NVIDIA NeMo Framework 在所有平台上包含一个漏洞，该漏洞存在于 NLP 组件中，攻击者创建的恶意数据可能导致代码注入问题。成功利用此漏洞

The impact of this vulnerability is significant. Successful exploitation could allow an attacker to execute arbitrary code on the system running the NVIDIA NeMo Framework. This could lead to a complete compromise of the system, including privilege escalation, allowing the attacker to gain administrative access. Furthermore, the attacker could potentially steal sensitive data or tamper with existing data, leading to data breaches and operational disruptions. The NLP component's role in processing natural language data makes it a prime target for attackers seeking to inject malicious code and manipulate the framework's behavior.

Organizations utilizing the NVIDIA NeMo Framework for natural language processing tasks, particularly those deploying it in production environments or handling sensitive data, are at risk. Those relying on older, unpatched versions of the framework are especially vulnerable. Researchers and developers working with NeMo should also be aware of this vulnerability and ensure their environments are secure.

• python / server:

import os
import subprocess
# Check for NeMo version
result = subprocess.run(['pip', 'show', 'nvidia-nemo'], capture_output=True, text=True)
if 'Version:' in result.stdout:
    version = result.stdout.split('Version:')[1].strip().split('\n')[0]
    if version <= '2.4.0':
        print('Vulnerable NeMo version detected!')

• generic web: Monitor access logs for unusual requests targeting the NLP component or related endpoints. Look for patterns indicative of input injection attempts. • generic web: Check response headers for unexpected content or error messages that might indicate a vulnerability exploitation.

1. 2025-08-26Disclosure

   disclosure

1. 已保留2025-01-14
2. 发布日期2025-08-26
3. 修改日期2026-02-26
4. EPSS 更新日期2026-03-23

The primary mitigation for CVE-2025-23313 is to upgrade to NVIDIA NeMo Framework version 2.4.0 or later. If upgrading is not immediately feasible, consider implementing input validation and sanitization techniques within your NLP pipelines to prevent the injection of malicious data. Review and restrict access to the NLP component, limiting who can provide input data. Monitor system logs for unusual activity or errors related to the NLP component, which could indicate an attempted exploit. After upgrading, confirm the fix by attempting to reproduce the vulnerability with known malicious input and verifying that it is no longer exploitable.