CVE-2024-21802 in GGUF
Summary
by MITRE • 02/26/2024
A heap-based buffer overflow vulnerability exists in the GGUF library info->ne functionality of llama.cpp Commit 18c2e17. A specially crafted .gguf file can lead to code execution. An attacker can provide a malicious file to trigger this vulnerability.
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Analysis
by VulDB Data Team • 02/13/2025
The heap-based buffer overflow vulnerability identified as CVE-2024-21802 resides within the GGUF library implementation of the llama.cpp project, specifically affecting the info->ne functionality. This vulnerability emerges from improper input validation and memory management practices within the library's handling of GGUF format files, which are commonly used for storing machine learning model data. The flaw manifests when processing specially crafted .gguf files that contain malformed metadata structures, particularly within the tensor information section that defines the neural network's architecture and parameters.
The technical root cause of this vulnerability stems from inadequate bounds checking during the parsing of tensor dimension information within the GGUF file format. When the library processes the ne field, which represents tensor dimensions, it fails to validate the size parameters against allocated memory boundaries. This allows an attacker to craft a .gguf file with excessively large dimension values that exceed the allocated heap buffer space, resulting in memory corruption that can be exploited for arbitrary code execution. The vulnerability is classified as a heap-based buffer overflow under CWE-121, which specifically addresses buffer overflow conditions in heap memory allocations.
The operational impact of this vulnerability is significant as it enables remote code execution when a victim system processes a maliciously crafted GGUF file through the affected llama.cpp library. Attackers can leverage this vulnerability by delivering a specially constructed .gguf file that, when loaded by applications using the vulnerable library, triggers the buffer overflow condition. This creates a persistent threat vector since GGUF files are commonly used in machine learning model distribution and can be embedded in various application workflows. The vulnerability affects systems that utilize llama.cpp for inference operations or model loading, potentially compromising servers, development environments, and end-user devices that process these files.
Mitigation strategies for CVE-2024-21802 should prioritize immediate patching of the affected llama.cpp library to address the buffer overflow in the GGUF parsing functionality. Organizations should implement strict input validation controls and file integrity checks for all GGUF files before processing, particularly in environments where external file uploads or model distribution occurs. The vulnerability can be addressed through memory bounds checking mechanisms and proper validation of tensor dimension parameters, ensuring that all input values fall within acceptable ranges before memory allocation occurs. Security teams should also consider implementing application whitelisting and sandboxing techniques to limit the potential impact of exploitation attempts. This vulnerability aligns with ATT&CK technique T1059.007 for command and scripting interpreter and T1203 for Exploitation for Client Execution, emphasizing the need for comprehensive defensive measures including network monitoring and endpoint protection systems to detect and prevent exploitation attempts.