How can LLMs aid or even emulate hackers? Threat actors are already using LLMs, creating the danger that defenders will not be prepared for this new threat.

We aim to become THE framework for testing LLM-based agents for security testing. To create common ground truth, we strive to create common security testbeds and benchmarks, evaluate multiple LLMs and techniques against those, and publish our prototypes and findings as open-source/open-access reports.

We strive to make our code-base as accessible as possible to allow for easy experimentation. Our experiments are structured into use-cases, e.g., privilege escalation attacks. A researcher wanting to create a new experiment would just create a new use-case that mostly consists of the control loop and corresponding prompt templates. We provide multiple helper and base classes, so that a new experiment can be implemented in a few dozens lines of code as connecting to the LLM, logging, etc. is taken care of by our framework. For further information (esp. if you want to contribute use-cases), please take a look at docs/use_case.md.

Our initial forays were focused upon evaluating the efficiency of LLMs for linux privilege escalation attacks and we are currently breaching out into evaluation the use of LLMs for web penetration-testing and web api testing.

We release all tooling, testbeds and findings as open-source as this is the only way that comprehensive information will find their way to defenders. APTs have access to more sophisticated resources, so we are only leveling the playing field for blue teams. For information about the implementation, please see our implementation notes. All source code can be found on github.

How are we doing this? The initial tool wintermute targets linux priv-esc attacks. It uses SSH to connect to a (presumably) vulnerable virtual machine and then asks OpenAI GPT to suggest linux commands that could be used for finding security vulnerabilities or privilege escalation. The provided command is then executed within the virtual machine, the output fed back to the LLM and, finally, a new command is requested from it..

• connects over SSH (linux targets) or SMB/PSExec (windows targets)
• supports OpenAI REST-API compatible models (gpt-3.5-turbo, gpt4, gpt-3.5-turbo-16k, etc.)
• supports locally running LLMs
• beautiful console output
• logs run data through sqlite either into a file or in-memory
• automatic root detection
• can limit rounds (how often the LLM will be asked for a new command)

This is a simple example run of wintermute.py using GPT-4 against a vulnerable VM. More example runs can be seen in our collection of historic runs.

Some things to note:

• initially the current configuration is output. Yay, so many colors!
• "Got command from LLM" shows the generated command while the panel afterwards has the given command as title and the command's output as content.
• the table contains all executed commands. ThinkTime denotes the time that was needed to generate the command (Tokens show the token count for the prompt and its response). StateUpdTime shows the time that was needed to generate a new state (the next column also gives the token count)
• "What does the LLM know about the system?" gives an LLM generated list of system facts. To generate it, it is given the latest executed command (and it's output) as well as the current list of system facts. This is the operation which time/token usage is shown in the overview table as StateUpdTime/StateUpdTokens. As the state update takes forever, this is disabled by default and has to be enabled through a command line switch.
• Then the next round starts. The next given command (sudo tar) will lead to a pwn'd system BTW.

The following would create a new (minimal) linux privilege-escalation agent. Through using our infrastructure, this already uses configurable LLM-connections (e.g., for testing OpenAI or locally run LLMs), logs trace data to a local sqlite database for each run, implements a round limit (after which the agent will stop if root has not been achieved until then) and is able to connect to a linux target over SSH for fully-autonomous command execution (as well as password guessing).

template_dir = pathlib.Path(__file__).parent / "templates"
template_next_cmd = Template(filename=str(template_dir / "query_next_command.txt"))

@use_case("linux_privesc", "Linux Privilege Escalation")
@dataclass
class LinuxPrivesc(RoundBasedUseCase, UseCase, abc.ABC):

    system: str = 'linux'
    
    _sliding_history: SlidingCliHistory = None
    _capabilities: Dict[str, Capability] = field(default_factory=dict)

    def init(self):
        super().init()
        # provide a shell history of limited size to the LLM
        self._sliding_history = SlidingCliHistory(self.llm)

    def perform_round(self, turn):
        got_root : bool = False

        with self.console.status("[bold green]Asking LLM for a new command..."):
            answer = self.get_next_command()
        cmd = answer.result

        with self.console.status("[bold green]Executing that command..."):
            if answer.result.startswith("test_credential"):
                result, got_root = self._capabilities["test_credential"](cmd)
            else:
                self.console.print(Panel(answer.result, title="[bold cyan]Got command from LLM:"))
                result, got_root = self._capabilities["run_command"](cmd)

        # log and output the command and its result
        self.log_db.add_log_query(self._run_id, turn, cmd, result, answer)
        self._sliding_history.add_command(cmd, result)
        self.console.print(Panel(result, title=f"[bold cyan]{cmd}"))

        # Output Round Data..
        self.console.print(ui.get_history_table(False, False, self._run_id, self.log_db, turn))

        # if we got root, we can stop the loop
        return got_root

    def get_next_command(self):
        template_size = self.llm.count_tokens(template_next_cmd.source)
        history = self._sliding_history.get_history(self.llm.context_size - llm_util.SAFETY_MARGIN - template_size)

        cmd = self.llm.get_response(template_next_cmd, _capabilities=self._capabilities, history=history, conn=self.conn, system=self.system, target_user="root")
        cmd.result = llm_util.cmd_output_fixer(cmd.result)
        return cmd

hackingBuddyGPT is described in Getting pwn'd by AI: Penetration Testing with Large Language Models :

@inproceedings{Happe_2023, series={ESEC/FSE ’23},
   title={Getting pwn’d by AI: Penetration Testing with Large Language Models},
   url={http://dx.doi.org/10.1145/3611643.3613083},
   DOI={10.1145/3611643.3613083},
   booktitle={Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering},
   publisher={ACM},
   author={Happe, Andreas and Cito, Jürgen},
   year={2023},
   month=nov, collection={ESEC/FSE ’23}
}

Preliminary results for the linux privilege escalation use-case can be found in Evaluating LLMs for Privilege-Escalation Scenarios:

@misc{happe2023evaluating,
      title={Evaluating LLMs for Privilege-Escalation Scenarios}, 
      author={Andreas Happe and Aaron Kaplan and Jürgen Cito},
      year={2023},
      eprint={2310.11409},
      archivePrefix={arXiv},
      primaryClass={cs.CR}
}

This work is partially based upon our empiric research into how hackers work:

@inproceedings{Happe_2023, series={ESEC/FSE ’23},
   title={Understanding Hackers’ Work: An Empirical Study of Offensive Security Practitioners},
   url={http://dx.doi.org/10.1145/3611643.3613900},
   DOI={10.1145/3611643.3613900},
   booktitle={Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering},
   publisher={ACM},
   author={Happe, Andreas and Cito, Jürgen},
   year={2023},
   month=nov, collection={ESEC/FSE ’23}
}

We try to keep our python dependencies as light as possible. This should allow for easier experimentation. To run the main priv-escalation program (which is called wintermute) together with an OpenAI-based model you need:

1. an OpenAI API account, you can find the needed keys in your account page
   • please note that executing this script will call OpenAI and thus charges will occur to your account. Please keep track of those.
2. a potential target that is accessible over SSH. You can either use a deliberately vulnerable machine such as Lin.Security.1 or a security benchmark such as our own priv-esc benchmark.

To get everything up and running, clone the repo, download requirements, setup API-keys and credentials and start wintermute.py:

# clone the repository
$ git clone https://github.com/andreashappe/hackingBuddyGPT.git
$ cd hackingBuddyGPT

# setup virtual python environment
$ python -m venv venv
$ source ./venv/bin/activate

# install python requirements
$ pip install -r requirements.txt

# copy default .env.example
$ cp .env.example .env

# IMPORTANT: setup your OpenAI API key, the VM's IP and credentials within .env
$ vi .env

# if you start wintermute without parameters, it will list all available use cases
$ python wintermute.py
usage: wintermute.py [-h] {linux_privesc,windows privesc} ...
wintermute.py: error: the following arguments are required: {linux_privesc,windows privesc}

# start wintermute, i.e., attack the configured virtual machine
$ python wintermute.py linux_privesc --enable_explanation true --enable_update_state true

Please note and accept all of them.

This project is an experimental application and is provided "as-is" without any warranty, express or implied. By using this software, you agree to assume all risks associated with its use, including but not limited to data loss, system failure, or any other issues that may arise.

The developers and contributors of this project do not accept any responsibility or liability for any losses, damages, or other consequences that may occur as a result of using this software. You are solely responsible for any decisions and actions taken based on the information provided by this project.

Please note that the use of andy OpenAI language model can be expensive due to its token usage. By utilizing this project, you acknowledge that you are responsible for monitoring and managing your own token usage and the associated costs. It is highly recommended to check your OpenAI API usage regularly and set up any necessary limits or alerts to prevent unexpected charges.

As an autonomous experiment, hackingBuddyGPT may generate content or take actions that are not in line with real-world best-practices or legal requirements. It is your responsibility to ensure that any actions or decisions made based on the output of this software comply with all applicable laws, regulations, and ethical standards. The developers and contributors of this project shall not be held responsible for any consequences arising from the use of this software.

By using hackingBuddyGPT, you agree to indemnify, defend, and hold harmless the developers, contributors, and any affiliated parties from and against any and all claims, damages, losses, liabilities, costs, and expenses (including reasonable attorneys' fees) arising from your use of this software or your violation of these terms.

Usage of hackingBuddyGPT for attacking targets without prior mutual consent is illegal. It's the end user's responsibility to obey all applicable local, state and federal laws. Developers assume no liability and are not responsible for any misuse or damage caused by this program. Only use for educational purposes.