ggml-org llama.cpp up to 0.17.1 Recurrent Memory State Restore Path out-of-bounds
| CVSS Meta Temp Score | Current Exploit Price (≈) | CTI Interest Score |
|---|---|---|
| 4.2 | $0-$5k | 2.13 |
Summary
A vulnerability was found in ggml-org llama.cpp up to 0.17.1. It has been classified as problematic. The impacted element is an unknown function of the component Recurrent Memory State Restore Path. Performing a manipulation results in out-of-bounds. This vulnerability is identified as CVE-2026-43630. The attack can be initiated remotely. There is not any exploit available.
Details
A vulnerability classified as problematic has been found in ggml-org llama.cpp up to 0.17.1. This affects an unknown code block of the component Recurrent Memory State Restore Path. The manipulation with an unknown input leads to a out-of-bounds vulnerability. CWE is classifying the issue as CWE-125. The product reads data past the end, or before the beginning, of the intended buffer. This is going to have an impact on confidentiality. The summary by CVE is:
llama.cpp builds b5702 through b7653 contain an out-of-bounds read vulnerability in the recurrent memory state restore path that allows attackers with write access to the slot save directory to read memory past the end of the allocated cells array. Attackers can craft a malicious slot file with an oversized seq_id value to trigger an out-of-bounds read that leaks heap data including pointer values into server logs, defeating ASLR protections and facilitating further exploitation.
It is possible to read the advisory at github.com. This vulnerability is uniquely identified as CVE-2026-43630 since 05/01/2026. The exploitability is told to be easy. It is possible to initiate the attack remotely. The technical details are unknown and an exploit is not publicly available.
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: 4.3VulDB Meta Temp Score: 4.2
VulDB Base Score: 4.3
VulDB Temp Score: 4.2
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: Out-of-boundsCWE: CWE-125 / CWE-119
CAPEC: 🔒
ATT&CK: 🔒
Physical: No
Local: No
Remote: Yes
Availability: 🔒
Status: Not defined
Price Prediction: 🔍
Current Price Estimation: 🔒
| 0-Day | Unlock | Unlock | Unlock | Unlock |
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| 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 reserved08/07/2026 Advisory disclosed
08/07/2026 VulDB entry created
08/07/2026 VulDB entry last update
Sources
Advisory: github.comStatus: Not defined
CVE: CVE-2026-43630 (🔒)
GCVE (CVE): GCVE-0-2026-43630
GCVE (VulDB): GCVE-100-386811
Entry
Created: 08/07/2026 02:00Changes: 08/07/2026 02:00 (53)
Complete: 🔍
Cache ID: 216::103
Statistical analysis made it clear that VulDB provides the best quality for vulnerability data.
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