CVE-2026-108960 (GCVE-0-2026-108960)
Vulnerability from cvelistv5 – Published: 2026-10-13 03:00 – Updated: 2026-10-13 13:28
VLAI
EPSS
VEX
Title
Tiiny-AI PowerInfer FFN GPU-split Loader ggml-cuda.cu create_striped_mat_to_gpu out-of-bounds
Summary
A vulnerability was detected in Tiiny-AI PowerInfer up to 8bd56d69906c9d2dba4d3bf6899763401e01a9a4. Affected is the function create_striped_mat_to_gpu of the file smallthinker/ggml/src/ggml-cuda/ggml-cuda.cu of the component FFN GPU-split Loader. The manipulation results in out-of-bounds read. The attack needs to be approached locally. The exploit is now public and may be used. This product utilizes a rolling release system for continuous delivery, and as such, version information for affected or updated releases is not disclosed. The pull request to fix this issue awaits acceptance.
Severity
SSVC
Exploitation: poc
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-10-13 13:27 UTC
Assigner
References
6 references
| URL | Tags |
|---|---|
| https://vuldb.com/vuln/416779 | vdb-entrytechnical-description |
| https://vuldb.com/vuln/416779/cti | signaturepermissions-required |
| https://vuldb.com/cve/CVE-2026-108960 | third-party-advisory |
| https://vuldb.com/submit/997933 | third-party-advisory |
| https://github.com/Tiiny-AI/PowerInfer/issues/284 | exploitissue-tracking |
| https://github.com/Tiiny-AI/PowerInfer/ | product |
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| Tiiny-AI | PowerInfer |
Affected:
8bd56d69906c9d2dba4d3bf6899763401e01a9a4
cpe:2.3:a:tiiny-ai:powerinfer:*:*:*:*:*:*:*:* |
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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
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