CVE-2026-43627 (GCVE-0-2026-43627)

Vulnerability from cvelistv5 – Published: 2026-08-06 15:40 – Updated: 2026-08-14 16:49 X_Open Source
VLAI
Title
llama.cpp b1283–b9058 Integer Overflow in llama_batch_init() Function
Summary
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.
SSVC
Exploitation: none Automatable: no Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-08-08 02:07 UTC
CWE
  • CWE-190 - Integer Overflow or Wraparound
  • CWE-680 - Integer Overflow to Buffer Overflow
Assigner
VulnCheck CNA under the mitre root
CNA scorecard B 84/100 over 7010 records in the last 180 days details
Impacted products
Vendor Product Version CPE status
ggml-org llama.cpp Affected: b1283 , ≤ b9058 (custom)
Affected: 0.4.0 , ≤ 0.17.1 (semver)
    cpe:2.3:a:ggml:llama.cpp:*:*:*:*:*:*:*:*
    cpe:2.3:a:ggml:llama.cpp:*:*:*:*:*:*:*:*
Create a notification for this product.
Date Public
2026-05-06 00:00
Show details on NVD website

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  • Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
  • Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
  • Not confirmed: The user expressed doubt about the validity of the vulnerability.
  • Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.
Vulnerability timeline

Sightings and recorded dates, grouped by day · UTC

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Events included in the timeline

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    Related by attack behaviour

    Vulnerabilities whose description is nearest to this one in the vector space of the CIRCL/vulnerability-attack-technique-biencoder model. This is a similarity search over the bi-encoder space (plain cosine), not a classification, and it has no measured accuracy.