Vulnerability-Lookup

CNVD-2018-10006

Vulnerability from cnvd - Published: 2018-05-22

Title

Cisco IoT Field Network Director跨站请求伪造漏洞

Description

Cisco IoT Field Network Director（IoT-FND）是美国思科（Cisco）公司的一套端到端的物联网管理系统。该系统具有设备管理、资产跟踪和智能计量等功能。 Cisco IoT-FND中的基于Web的管理界面存在跨站请求伪造漏洞，该漏洞源于程序未能对基于Web的管理界面执行充分的跨站请求伪造保护。远程攻击者可通过诱使用户打开恶意的链接利用该漏洞以该受影响用户的权限执行任意操作。

Severity

高

Patch Name

Cisco IoT Field Network Director跨站请求伪造漏洞的补丁

Patch Description

Formal description

目前厂商已发布升级补丁以修复漏洞，补丁获取链接： https://tools.cisco.com/security/center/content/CiscoSecurityAdvisory/cisco-sa-20180516-fnd

Reference

https://tools.cisco.com/security/center/content/CiscoSecurityAdvisory/cisco-sa-20180516-fnd

Impacted products

Name
Cisco IoT Field Network Director

Show details on source website

JSON

To clipboard

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}

CVE-2018-0270 (GCVE-0-2018-0270)

Vulnerability from cvelistv5 – Published: 2018-05-17 03:00 – Updated: 2024-11-29 15:10

Summary

A vulnerability in the web-based management interface of Cisco IoT Field Network Director (IoT-FND) could allow an unauthenticated, remote attacker to conduct a cross-site request forgery (CSRF) attack and alter the data of existing users and groups on an affected device. The vulnerability is due to insufficient CSRF protections for the web-based management interface on an affected device. An attacker could exploit this vulnerability by persuading a user of the interface to follow a malicious link. A successful exploit could allow the attacker to perform arbitrary actions with the privilege level of the affected user. If the user has administrative privileges, the attacker could create a new, privileged account to obtain full control over the device interface. This vulnerability affects Connected Grid Network Management System, if running a software release prior to IoT-FND Release 3.0; and IoT Field Network Director, if running a software release prior to IoT-FND Release 4.1.1-6 or 4.2.0-123. Cisco Bug IDs: CSCvi02448.

Severity

No CVSS data available.

SSVC

Exploitation: none Automatable: no Technical Impact: total

CISA Coordinator · CISA-ADP (v2.0.3)

Decision recorded 2024-11-29 14:42 UTC

CWE

CWE-352

Assigner

cisco mitre root

References

2 references

URL	Tags
http://www.securityfocus.com/bid/104242	vdb-entryx_refsource_BID
https://tools.cisco.com/security/center/content/C…	x_refsource_CONFIRM

Impacted products

1 product

Vendor	Product	Version
n/a	Cisco IoT Field Network Director	Affected: Cisco IoT Field Network Director

Date Public

2018-05-16 00:00

Show details on NVD website

JSON

To clipboard

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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.