CVE-2026-92786 (GCVE-0-2026-92786)
Vulnerability from cvelistv5 – Published: 2026-09-16 20:32 – Updated: 2026-09-16 20:32
VLAI
EPSS
VEX
Title
LightGBM through 4.7.0 Out-of-Bounds Write via Crafted Model
Summary
LightGBM through 4.7.0 fails to validate child and split array values when parsing text models, allowing attackers to write out-of-bounds memory during SHAP prediction. Attackers can craft malicious model files with invalid node references that trigger out-of-bounds writes at attacker-chosen offsets in the leaf_depth_ buffer during feature contribution computation.
Severity
CWE
- CWE-787 - Out-of-bounds Write
Assigner
References
5 references
| URL | Tags |
|---|---|
| https://github.com/lightgbm-org/LightGBM/issues/7357 | issue-tracking |
| https://github.com/lightgbm-org/LightGBM | product |
| https://github.com/lightgbm-org/LightGBM/blob/v4.… | technical-description |
| https://github.com/lightgbm-org/LightGBM/blob/v4.… | technical-description |
| https://www.vulncheck.com/advisories/lightgbm-thr… | third-party-advisory |
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| lightgbm-org | LightGBM |
Affected:
0 , ≤ 4.7.0
(semver)
|
guessed |
Date Public
2026-07-10 00:00
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}
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"tags": [
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},
{
"tags": [
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],
"url": "https://github.com/lightgbm-org/LightGBM"
},
{
"name": "RecomputeLeafDepths writes at the bit-inverted child value with no bounds check",
"tags": [
"technical-description"
],
"url": "https://github.com/lightgbm-org/LightGBM/blob/v4.6.0/include/LightGBM/tree.h#L691-L698"
},
{
"name": "child and split arrays are parsed with only a length check",
"tags": [
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"url": "https://github.com/lightgbm-org/LightGBM/blob/v4.6.0/src/io/tree.cpp#L750-L766"
},
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"title": "LightGBM through 4.7.0 Out-of-Bounds Write via Crafted Model",
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"datePublished": "2026-09-16T20:32:43.282Z",
"dateReserved": "2026-09-16T19:31:51.976Z",
"dateUpdated": "2026-09-16T20:32:43.282Z",
"state": "PUBLISHED"
},
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"date": "2026-09-17",
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"percentile": "0.02746"
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}
],
"id": "CVE-2026-92786",
"lastModified": "2026-09-16T21:17:27.880",
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"exploitMaturity": "NOT_DEFINED",
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"modifiedAttackRequirements": "NOT_DEFINED",
"modifiedAttackVector": "NOT_DEFINED",
"modifiedPrivilegesRequired": "NOT_DEFINED",
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}
}
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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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