CVE-2022-3732 (GCVE-0-2022-3732)

Vulnerability from cvelistv5 – Published: 2022-10-28 00:00 – Updated: 2025-04-15 13:23
VLAI
Title
seccome Ehoney set sql injection
Summary
A vulnerability was found in seccome Ehoney and classified as critical. Affected by this issue is some unknown functionality of the file /api/v1/bait/set. The manipulation of the argument Payload leads to sql injection. The attack may be launched remotely. VDB-212414 is the identifier assigned to this vulnerability.
SSVC
Exploitation: none Automatable: no Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2025-04-14 17:07 UTC
CWE
  • CWE-707 - Improper Neutralization -> CWE-74 Injection -> CWE-89 SQL Injection
References
Impacted products
Vendor Product Version CPE status
seccome Ehoney Affected: n/a
guessed Create a notification for this product.
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.

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.

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Detection rules are retrieved from Rulezet.

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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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