CVE-2024-21836 in GGUF
Summary
by MITRE • 02/26/2024
A heap-based buffer overflow vulnerability exists in the GGUF library header.n_tensors 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-2024-21836 resides within the GGUF library implementation of the llama.cpp project, specifically affecting the header.n_tensors functionality. This vulnerability manifests when processing specially crafted .gguf files that contain malformed tensor count values, creating a condition where the application fails to properly validate input data before attempting to allocate memory on the heap. The flaw stems from inadequate bounds checking mechanisms that allow an attacker to manipulate the tensor count field in the GGUF file header, leading to excessive memory allocation requests that exceed the intended buffer boundaries. The vulnerability is particularly concerning as it exists in a widely used machine learning inference library that processes model files for large language models, making it a prime target for attackers seeking to compromise systems running these applications.
The technical exploitation of this vulnerability follows a classic heap overflow pattern where the attacker crafts a malicious GGUF file containing an inflated n_tensors value that, when processed by the vulnerable llama.cpp implementation, causes the heap allocator to allocate insufficient memory for the expected tensor data structures. This memory corruption occurs during the header parsing phase where the application attempts to read and process tensor metadata without proper validation of the tensor count field. The flaw is classified as a heap-based buffer overflow under CWE-122, which specifically addresses heap memory allocation vulnerabilities where insufficient bounds checking allows for memory corruption. When the application attempts to write beyond the allocated heap buffer boundaries, it can overwrite adjacent memory regions including function pointers, return addresses, or other critical data structures, potentially enabling arbitrary code execution.
The operational impact of this vulnerability extends beyond simple memory corruption as it represents a critical security risk for any system that processes GGUF model files, particularly in server environments where model files may be downloaded from untrusted sources or received through network transfers. Attackers can leverage this vulnerability to execute arbitrary code on vulnerable systems with the privileges of the running process, potentially leading to complete system compromise. The vulnerability affects the broader machine learning ecosystem since llama.cpp is commonly used in production environments, research deployments, and containerized applications where model file processing occurs automatically. This makes the attack surface particularly wide, as the vulnerability can be triggered through various attack vectors including web-based file uploads, automated model downloading processes, or even through compromised model repositories that serve malicious GGUF files to unsuspecting users.
Mitigation strategies for CVE-2024-21836 should prioritize immediate patching of the affected llama.cpp library version, specifically targeting the commit 18c2e17 that introduced this vulnerability. Organizations should implement strict input validation measures that enforce reasonable limits on tensor count values within GGUF file headers, implementing proper bounds checking that prevents excessive memory allocation requests. The defensive approach should include runtime protections such as heap canaries, address space layout randomization, and stack canaries to detect and prevent exploitation attempts. Additionally, security teams should deploy automated file validation systems that scan GGUF files for suspicious tensor count values and implement network-based intrusion detection rules that monitor for patterns consistent with this vulnerability. From an ATT&CK framework perspective, this vulnerability maps to T1059.007 (Command and Scripting Interpreter: PowerShell) and T1548.001 (Abuse Elevation Control Mechanism: Setuid and Setgid) as attackers may attempt to escalate privileges through code execution, while the initial compromise typically follows T1190 (Exploit Public-Facing Application) through the processing of malicious model files. Organizations should also consider implementing network segmentation and file integrity monitoring solutions to prevent unauthorized GGUF file processing and maintain audit trails of model file handling activities.