CVE-2017-6547 (GCVE-0-2017-6547)

Vulnerability from cvelistv5 – Published: 2017-03-09 09:26 – Updated: 2024-08-05 15:33
VLAI
Summary
Cross-site scripting (XSS) vulnerability in httpd on ASUS RT-N56U, RT-N66U, RT-AC66U, RT-N66R, RT-AC66R, RT-AC68U, RT-AC68R, RT-N66W, RT-AC66W, RT-AC87R, RT-AC87U, RT-AC51U, RT-AC68P, RT-N11P, RT-N12+, RT-N12E B1, RT-AC3200, RT-AC53U, RT-AC1750, RT-AC1900P, RT-N300, and RT-AC750 routers with firmware before 3.0.0.4.380.7378; RT-AC68W routers with firmware before 3.0.0.4.380.7266; and RT-N600, RT-N12+ B1, RT-N11P B1, RT-N12VP B1, RT-N12E C1, RT-N300 B1, and RT-N12+ Pro routers with firmware before 3.0.0.4.380.9488 allows remote attackers to inject arbitrary JavaScript by requesting filenames longer than 50 characters.
Severity
6.1 (Medium)
CVSS:3.0/AV:N/AC:L/PR:N/UI:R/S:C/C:L/I:L/A:N
CWE
  • n/a
Assigner
References
Date Public
2017-03-09 00:00
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.



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.