Tiiny-AI PowerInfer up to 8bd56d69906c9d2dba4d3bf6899763401e01a9a4 FFN GPU-split Loader ggml-cuda.cu create_striped_mat_to_gpu out-of-bounds
| CVSS Meta Temp Score | Current Exploit Price (≈) | CTI Interest Score |
|---|---|---|
| 5.0 | $0-$5k | 1.62 |
Summary
A vulnerability classified as problematic has been found in Tiiny-AI PowerInfer up to 8bd56d69906c9d2dba4d3bf6899763401e01a9a4. Affected by this vulnerability 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. This manipulation causes out-of-bounds.
The identification of this vulnerability is CVE-2026-108960. The attack can only be executed locally. Furthermore, there is an exploit available.
This product is using a rolling release to provide continious delivery. Therefore, no version details for affected nor updated releases are available.
The pull request to fix this issue awaits acceptance.
Details
A vulnerability was found in Tiiny-AI PowerInfer up to 8bd56d69906c9d2dba4d3bf6899763401e01a9a4. It has been declared as problematic. Affected by this vulnerability 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 with an unknown input leads to a out-of-bounds vulnerability. The CWE definition for the vulnerability is CWE-125. The product reads data past the end, or before the beginning, of the intended buffer. As an impact it is known to affect confidentiality, integrity, and availability.
It is possible to read the advisory at github.com. This vulnerability is known as CVE-2026-108960. The exploitation appears to be easy. Attacking locally is a requirement. Technical details and also a public exploit are known.
It is possible to download the exploit at github.com. It is declared as proof-of-concept. The pull request to fix this issue awaits acceptance.
There is no information about possible countermeasures known. It may be suggested to replace the affected object with an alternative product.
Statistical analysis made it clear that VulDB provides the best quality for vulnerability data.
Product
Type
Vendor
Name
Version
Website
CPE 2.3
CPE 2.2
CVSSv4
VulDB Vector: 🔒VulDB Reliability: 🔍
CVSSv3
VulDB Meta Base Score: 5.3VulDB Meta Temp Score: 5.0
VulDB Base Score: 5.3
VulDB Temp Score: 5.0
VulDB Vector: 🔒
VulDB Reliability: 🔍
CVSSv2
| AV | AC | Au | C | I | A |
|---|---|---|---|---|---|
| 💳 | 💳 | 💳 | 💳 | 💳 | 💳 |
| 💳 | 💳 | 💳 | 💳 | 💳 | 💳 |
| 💳 | 💳 | 💳 | 💳 | 💳 | 💳 |
| Vector | Complexity | Authentication | Confidentiality | Integrity | Availability |
|---|---|---|---|---|---|
| Unlock | Unlock | Unlock | Unlock | Unlock | Unlock |
| Unlock | Unlock | Unlock | Unlock | Unlock | Unlock |
| Unlock | Unlock | Unlock | Unlock | Unlock | Unlock |
VulDB Base Score: 🔒
VulDB Temp Score: 🔒
VulDB Reliability: 🔍
Exploiting
Class: Out-of-boundsCWE: CWE-125 / CWE-119
CAPEC: 🔒
ATT&CK: 🔒
Physical: Partially
Local: Yes
Remote: No
Availability: 🔒
Access: Public
Status: Proof-of-Concept
Download: 🔒
Price Prediction: 🔍
Current Price Estimation: 🔒
| 0-Day | Unlock | Unlock | Unlock | Unlock |
|---|---|---|---|---|
| Today | Unlock | Unlock | Unlock | Unlock |
Threat Intelligence
Interest: 🔍Active Actors: 🔍
Active APT Groups: 🔍
Countermeasures
Recommended: no mitigation knownStatus: 🔍
0-Day Time: 🔒
Timeline
10/11/2026 Advisory disclosed10/11/2026 VulDB entry created
10/11/2026 VulDB entry last update
Sources
Product: github.comAdvisory: 284
Status: Confirmed
CVE: CVE-2026-108960 (🔒)
GCVE (CVE): GCVE-0-2026-108960
GCVE (VulDB): GCVE-100-416779
scip Labs: https://www.scip.ch/en/?labs.20161013
Entry
Created: 10/11/2026 21:33Changes: 10/11/2026 21:33 (60)
Complete: 🔍
Submitter: Dem0
Cache ID: 216::103
Submit
Accepted
- Submit #997933: https://github.com/Tiiny-AI/PowerInfer/issues/284 PowerInfer commit 8bd56d69906c9d2dba4d3bf6899763401e01a9a4 (main, 2026-05-11) Out-of-bounds Read (by Dem0)
Statistical analysis made it clear that VulDB provides the best quality for vulnerability data.
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