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CVE-2018-1000159 (GCVE-0-2018-1000159)

Vulnerability from cvelistv5 – Published: 2018-04-18 19:00 – Updated: 2024-08-05 12:33
VLAI
Summary
tlslite-ng version 0.7.3 and earlier, since commit d7b288316bca7bcdd082e6ccff5491e241305233 contains a CWE-354: Improper Validation of Integrity Check Value vulnerability in TLS implementation, tlslite/utils/constanttime.py: ct_check_cbc_mac_and_pad(); line "end_pos = data_len - 1 - mac.digest_size" that can result in an attacker manipulating the TLS ciphertext which will not be detected by receiving tlslite-ng. This attack appears to be exploitable via man in the middle on a network connection. This vulnerability appears to have been fixed after commit 3674815d1b0f7484454995e2737a352e0a6a93d8.
Severity
5.9 (Medium)
CVSS:3.0/AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:H/A:N
CWE
  • n/a
Assigner
References
Date Public
2018-04-18 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.


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.