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CVE-2019-18995 (GCVE-0-2019-18995)

Vulnerability from cvelistv5 – Published: 2019-12-18 20:19 – Updated: 2024-08-05 02:02
VLAI
Title
ABB PB610 HMISimulator does not check content-length of the HTTP request
Summary
The HMISimulator component of ABB PB610 Panel Builder 600 versions 2.8.0.424 and earlier fails to validate the content-length field for HTTP requests, exposing HMISimulator to denial of service via crafted HTTP requests manipulating the content-length setting.
Severity
4.3 (Medium)
CVSS:3.1/AV:A/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:L
CWE
  • CWE-20 - Improper Input Validation
Assigner
References
Impacted products
Vendor Product Version CPE status
ABB PB610 Panel Builder 600 Affected: unspecified , ≤ 2.8.0.424 (custom)
guessed Create a notification for this product.
Credits
NSFOCUS for providing vulnerability details and proof of concept.
Show details on NVD website
To clipboard 

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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.

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.


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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.