Meta Llama Framework Vulnerability Allows Remote Code Execution

2025-01-27

Learn about the risks of unpatched AI frameworks and how CloudGuard can help protect against remote code execution vulnerabilities.

A vulnerability in Meta's Llama large language model framework could allow attackers to execute arbitrary code on the inference server by exploiting a flaw in the deserialization process. This issue affects the Llama Stack component, specifically the reference Python Inference API implementation, which uses the unsafe pickle library to deserialize data. If the ZeroMQ socket is exposed, attackers could send malicious objects to execute code on the host machine.

Key Facts

Risks:

API Vulnerability, Open Source, Other: Remote Code Execution

Keywords:

Meta, Llama, Remote Code Execution, Vulnerability, AI Security, CVE-2024-50050

CVE:

CVE-2024-50050

Affected:

Meta, Llama, Llama Stack, Python Inference API, ZeroMQ

Article Body

Meta's Llama Framework Vulnerability: A Risk to AI Systems

Meta's Llama large language model (LLM) framework has a critical security flaw that could potentially allow attackers to execute arbitrary code on the Llama inference server. This vulnerability, identified as CVE-2024-50050, poses a significant risk due to its capability of remote code execution.

Vulnerability Details

The issue is rooted in a component called Llama Stack, which provides API interfaces for developing AI applications using Meta's Llama models. The flaw lies in the deserialization process of the reference Python Inference API implementation. It uses Python's pickle library, which is known to be unsafe when deserializing untrusted data, as it can lead to arbitrary code execution.

Exploitation Path

In cases where the ZeroMQ socket is exposed over a network, the vulnerability can be exploited. An attacker can send crafted malicious objects to the socket. Since the recv_pyobj function automatically unpickles these objects, this can lead to arbitrary code execution on the host machine running the Llama inference server.

Implications and Recommendations

This vulnerability highlights the importance of securing AI frameworks against deserialization attacks. Organizations using the Llama framework should ensure that their servers are not exposed to untrusted networks and consider implementing additional security controls to mitigate this risk.

Read More

https://thehackernews.com/2025/01/metas-llama-framework-flaw-exposes-ai.html?m=1