CVE-2026-116829: llama.cpp b10398 before b11301 Integer Overflow in gguf_init_from_reader

Published Oct 27, 2026
·
Updated

llama.cpp b10398 before b11301 contains an integer overflow vulnerability in ggufinitfromreader that allows attackers to bypass tensor element-count validation by supplying shapes whose product wraps to zero. Attackers can craft GGUF files with enormous tensor extents that overflow ggmlnbytes() and strides, causing out-of-bounds memory access when buffers are sized from wrapped byte counts.

Affected Software

1 affected component
ggml-org llama.cpp>=b10398<b11301

Event History

Oct 27, 2026
CVE Published
via MITRE·12:33 AM
Data Sourced
via MITRE·12:33 AM
DescriptionSeverityWeakness

Frequently Asked Questions

1

Which installations are affected?

ggml-org llama.cpp builds from b10398 through builds before b11301 are affected. The issue is triggered while reading GGUF files.

2

What does an attacker need to exploit this issue?

An attacker needs to supply a crafted GGUF file containing tensor shapes with enormous extents whose product wraps to zero. Exploitation requires the vulnerable software to process that file.

3

What is the practical impact of a malicious GGUF file?

The crafted tensor metadata can bypass element-count validation and cause byte-count and stride calculations to overflow. Buffers may then be sized using wrapped values, leading to out-of-bounds memory access.

4

What should be done if affected builds are in use?

Update to b11301 or a later build. Until updating is possible, do not process GGUF files from untrusted sources, since crafted files are the input required to trigger the vulnerable parsing path.

Contact

SecAlerts Pty Ltd.
132 Wickham Terrace
Fortitude Valley,
QLD 4006, Australia
info@secalerts.co
By using SecAlerts services, you agree to our services end-user license agreement. This website is safeguarded by reCAPTCHA and governed by the Google Privacy Policy and Terms of Service. All names, logos, and brands of products are owned by their respective owners, and any usage of these names, logos, and brands for identification purposes only does not imply endorsement. If you possess any content that requires removal, please get in touch with us.
© 2026 SecAlerts Pty Ltd.
ABN: 70 645 966 203, ACN: 645 966 203