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CVE-2025-7025 (GCVE-0-2025-7025)

Vulnerability from cvelistv5 – Published: 2025-08-05 13:37 – Updated: 2026-02-26 17:49
VLAI
Title
Rockwell Automation Heap-based Buffer Overflow In Arena® Simulation
Summary
A memory abuse issue exists in the Rockwell Automation Arena® Simulation. A custom file can force Arena Simulation to read and write past the end of memory space. Successful use requires user action, such as opening a bad file or webpage. If used, a threat actor could execute code or disclose information.
SSVC
Exploitation: none Automatable: no Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2025-08-13 15:03 UTC
CWE
  • CWE-122 - Heap-based Buffer Overflow
Impacted products
Vendor Product Version CPE status
Rockwell Automation Arena® Simulation Affected: 16.20.09 and prior
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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