CVE-2026-90716 (GCVE-0-2026-90716)
Vulnerability from cvelistv5 – Published: 2026-09-14 12:45 – Updated: 2026-09-16 14:55 X_Open Source
VLAI
EPSS
VEX
Title
marcobambini Gravity Number gravity_parser.c parse_number_expression out-of-bounds
Summary
A vulnerability was detected in marcobambini Gravity up to 0.9.7. This impacts the function parse_number_expression of the file src/compiler/gravity_parser.c of the component Number Parser. Performing a manipulation results in out-of-bounds read. It is possible to initiate the attack remotely. The exploit is now public and may be used. Upgrading to version 0.9.8 will fix this issue. The patch is named 1b9bbf3ad5749e2a3434e6ad073c6e93c24207b6. It is recommended to upgrade the affected component.
Severity
SSVC
Exploitation: poc
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-09-16 14:55 UTC
Assigner
References
9 references
| URL | Tags |
|---|---|
| https://vuldb.com/vuln/403270 | vdb-entrytechnical-description |
| https://vuldb.com/vuln/403270/cti | signaturepermissions-required |
| https://vuldb.com/cve/CVE-2026-90716 | third-party-advisory |
| https://vuldb.com/submit/919190 | third-party-advisory |
| https://github.com/marcobambini/gravity/issues/446 | exploitissue-tracking |
| https://github.com/marcobambini/gravity/pull/449 | issue-trackingpatch |
| https://github.com/sthagen/marcobambini-gravity/c… | patch |
| https://github.com/marcobambini/gravity/releases/… | patch |
| https://github.com/marcobambini/gravity/ | product |
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| marcobambini | Gravity |
Affected:
0.9.0
Affected: 0.9.1 Affected: 0.9.2 Affected: 0.9.3 Affected: 0.9.4 Affected: 0.9.5 Affected: 0.9.6 Affected: 0.9.7 Unaffected: 0.9.8 cpe:2.3:a:marcobambini:gravity:*:*:*:*:*:*:*:* |
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"assignerShortName": "VulDB",
"cveId": "CVE-2026-90716",
"datePublished": "2026-09-14T12:45:10.603Z",
"dateReserved": "2026-09-13T09:53:09.606Z",
"dateUpdated": "2026-09-16T14:55:40.534Z",
"state": "PUBLISHED"
},
"dataType": "CVE_RECORD",
"dataVersion": "5.2"
}
}
}
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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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