CVE-2026-41139 (GCVE-0-2026-41139)
Vulnerability from cvelistv5 – Published: 2026-05-07 05:06 – Updated: 2026-07-15 00:59
VLAI
EPSS
VEX
Title
Unsafe array index getter in mathjs
Summary
Math.js is an extensive math library for JavaScript and Node.js. From version 13.1.0 to before version 15.2.0, arbitrary JavaScript can be executed via the expression parser of mathjs. This issue has been patched in version 15.2.0.
Severity
8.8 (High)
8.8 (High)
SSVC
Exploitation: none
Automatable: no
Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-05-07 13:41 UTC
CWE
Assigner
References
8 references
| URL | Tags |
|---|---|
| https://github.com/josdejong/mathjs/security/advi… | x_refsource_CONFIRM |
| https://github.com/josdejong/mathjs/pull/3656 | x_refsource_MISC |
| https://github.com/josdejong/mathjs/commit/0aee2f… | x_refsource_MISC |
| https://github.com/josdejong/mathjs/commit/bcf0da… | x_refsource_MISC |
| https://github.com/josdejong/mathjs/releases/tag/… | x_refsource_MISC |
| https://access.redhat.com/security/cve/CVE-2026-41139 | vdb-entryx_refsource_REDHAT |
| https://bugzilla.redhat.com/show_bug.cgi?id=2467648 | issue-trackingx_refsource_REDHAT |
| https://security.access.redhat.com/data/csaf/v2/v… | x_sadp-csaf-vex |
Impacted products
9 products
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| josdejong | mathjs |
Affected:
>= 13.1.0, < 15.2.0
|
guessed | |
| Red Hat | Cryostat 4 |
cpe:/a:redhat:cryostat:4
|
||
| Red Hat | Red Hat Developer Hub |
cpe:/a:redhat:rhdh:1
|
||
| Red Hat | Red Hat Enterprise Linux 10 |
cpe:/o:redhat:enterprise_linux:10
|
||
| Red Hat | Red Hat Enterprise Linux 8 |
cpe:/o:redhat:enterprise_linux:8
|
||
| Red Hat | Red Hat Enterprise Linux 9 |
cpe:/o:redhat:enterprise_linux:9
|
||
| Red Hat | Red Hat OpenShift AI (RHOAI) |
cpe:/a:redhat:openshift_ai
|
||
| Red Hat | Red Hat OpenShift Container Platform 4 |
cpe:/a:redhat:openshift:4
|
||
| Red Hat | Self-service automation portal 2 |
cpe:/a:redhat:ansible_portal:2
|
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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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