Python dataclasses: static analysis warning about fields mutable default values

Inside dataclasses, fields with mutable default values are quite tricky. They suffer the same issue as mutable default arguments

Consider the following code:

from typing import List
from dataclasses import dataclass


@dataclass
class Inner:
    name: str = ""


@dataclass
class Foo:
    inner: Inner = Inner()
    items: List[str] = []


foo1 = Foo()
foo1.inner.name = "Hello"
foo1.items.append("World")
print(f"{foo1.inner.name=} {foo1.items=}")

foo2 = Foo()
print(f"{foo2.inner.name=} {foo2.items=}")

This will print:

foo1.inner.name='Hello' foo1.items=['World']
foo2.inner.name='Hello' foo2.items=['World']

i.e. Foo members default values are mutable and shared between all instances!

So, my question is:

Is there any static analysis tool and or tool configuration that can reliably warn against dataclasses fields using mutable default values?

Here is the current status I found:

• Concerning the member that was built via a function/constructor call ("inner: Inner = Inner()"): mypy, pyright, pylint, flake8-bugbear, CLion(and probably pycharm) will miss it

• Concerning the member that is a list ("items: List[str] = []"): CLion (and probably pycharm) will catch it. It is missed by mypy, pyright, pylint, flake8-bugbear

Note: I already asked a similar question concerning function parameters with mutable default argument and found a static analysis tool that can detect them (I selected flake8-bugbear)