CVE-2024-47900 - Unauthorized GPU Access Vulnerability: Exploiting Improper GPU System Calls for OOB Kernel Memory Access

In the ever-evolving world of digital security, new vulnerabilities are discovered and exploited every day. One such vulnerability, identified as CVE-2024-47900, has been found to exist within existing GPU system calls being performed by non-privileged user software. The consequences of this vulnerability can be quite severe, potentially allowing for unauthorized access to sensitive kernel memory. In this long-read post, we will provide an in-depth analysis of this flaw, discuss its potential impact, and explore possible mitigations.

Description

This vulnerability arises due to improper GPU system calls being made by software running with non-privileged user permissions. As a result, a threat actor can potentially gain unauthorized access to out-of-bounds (OOB) kernel memory, which can lead to security risks like the leakage of sensitive kernel data or even more severe consequences, such as elevation of privileges or execution of arbitrary code. For more information on the original discovery of this vulnerability, please refer to this official reference.

Technical Overview

Let us delve deeper into the technical aspects and exploitable details of this vulnerability. When a non-privileged user installs and runs software that utilizes GPU system calls to access kernel memory, certain parameters may not be handled correctly. Here is a simple illustration of problematic code that uses an improper GPU system call:

#include <iostream>
#include <cuda.h>
#include <cuda_runtime.h>

using namespace std;

int main()
{
  // Allocate device pointers for GPU
  int *device_ptr_A, *device_ptr_B;
  cudaError_t err;

  // Allocate memory on the GPU
  err = cudaMalloc(&device_ptr_A, 1024 * sizeof(int));
  if (err != cudaSuccess)
  {
    cerr << "cudaMalloc error: " << cudaGetErrorString(err) << endl;
    return 1;
  }

  // Perform an improper GPU system call
  err = gpuSystemCall(device_ptr_A, device_ptr_B);
  if (err != cudaSuccess)
  {
    cerr << "gpuSystemCall error: " << cudaGetErrorString(err) << endl;
    return 1;
  }

  return ;
}

In the above code, gpuSystemCall is a placeholder for a specific function call which triggers the vulnerability. It is important to note that the actual exploit would be more complex than the presented code but serves as a simplified example to illustrate a possible scenario. The improper call made by the function allows for an unauthorized GPU driver to gain access to kernel memory in an unintended manner, opening up a potential exploit for attackers to gather sensitive information or manipulate kernel memory.

Possible Exploits and Associated Risks

There are multiple exploitation possibilities with this vulnerability, which can lead to a wide range of security risk scenarios. Some examples include, but are not limited to:

1. Leakage of sensitive kernel data: Since the vulnerability enables unauthorized access to kernel memory, sensitive kernel data can be extracted, which can lead to data exfiltration or additional security vulnerabilities.
2. Privilege escalation: An attacker could leverage this vulnerability to elevate their non-privileged user permissions and obtain administrative control of the affected system.
3. Execution of arbitrary code: By modifying the kernel memory, an attacker could potentially carry out arbitrary code execution, giving them the power to wreak havoc on the compromised system.

Mitigation

A patch or fix for this vulnerability should be developed by the concerned GPU vendor(s) in collaboration with the original discoverers and affected system vendors. End-users should be vigilant and keep their systems updated with the latest security patches. The following are some recommendations for mitigating this vulnerability:

Implement proper access control mechanisms and follow the principle of least privilege.

3. Perform regular security audits and vulnerability scanning on systems to identify and rectify security flaws.

Conclusion

CVE-2024-47900 hangs as a possible risk over many systems around the globe. Due to the nature of how software is often installed and run by non-privileged users, along with the prevalence of GPU-accelerated applications, this is a vulnerability that cannot be taken lightly. It is important for all stakeholders, including GPU vendors, cybersecurity researchers, and end-users, to work together in addressing and mitigating this security threat. Reducing the risk of unauthorized access to kernel memory requires greater vigilance and a continued dedication to cybersecurity best practices.

Timeline

Published on: 01/31/2025 04:15:08 UTC
Last modified on: 03/14/2025 16:15:36 UTC