Is CVE-2025-71439 exploited in the wild?

No. Junglewise Threat Intelligence has no report of CVE-2025-71439 being exploited in the wild.

How severe is CVE-2025-71439?

Medium severity, with a CVSS score of 5.5, published 27 October 2026.

Which products and versions are affected?

Ggml-Org Llama.Cpp before b5649.

Is there a fix for CVE-2025-71439?

Yes: fixed in b5649. Junglewise finds every place you run it, drafts the upgrade and applies it once you approve.

How is CVE-2025-71439 attacked?

It is attacked with local access, in a software library.

Am I affected by CVE-2025-71439?

Check whether you run Ggml-Org Llama.Cpp at an affected version. Junglewise does that check for you around the clock: connect your code, cloud and domains, and it matches new vulnerabilities like this one against what you actually run and tells you when one reaches you.

CVE-2025-71439: llama.cpp heap out-of-bounds read in tokenizer loading

CVE-2025-71439 · Severity: medium · CVSS 5.5 · Published 2026-10-27

llama.cpp is a C++ implementation of the Llama language model that powers AI inference applications. A crafted GGUF model file with oversized token IDs can trigger an out-of-bounds memory read when loading the tokenizer, crashing the llama-cli or llama-server applications that process the model.

Technologies: Ggml-Org Llama.Cpp. Vendors: Ggml-Org.

Quick answers

Is CVE-2025-71439 exploited in the wild?: No. Junglewise Threat Intelligence has no report of CVE-2025-71439 being exploited in the wild.
How severe is CVE-2025-71439?: Medium severity, with a CVSS score of 5.5, published 27 October 2026.
Which products and versions are affected?: Ggml-Org Llama.Cpp before b5649.
Is there a fix for CVE-2025-71439?: Yes: fixed in b5649. Junglewise finds every place you run it, drafts the upgrade and applies it once you approve.
How is CVE-2025-71439 attacked?: It is attacked with local access, in a software library.
Am I affected by CVE-2025-71439?: Check whether you run Ggml-Org Llama.Cpp at an affected version. Junglewise does that check for you around the clock: connect your code, cloud and domains, and it matches new vulnerabilities like this one against what you actually run and tells you when one reaches you.

How to fix

Fixed in: b5649

Technical details

A heap out-of-bounds read vulnerability exists in the llama_vocab::impl::print_info() function when processing GGUF model files. An attacker can supply a crafted model with a short tokenizer.ggml.tokens array but large special token IDs, causing the code to read past the id_to_token buffer boundary. This results in a crash (denial of service) during model loading in llama-cli or llama-server.

Affected products

ggml-org llama.cpp before b5649

Timeline

2026-10-27: disclosed

References

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