CVE-2026-86463 (GCVE-0-2026-86463)

Vulnerability from cvelistv5 – Published: 2026-10-09 10:18 – Updated: 2026-10-09 11:08
VLAI
Title
Apache CXF: FIQL Query Parser Denial of Service
Summary
Apache CXF's FIQL query parser has a vulnerability in how it searches for operators in query expressions. The search pattern can get stuck trying many combinations when it encounters a long string without an operator, causing the parser to consume excessive CPU time. An attacker can send a crafted query to make the server use up CPU resources, potentially slowing down or stopping other requests. The fix was to limit FIQL expressions to 4 KiB by default, preventing attackers from sending extremely long inputs while still allowing normal queries. Users are recommended to upgrade to versions 4.2.4 or 4.1.9 or 3.6.13, which fix this issue.
Severity
No CVSS data available.
Assigner
apache CNA under the mitre root
CNA scorecard B 76/100 over 939 records in the last 180 days details
References
Impacted products
Vendor Product Version CPE status
Apache Software Foundation Apache CXF Affected: 4.2.0 , < 4.2.4 (semver)
Affected: 4.0.0 , < 4.1.9 (semver)
Affected: 0 , < 3.6.13 (semver)
guessed Create a notification for this product.
Credits
Mike Read (github.com/Michael-JRead)
Show details on NVD website
To clipboard 

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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.

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  • 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.
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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.



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.