CVE-2019-3962 (GCVE-0-2019-3962)

Vulnerability from cvelistv5 – Published: 2019-07-01 19:39 – Updated: 2024-08-04 19:26
VLAI
Summary
Content Injection vulnerability in Tenable Nessus prior to 8.5.0 may allow an authenticated, local attacker to exploit this vulnerability by convincing another targeted Nessus user to view a malicious URL and use Nessus to send fraudulent messages. Successful exploitation could allow the authenticated adversary to inject arbitrary text into the feed status, which will remain saved post session expiration.
Severity
3.3 (Low)
CVSS:3.0/AV:L/AC:L/PR:N/UI:R/S:U/C:N/I:L/A:N
CWE
  • Content Injection
Assigner
References
URL Tags
https://www.tenable.com/security/tns-2019-04 x_refsource_CONFIRM
http://www.securityfocus.com/bid/109025 vdb-entryx_refsource_BID
Impacted products
Vendor Product Version CPE status
Tenable Nessus Affected: All versions prior to 8.5.0
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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.


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.