CVE-2026-2069 identifies a stack-based buffer overflow vulnerability in the open-source ggml-org llama.cpp project, a lightweight C++ implementation used for running large language models locally. The vulnerability resides in the function llama_grammar_advance_stack within the GBNF Grammar Handler component, specifically in the llama.cpp/src/llama-grammar.cpp file. This function improperly handles input data, allowing an attacker to overflow a stack buffer by manipulating the grammar advancement logic. The flaw requires local access with limited privileges (PR:L) and does not require user interaction (UI:N). The vulnerability can lead to memory corruption, which might be leveraged to crash the application or potentially execute arbitrary code, although no direct code execution is confirmed. The CVSS 4.8 score reflects a medium severity, considering the local attack vector and limited privileges required. The vulnerability was published on February 6, 2026, and a patch (18993) has been released to address the issue. While no active exploitation has been reported, the existence of a public exploit increases the risk of attacks in environments where llama.cpp is deployed. The vulnerability primarily impacts developers and organizations using llama.cpp for AI model inference or development, especially where local users have access to the system. Given the growing adoption of llama.cpp in AI research and applications, this vulnerability poses a tangible risk if left unpatched.

For European organizations, the impact of CVE-2026-2069 is primarily related to the integrity and availability of systems running llama.cpp. Since the vulnerability allows a local attacker to cause a stack-based buffer overflow, it can lead to application crashes (denial of service) or potentially enable privilege escalation or arbitrary code execution if combined with other vulnerabilities. This could disrupt AI model inference workflows, development environments, or any services relying on llama.cpp. Confidentiality impact is limited as the vulnerability does not directly expose data. However, the disruption of AI services could affect business operations, research productivity, and service reliability. Organizations with multi-user environments or shared development machines are at higher risk, as local attackers could exploit the flaw to compromise systems. The medium severity rating suggests moderate urgency, but the availability of a public exploit means European entities should act quickly to patch. Failure to mitigate could lead to targeted attacks in AI research institutions, tech companies, or any organization integrating llama.cpp into their AI toolchains.

1. Apply the official patch (18993) released by ggml-org immediately to all affected versions of llama.cpp to remediate the vulnerability. 2. Restrict local access to systems running llama.cpp to trusted users only, minimizing the risk of local exploitation. 3. Implement strict user privilege management and sandboxing to limit the impact of any local exploit attempts. 4. Monitor systems for unusual crashes or behavior in applications using llama.cpp, which may indicate exploitation attempts. 5. Employ runtime protections such as stack canaries, address space layout randomization (ASLR), and control flow integrity (CFI) where possible to mitigate exploitation impact. 6. Educate developers and system administrators about the vulnerability and ensure secure coding practices to prevent similar issues. 7. Regularly update and audit AI development environments to ensure all dependencies, including llama.cpp, are up to date with security patches.