CVE-2026-42031 (GCVE-0-2026-42031)
Vulnerability from cvelistv5 – Published: 2026-05-13 18:52 – Updated: 2026-05-15 19:15Title
CKAN: Unauthenticated SQL Injection and Authorization Bypass in `datastore_search_sql`
Summary
CKAN is an open-source DMS (data management system) for powering data hubs and data portals. Prior to 2.10.10 and 2.11.5, a vulnerability in datastore_search_sql allowed attackers to inject SQL in order to gain access to private resources and PostgreSQL system information This vulnerability is fixed in 2.10.10 and 2.11.5.
Severity
SSVC
Exploitation: none
Automatable: yes
Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-05-15 19:12 UTC
CWE
- CWE-89 - Improper Neutralization of Special Elements used in an SQL Command ('SQL Injection')
Assigner
References
1 reference
| URL | Tags |
|---|---|
| https://github.com/ckan/ckan/security/advisories/… | x_refsource_CONFIRM |
Impacted products
Previdian
Known Exploited Vulnerability - GCVE BCP-07 Compliant
KEV entry ID: 8dce610f-d498-42b4-8529-efc9073923ac
Exploited: Yes
Timestamps
First Seen: 2026-09-10
Asserted: 2026-09-10
Scope
Notes: CKAN: Unauthenticated SQL Injection and Authorization Bypass in `datastore_search_sql` | Affected: Ckan / ckan | CVSS: 8.3 (HIGH) | EPSS: 0.01817 | Used in malware: unknown | Not yet in CISA KEV: True
Evidence
Type: Public Report
Signal: Successful Exploitation
Confidence: 70%
Source: previdian
Details
| Feed | Previdian (previdian.com) |
|---|---|
| Title | CKAN: Unauthenticated SQL Injection and Authorization Bypass in `datastore_search_sql` |
| Cve Id | CVE-2026-42031 |
| Vendor | Ckan |
| Ghsa Id | None |
| Product | ckan |
| Added Date | 2026-09-10T09:03:59.303Z |
| Cvss Score | 8.3 |
| Epss Score | 0.01817 |
| Previous Ids | |
| Cvss Severity | HIGH |
| Virtual Patch | False |
| Cvss Estimated | False |
| Epss Percentile | 0.77418 |
| Used In Malware | unknown |
| Vulnerability Id | CVE-2026-42031 |
| Ahead Of Cisa Kev | None |
| Not Yet In Cisa Kev | True |
References
Created: 2026-09-11 10:00 UTC
| Updated: 2026-09-11 10:00 UTC
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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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