CVE-2026-12975 (GCVE-0-2026-12975)
Vulnerability from cvelistv5 – Published: 2026-06-25 21:12 – Updated: 2026-08-26 12:04
VLAI
EPSS
VEX
Title
Apicurio/apicurio-registry: apicurio-registry: unhardened saxparser in content-type detection leads to blind xxe / ssrf / billion-laughs dos
Summary
A flaw was found in Apicurio Registry. The ContentTypeUtil.isParsableXml() method creates a SAXParserFactory without enabling secure processing features or disabling external entity resolution. An attacker with artifact-write permission (or unauthenticated when the registry runs with default configuration) can upload a crafted XML document to trigger blind server-side request forgery (SSRF) via external DTD/entity fetch, or cause denial of service via entity expansion.
Severity
8.5 (High)
SSVC
Exploitation: none
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-06-26 13:38 UTC
CWE
- CWE-611 - Improper Restriction of XML External Entity Reference
Assigner
References
4 references
| URL | Tags |
|---|---|
| https://access.redhat.com/errata/RHSA-2026:59360 | vendor-advisoryx_refsource_REDHAT |
| https://access.redhat.com/security/cve/CVE-2026-12975 | vdb-entryx_refsource_REDHAT |
| https://bugzilla.redhat.com/show_bug.cgi?id=2491688 | issue-trackingx_refsource_REDHAT |
| https://security.access.redhat.com/data/csaf/v2/v… | x_sadp-csaf-vex |
Impacted products
2 products
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| Red Hat | Red Hat build of Apicurio Registry 3.3.1 |
cpe:/a:redhat:apicurio_registry:3.3
|
||
| Red Hat | Red Hat build of Apicurio Registry 3.3.1 |
Unaffected:
registry-rhel8 , < *
(rpm)
Unaffected: registry-rhel9 , < * (rpm) cpe:/a:redhat:apicurio_registry:3.3 |
Date Public
2026-06-10 13:00
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}
],
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},
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"attackVector": "NETWORK",
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"baseScore": 8.5,
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"confidentialityImpact": "LOW",
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"value": "Made public."
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],
"title": "Apicurio/apicurio-registry: apicurio-registry: unhardened saxparser in content-type detection leads to blind xxe / ssrf / billion-laughs dos",
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}
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Experimental. This forecast is provided for visualization only and may change without notice. Do not use it for operational decisions.
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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The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the
vulnerability by the CIRCL/vulnerability-attack-technique-classification-roberta-base
model, served locally by ML-Gateway.
They have not been verified by an analyst and are provided for guidance only.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
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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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