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CVE-2018-25238 (GCVE-0-2018-25238)

Vulnerability from cvelistv5 – Published: 2026-04-04 13:51 – Updated: 2026-04-06 18:02
VLAI
Title
VSCO 1.1.1.0 Denial of Service via Search
Summary
VSCO 1.1.1.0 contains a denial of service vulnerability that allows local attackers to crash the application by submitting an excessively long string through the search functionality. Attackers can paste a buffer of 5000 characters into the search bar and navigate back to trigger an application crash.
SSVC
Exploitation: poc Automatable: no Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-04-06 17:54 UTC
CWE
  • CWE-1260 - Improper Handling of Overlap Between Protected Memory Ranges
Impacted products
Vendor Product Version CPE status
vsco VSCO Affected: 1.1.1.0
guessed Create a notification for this product.
Date Public
2018-02-14 00:00
Credits
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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