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carbon's Introduction

from GitHub import ReadMe

class osiris(ReadMe):
    def __init__(self):
        self.username = "0sir1s"
        self.name = "James"
        self.language = "English"
        self.used_programming_languages = ["Python", "Javascript", "Typescript", "C++"]
        self.location = "United Kingdom"
        self.education = "2nd Year of Computer Science"

    def about(self):
        print(f"""
Hi, I'm {self.name}, or {self.username} online.
In my free time I create projects to help deepen my understanding of code. I find the more you build, the faster you learn.
I have previously used the following programming languages ordered from most to least used: {', '.join(i for i in self.used_programming_languages)}.
You may find some of these projects on my GitHub profile.""")

me = osiris()
me.about()

carbon's People

Contributors

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carbon's Issues

Improperly renames function parameters

We have the original code here:

def add(x: int, y: int) -> None: print(f"{x:,} + {y:,} = {(x+y):,}") add(10, 10)

after obfuscation

`
def lIIlllIIIlIllI(llIIlllIllIl: int, IllIIIlIllIl: int) -> None:
print(f"{x:,} + {y:,} = {(llIIlllIllIl+IllIIIlIllIl):,}")
lIIlllIIIlIllI(10, 10)

`

Probably not safe against LLM de-obfuscating

Seems like language models do a really decent job in de-obfuscating the generated code (same holds for other obfuscating tools, not Carbon in particular). Didn't test it in all any depth, but GPT-3.5 give great hints when fead with a function from the example folder: https://chat.openai.com/share/0dd8d626-4de1-4de4-af79-d9acbd66c7b5

So, be careful when you use it against important stuff. If larger code bases are used, at least for now, the limited context length of LLMs may give a bit of protection.

Replacing names

Names in strings or default parameter values are also replaced.
e.g.
def func(arg1="arg1"):
print("arg1 is", arg1)
will be replaced with
def func(llIlIIllIIIlIllll='llIlIIllIIIlIllll'):
print("llIlIIllIIIlIllll is", llIlIIllIIIlIllll)

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