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CVE-2018-25262 (GCVE-0-2018-25262)

Vulnerability from cvelistv5 – Published: 2026-04-22 14:56 – Updated: 2026-04-22 15:59
VLAI
Title
Angry IP Scanner for Linux 3.5.3 Denial of Service
Summary
Angry IP Scanner for Linux 3.5.3 contains a denial of service vulnerability that allows local attackers to crash the application by supplying malformed input to the port selection field. Attackers can craft a malicious string containing buffer overflow patterns and paste it into the Preferences Ports tab to trigger an application crash.
SSVC
Exploitation: poc Automatable: no Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-04-22 15:57 UTC
CWE
Impacted products
Vendor Product Version CPE status
Angryip Angry IP Scanner for Linux Affected: 3.5.3
guessed Create a notification for this product.
Date Public
2018-12-22 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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