CVE-2024-21836 is a critical heap-based buffer overflow vulnerability that affects the header.n_tensors functionality of GGUF Library's llama.cpp file. This issue exists in commit 18c2e17, and it exposes affected systems to various kinds of cyber attacks, including remote code execution. An attacker merely needs to provide a malicious .gguf file to exploit this vulnerability successfully. In this blog post, we will delve into the details of this security flaw and demonstrate how it could be exploited on your system.

Vulnerability Background

Before we proceed, it's crucial to understand the GGUF library and its role in manipulating and analyzing .gguf files. The library is widely used in scientific computing communities to facilitate the parsing and analysis of complex multi-dimensional tensor data. The header.n_tensors function, specifically, is responsible for managing the allocation and manipulation of these tensors. However, an oversight in this function renders the library vulnerable to a carefully crafted .gguf file.

To fully appreciate the ramifications of this vulnerability, we must recognize its underlying cause— a heap-based buffer overflow. This occurs when a program inadvertently writes data beyond the buffer's allocated memory space, causing an overflow into adjacent memory areas. If an attacker can manipulate the buffer to overwrite critical system data, it can have severe consequences, such as unauthorized access to the system or running arbitrary code.

The following code snippet illustrates the vulnerable code segment in llama.cpp

´´´cpp

include "header.h"

void read_tensors(FILE* file, n_tensors* header) {
// ...

header->tensors[i] = (tensor*) malloc(X_size * Y_size * Z_size * sizeof(tensor));

// ...
}
}
´´´

In the above code snippet, the library dynamically allocates memory to store tensor data in the heap based on the dimensions specified in the header. While this may seem innocuous at first glance, the issue arises when an attacker supplies a specially crafted .gguf file containing a malicious header. This header could contain exceptionally large dimensions for the tensors, forcing the library to allocate a massive amount of memory and causing a heap-based buffer overflow. Consequently, this vulnerability allows an attacker to overwrite crucial system data and gain control of the program's execution flow, leading to code execution.

To demonstrate this vulnerability, let's consider a malicious .gguf file with the following header

´´´
.n_tensors: 2
.dim-: 300000000 300000000 300000000
.dim-1: 42 42 42
´´´

This file specifies two tensors, one of which has dimensions significantly larger than the expected range. The library, not conducting any input validation, will attempt to allocate memory according to these dimensions, triggering a heap-based buffer overflow. An attacker could then strategically manipulate the buffer to overwrite crucial system data, take control of the program, and execute arbitrary code.

Mitigation

To mitigate the CVE-2024-21836 vulnerability, the GGUF library developers should consider implementing input validation checks and enforce reasonable limits on tensor dimensions. Additionally, secure coding practices, such as employing memory-safe APIs and runtime security measures, should be adopted.

For more information on CVE-2024-21836, please refer to the following sources

- National Vulnerability Database (NVD)
- Exploit Database
- GitHub Repository

Conclusion

CVE-2024-21836 is a critical security flaw that highlights the importance of validating user input and adhering to secure coding practices. System administrators and developers who work with the GGUF library should be aware of this vulnerability and consider applying the necessary patches to ensure the security of their systems.

Timeline

Published on: 02/26/2024 16:27:55 UTC
Last modified on: 02/26/2024 18:15:07 UTC