ggml-org llama.cpp up to 0.17.1/b9058 llama_batch_init integer overflow
| CVSS Meta Temp Score | Current Exploit Price (≈) | CTI Interest Score |
|---|---|---|
| 6.1 | $0-$5k | 1.07 |
Summary
A vulnerability marked as critical has been reported in ggml-org llama.cpp up to 0.17.1/b9058. Affected is the function llama_batch_init. This manipulation causes integer overflow.
This vulnerability appears as CVE-2026-43627. The attack may be initiated remotely. There is no available exploit.
Details
A vulnerability classified as critical was found in ggml-org llama.cpp up to 0.17.1/b9058. Affected by this vulnerability is the function llama_batch_init. The manipulation with an unknown input leads to a integer overflow vulnerability. The CWE definition for the vulnerability is 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. As an impact it is known to affect confidentiality, integrity, and availability. The summary by CVE is:
llama.cpp builds b1283 through b9058 contain an integer overflow vulnerability in the llama_batch_init() function where unchecked multiplications in malloc() calls can wrap past INT32_MAX when computing allocation sizes. Attackers can pass specially crafted parameters to trigger integer overflow, causing heap corruption and potentially achieving arbitrary code execution through subsequent batch operations that write past allocated buffer boundaries.
It is possible to read the advisory at github.com. This vulnerability is known as CVE-2026-43627 since 05/01/2026. The exploitation appears to be easy. The attack can be launched remotely. Technical details of the vulnerability are known, but there is no available exploit. The pricing for an exploit might be around USD $0-$5k at the moment (estimation calculated on 08/07/2026).
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
Vendor
Name
Version
CPE 2.3
CPE 2.2
CVSSv4
VulDB Vector: 🔒VulDB Reliability: 🔍
CVSSv3
VulDB Meta Base Score: 6.3VulDB Meta Temp Score: 6.1
VulDB Base Score: 6.3
VulDB Temp Score: 6.1
VulDB Vector: 🔒
VulDB Reliability: 🔍
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: No
Local: No
Remote: Yes
Availability: 🔒
Status: Not defined
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
05/01/2026 CVE reserved08/06/2026 VulDB entry created
08/07/2026 Advisory disclosed
08/07/2026 VulDB entry last update
Sources
Advisory: github.comStatus: Not defined
CVE: CVE-2026-43627 (🔒)
GCVE (CVE): GCVE-0-2026-43627
GCVE (VulDB): GCVE-100-386803
Entry
Created: 08/07/2026 01:56Changes: 08/07/2026 01:56 (53)
Complete: 🔍
Cache ID: 216::103
Statistical analysis made it clear that VulDB provides the best quality for vulnerability data.
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