CVE-2026-25076 (GCVE-0-2026-25076)
Vulnerability from cvelistv5 – Published: 2026-03-12 21:07 – Updated: 2026-03-13 13:08
VLAI
EPSS
VEX
Title
Anchore Enterprise GraphQL Reports API SQL injection
Summary
Anchore Enterprise versions before 5.25.1 contain an SQL injection vulnerability in the GraphQL Reports API. An authenticated attacker that is able to access the GraphQL API could execute arbitrary SQL instructions resulting in modifications to the data contained in the Anchore Enterprise database.
Severity
SSVC
Exploitation: none
Automatable: no
Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-03-13 13:07 UTC
CWE
- CWE-89 - SQL Injection
Assigner
References
3 references
| URL | Tags |
|---|---|
| https://docs.anchore.com/current/docs/release_not… | release-notespatch |
| https://anchore.com/platform/ | product |
| https://www.vulncheck.com/advisories/anchore-ente… | third-party-advisory |
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| Anchore | Anchore Enterprise |
Affected:
0 , < 5.25.1
(semver)
cpe:2.3:a:anchore:anchore:*:*:*:*:enterprise:*:*:* |
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Sightings
| Author | Source | Type | Date | Other |
|---|
Nomenclature
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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.
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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.