3002",
  "formalWay": "\u76ee\u524d\u5382\u5546\u5c1a\u672a\u63d0\u4f9b\u76f8\u5173\u6f0f\u6d1e\u8865\u4e01\uff0c\u8bf7\u5173\u6ce8\u5382\u5546\u4e3b\u9875\u968f\u65f6\u66f4\u65b0\uff1a\r\nhttps://www.intelbras.com",
  "isEvent": "\u901a\u7528\u8f6f\u786c\u4ef6\u6f0f\u6d1e",
  "number": "CNVD-2020-01159",
  "openTime": "2020-01-08",
  "products": {
    "product": "Intelbras IWR 3000N 1.8.7"
  },
  "referenceLink": "https://nvd.nist.gov/vuln/detail/CVE-2019-19996",
  "serverity": "\u9ad8",
  "submitTime": "2019-12-27",
  "title": "Intelbras IWR 3000N\u62d2\u7edd\u670d\u52a1\u6f0f\u6d1e"
}

CVE-2019-19996 (GCVE-0-2019-19996)

Vulnerability from cvelistv5 – Published: 2019-12-26 17:34 – Updated: 2024-08-05 02:32

Summary

An issue was discovered on Intelbras IWR 3000N 1.8.7 devices. A malformed login request allows remote attackers to cause a denial of service (reboot), as demonstrated by JSON misparsing of the \""} string to v1/system/login.

Severity

No CVSS data available.

CWE

Assigner

mitre mitre root

References

1 reference

URL	Tags
https://medium.com/%40rsantos_14778/dos-cve-2019-…	x_refsource_MISC

Date Public

2019-12-26 00:00

Show details on NVD website

JSON

To clipboard

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            "url": "https://medium.com/%40rsantos_14778/dos-cve-2019-19996-5ad1be772179"
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      "descriptions": [
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                "product": {
                  "product_data": [
                    {
                      "product_name": "n/a",
                      "version": {
                        "version_data": [
                          {
                            "version_value": "n/a"
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  "dataType": "CVE_RECORD",
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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.

Detection rules are retrieved from Rulezet.

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.

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.