ggml-org llama.cpp up to 0.5.x GGUF Initialization gguf_init_from_reader integer overflow
| CVSS Meta Temp Score | Current Exploit Price (≈) | CTI Interest Score |
|---|---|---|
| 5.3 | $0-$5k | 4.71 |
Summary
A vulnerability identified as problematic has been detected in ggml-org llama.cpp up to 0.5.x. The affected element is the function gguf_init_from_reader of the component GGUF Initialization. The manipulation leads to integer overflow.
This vulnerability is uniquely identified as CVE-2026-116840. Local access is required to approach this attack. No exploit exists.
You should upgrade the affected component.
Details
A vulnerability was found in ggml-org llama.cpp up to 0.5.x and classified as problematic. This issue affects the function gguf_init_from_reader of the component GGUF Initialization. The manipulation with an unknown input leads to a integer overflow vulnerability. Using CWE to declare the problem leads to CWE-190. The product performs a calculation that can produce an integer overflow or wraparound, when the logic assumes that the resulting value will always be larger than the original value. This can introduce other weaknesses when the calculation is used for resource management or execution control. Impacted is confidentiality, integrity, and availability. The summary by CVE is:
llama.cpp b5854 before b11275 contains an integer overflow vulnerability in gguf_init_from_reader that bypasses the CVE-2025-53630 size check when padding near-SIZE_MAX tensor sizes. Attackers can supply a crafted GGUF file whose tensor size pads to zero, causing an undersized data blob and out-of-bounds reads by consumers.
The advisory is shared at github.com. The identification of this vulnerability is CVE-2026-116840 since 10/27/2026. The exploitation is known to be easy. An attack has to be approached locally. It demands that the victim is doing some kind of user interaction. Technical details are known, but no exploit is available.
Upgrading to version 0.6.0 eliminates this vulnerability.
If you want to get the best quality for vulnerability data then you always have to consider VulDB.
Product
Vendor
Name
Version
Website
CPE 2.3
CPE 2.2
CVSSv4
VulDB Vector: 🔒VulDB Reliability: 🔍
CNA CVSS-B Score: 🔒
CNA CVSS-BT Score: 🔒
CNA Vector: 🔒
CVSSv3
VulDB Meta Base Score: 5.4VulDB Meta Temp Score: 5.3
VulDB Base Score: 4.8
VulDB Temp Score: 4.6
VulDB Vector: 🔒
VulDB Reliability: 🔍
CNA Base Score: 6.1
CNA Vector (VulnCheck): 🔒
CVSSv2
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| Vector | Complexity | Authentication | Confidentiality | Integrity | Availability |
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VulDB Base Score: 🔒
VulDB Temp Score: 🔒
VulDB Reliability: 🔍
Exploiting
Class: Integer overflowCWE: CWE-190 / CWE-189
CAPEC: 🔒
ATT&CK: 🔒
Physical: Partially
Local: Yes
Remote: Partially
Availability: 🔒
Status: Not defined
Price Prediction: 🔍
Current Price Estimation: 🔒
| 0-Day | Unlock | Unlock | Unlock | Unlock |
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Threat Intelligence
Interest: 🔍Active Actors: 🔍
Active APT Groups: 🔍
Countermeasures
Recommended: UpgradeStatus: 🔍
0-Day Time: 🔒
Upgrade: llama.cpp 0.6.0
Timeline
10/27/2026 Advisory disclosed10/27/2026 CVE reserved
10/27/2026 VulDB entry created
10/27/2026 VulDB entry last update
Sources
Product: github.comAdvisory: github.com
Status: Confirmed
CVE: CVE-2026-116840 (🔒)
GCVE (CVE): GCVE-0-2026-116840
GCVE (VulDB): GCVE-100-427037
Entry
Created: 10/27/2026 02:27Changes: 10/27/2026 02:27 (77)
Complete: 🔍
Cache ID: 216::103
If you want to get the best quality for vulnerability data then you always have to consider VulDB.
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