CVE-2026-63204 (GCVE-0-2026-63204)

Vulnerability from cvelistv5 – Published: 2026-09-25 18:22 – Updated: 2026-09-25 18:41
VLAI
Title
Zammad: Authenticated agents can read AI summary error messages from inaccessible tickets
Summary
Zammad is a web based open source helpdesk/customer support system. Prior to 7.1.2, an authenticated user with agent permissions can supply an arbitrary AI analytics run identifier to the ticket summarize endpoint and receive the AI provider error message stored for that run, even if the run belongs to a ticket the agent is not authorized to access. The disclosure is limited to the provider error string; ticket content is not exposed. This issue is fixed in version 7.1.2.
Severity
2.3 (Low)
CVSS:4.0/AV:N/AC:L/AT:P/PR:L/UI:N/VC:L/VI:N/VA:N/SC:N/SI:N/SA:N
SSVC
Exploitation: none Automatable: no Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-09-25 18:41 UTC
CWE
  • CWE-639 - Authorization Bypass Through User-Controlled Key
  • CWE-862 - Missing Authorization
Assigner
References
Impacted products
Vendor Product Version CPE status
zammad zammad Affected: < 7.1.2
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.
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  • 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.