Vulnerability-Lookup

CNVD-2021-88187

Vulnerability from cnvd - Published: 2021-11-16

Title

IBM QRadar Network Security跨站脚本漏洞（CNVD-2021-88187）

Description

IBM QRadar Network Security是美国IBM公司的一个网络安全管理器。用于提供对网络上的活动和用户的更好的可见性和控制，同时使用深度数据包检查、启发式和基于行为的分析来检测和预防高级威胁。 IBM QRadar Network Security 5.4.0和5.5.0版本中存在跨站脚本漏洞。该漏洞源于IBM QRadar网络安全易受跨站点脚本攻击。攻击者可利用该漏洞在Web UI中嵌入任意JavaScript代码，从而改变预期的功能，导致可信会话中的凭证泄露。

Severity

低

Patch Name

IBM QRadar Network Security跨站脚本漏洞（CNVD-2021-88187）的补丁

Patch Description

Formal description

目前厂商已经发布了升级补丁以修复这个安全问题，请到厂商的主页下载： https://www.ibm.com/support/pages/node/6514403

Reference

https://www.ibm.com/support/pages/node/6514403

Impacted products

Name
['IBM IBM QRadar Network Security 5.4.0', 'IBM IBM QRadar Network Security 5.5.0']

Show details on source website

JSON

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  "cves": {
    "cve": {
      "cveNumber": "CVE-2020-4153",
      "cveUrl": "https://nvd.nist.gov/vuln/detail/CVE-2020-4153"
    }
  },
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  "number": "CNVD-2021-88187",
  "openTime": "2021-11-16",
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  "patchName": "IBM QRadar Network Security\u8de8\u7ad9\u811a\u672c\u6f0f\u6d1e\uff08CNVD-2021-88187\uff09\u7684\u8865\u4e01",
  "products": {
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      "IBM IBM QRadar Network Security 5.5.0"
    ]
  },
  "referenceLink": "https://www.ibm.com/support/pages/node/6514403",
  "serverity": "\u4f4e",
  "submitTime": "2021-11-12",
  "title": "IBM QRadar Network Security\u8de8\u7ad9\u811a\u672c\u6f0f\u6d1e\uff08CNVD-2021-88187\uff09"
}

CVE-2020-4153 (GCVE-0-2020-4153)

Vulnerability from cvelistv5 – Published: 2021-11-08 16:50 – Updated: 2024-09-16 17:03

Summary

IBM QRadar Network Security 5.4.0 and 5.5.0 is vulnerable to cross-site scripting. This vulnerability allows users to embed arbitrary JavaScript code in the Web UI thus altering the intended functionality potentially leading to credentials disclosure within a trusted session. IBM X-Force ID: 174269.

Severity

5.4 (Medium)


                        
                          CVSS:3.0/A:N/S:C/AC:L/C:L/I:L/AV:N/PR:L/UI:R/RL:O/E:H/RC:C

CWE

Cross-Site Scripting

Assigner

ibm mitre root

References

2 references

URL	Tags
https://www.ibm.com/support/pages/node/6514403	x_refsource_CONFIRM
https://exchange.xforce.ibmcloud.com/vulnerabilit…	vdb-entryx_refsource_XF

Impacted products

1 product

Vendor	Product	Version
IBM	QRadar Network Security	Affected: 5.4.0 Affected: 5.5.0

Date Public

2021-11-07 00:00

Show details on NVD website

JSON

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            "url": "https://www.ibm.com/support/pages/node/6514403"
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            "url": "https://exchange.xforce.ibmcloud.com/vulnerabilities/174269"
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        ],
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    "cna": {
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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.