CVE-2026-27940 is a heap-based buffer overflow vulnerability identified in the llama.cpp project maintained by ggml-org, which is a C/C++ implementation for inference of large language models (LLMs). The vulnerability exists in the function gguf_init_from_file_impl() within gguf.cpp prior to commit b8146. The root cause is an integer overflow (CWE-190) that leads to an undersized heap allocation. Subsequently, the function fread() reads and writes at least 528 bytes of attacker-controlled data beyond the allocated buffer boundary, resulting in a heap overflow (CWE-122). This flaw effectively bypasses a prior patch for a similar vulnerability (CVE-2025-53630) because the fix did not cover all vulnerable code paths. The vulnerability requires local access (AV:L) and no privileges (PR:N), but user interaction (UI:R) is necessary to trigger the overflow. The impact is severe, allowing an attacker to potentially execute arbitrary code, corrupt memory, or cause denial of service, affecting confidentiality, integrity, and availability. The vulnerability has a CVSS v3.1 base score of 7.8, reflecting its high severity. Although no exploits are currently known in the wild, the vulnerability is critical for users of llama.cpp versions prior to b8146. The issue was publicly disclosed on March 12, 2026, and fixed in the b8146 commit. The vulnerability highlights the importance of thorough patch validation to avoid incomplete fixes.

The impact of CVE-2026-27940 on organizations worldwide is significant due to the widespread adoption of llama.cpp for running LLM inference locally or in enterprise environments. Successful exploitation can lead to arbitrary code execution, enabling attackers to compromise system confidentiality by accessing sensitive data processed by the model, integrity by altering model behavior or outputs, and availability by crashing or destabilizing the application. Since llama.cpp is often integrated into AI workflows, compromised systems could be used as pivot points for lateral movement or data exfiltration. The requirement for local access and user interaction limits remote exploitation but does not eliminate risk, especially in environments where untrusted users have local machine access or where malicious files are opened. Organizations relying on vulnerable versions risk operational disruption, intellectual property theft, and potential regulatory compliance violations if sensitive data is exposed. The incomplete fix from the prior CVE also suggests a need for careful code auditing and patch management to prevent similar oversights.

To mitigate CVE-2026-27940, organizations should immediately update llama.cpp to version b8146 or later, where the vulnerability is fully patched. Users should audit their deployment pipelines to ensure no legacy versions remain in use. Implement strict local access controls to limit who can execute or interact with llama.cpp binaries, reducing the risk of exploitation via user interaction. Employ application whitelisting and endpoint protection to detect anomalous behavior indicative of exploitation attempts. Conduct thorough code reviews and fuzz testing on components handling untrusted input to identify similar integer overflow and buffer overflow issues proactively. Additionally, sandbox llama.cpp execution environments to contain potential compromises and monitor logs for unusual fread() or memory allocation errors. Educate users about the risks of opening untrusted files or inputs that could trigger the vulnerability. Finally, maintain an up-to-date inventory of AI inference tools and their versions to facilitate rapid response to future vulnerabilities.