CVE-2025-66446 (GCVE-0-2025-66446)

Vulnerability from cvelistv5 – Published: 2025-12-11 21:47 – Updated: 2025-12-18 20:41
VLAI
Title
MaxKB has a Python sandbox LD_PRELOAD bypass
Summary
MaxKB is an open-source AI assistant for enterprise. Versions 2.3.1 and below have improper file permissions which allow attackers to overwrite the built-in dynamic linker and other critical files, potentially resulting in privilege escalation. This issue is fixed in version 2.4.0.
SSVC
Exploitation: none Automatable: no Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2025-12-18 20:41 UTC
CWE
  • CWE-362 - Concurrent Execution using Shared Resource with Improper Synchronization ('Race Condition')
Assigner
GitHub_M CNA under the mitre root
CNA scorecard C 72/100 over 7634 records in the last 180 days details
References
Impacted products
Vendor Product Version CPE status
1Panel-dev MaxKB Affected: < 2.4.0
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

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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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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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