CVE-2023-2062 (GCVE-0-2023-2062)

Vulnerability from cvelistv5 – Published: 2023-06-02 04:04 – Updated: 2025-03-05 18:58
VLAI
Title
Information Disclosure vulnerability in EtherNet/IP Configuration tools
Summary
Missing Password Field Masking vulnerability in Mitsubishi Electric Corporation EtherNet/IP configuration tools SW1DNN-EIPCT-BD and SW1DNN-EIPCTFX5-BD allows a remote unauthenticated attacker to know the password for MELSEC iQ-R Series EtherNet/IP module RJ71EIP91 and MELSEC iQ-F Series EtherNet/IP module FX5-ENET/IP. This vulnerability results in authentication bypass vulnerability, which allows the attacker to access MELSEC iQ-R Series EtherNet/IP module RJ71EIP91 and MELSEC iQ-F Series EtherNet/IP module FX5-ENET/IP via FTP.
SSVC
Exploitation: none Automatable: no Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2025-03-05 18:36 UTC
CWE
  • CWE-549 - Missing Password Field Masking
References
Show details on NVD website

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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.

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  • 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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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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