CVE-2026-7018 (GCVE-0-2026-7018)
Vulnerability from cvelistv5 – Published: 2026-04-26 03:30 – Updated: 2026-04-27 17:02
VLAI
EPSS
VEX
Title
Datavane Datavines JWT Token TokenManager.java hard-coded key
Summary
A vulnerability was determined in Datavane Datavines up to 13607645e14a4982468cfdbcf75c85cde63bae71. The affected element is an unknown function of the file datavines-core/src/main/java/io/datavines/core/utils/TokenManager.java of the component JWT Token Handler. Executing a manipulation of the argument tokenSecret can lead to use of hard-coded cryptographic key
. The attack can be executed remotely. The attack requires a high level of complexity. The exploitability is described as difficult. The exploit has been publicly disclosed and may be utilized. This product implements a rolling release for ongoing delivery, which means version information for affected or updated releases is unavailable. This patch is called e540d6dc04e2e6ad11907fb655f3728a13e7b939. It is advisable to implement a patch to correct this issue. The project was informed of the problem early through a pull request but has not reacted yet.
Severity
SSVC
Exploitation: poc
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-04-27 17:02 UTC
Assigner
References
8 references
| URL | Tags |
|---|---|
| https://vuldb.com/vuln/359597 | vdb-entrytechnical-description |
| https://vuldb.com/vuln/359597/cti | signaturepermissions-required |
| https://vuldb.com/submit/797305 | third-party-advisory |
| https://github.com/datavane/datavines/issues/580 | issue-tracking |
| https://github.com/datavane/datavines/pull/579 | issue-trackingpatch |
| https://github.com/datavane/datavines/issues/580#… | exploitissue-tracking |
| https://github.com/datavane/datavines/pull/579/ch… | issue-trackingpatch |
| https://github.com/datavane/datavines/ | product |
Impacted products
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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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| 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.
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.