├── AutoCorpus
├── __init__.py
└── auto_corpus.py
├── LICENSE
├── README.md
├── logo.gif
├── requirements.txt
└── setup.py
/AutoCorpus/__init__.py:
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https://raw.githubusercontent.com/user1342/AutoCorpus/0b8e0b9883d542326a5ef9024a22d3c3151fc1a9/AutoCorpus/__init__.py
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/AutoCorpus/auto_corpus.py:
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1 | import re
2 | import argparse
3 | import torch
4 | from rich.console import Console
5 | from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
6 | import os
7 | import random
8 | import time
9 |
10 | class AutoCorpus:
11 | def _load_model(self, model_name, device):
12 | """
13 | Load the pre-trained language model and tokenizer.
14 |
15 | Args:
16 | model_name (str): Name of the pre-trained model.
17 | device (str): Device to load the model onto.
18 |
19 | Returns:
20 | model (transformers.PreTrainedModel): Loaded language model.
21 | tokenizer (transformers.PreTrainedTokenizer): Loaded tokenizer.
22 | """
23 | quantization_config = BitsAndBytesConfig(
24 | load_in_4bit=True,
25 | bnb_4bit_compute_dtype=torch.float16,
26 | bnb_4bit_quant_type="nf4",
27 | bnb_4bit_use_double_quant=True,
28 | )
29 | model = AutoModelForCausalLM.from_pretrained("unsloth/mistral-7b-instruct-v0.2-bnb-4bit", quantization_config=quantization_config)
30 | tokenizer = AutoTokenizer.from_pretrained("unsloth/mistral-7b-instruct-v0.2-bnb-4bit", padding_side="left")
31 | return model, tokenizer
32 |
33 | def _generate_dialogue_response(self, model, tokenizer, device, messages, max_size):
34 | """
35 | Generate response from the language model given the input messages.
36 |
37 | Args:
38 | model (transformers.PreTrainedModel): Loaded language model.
39 | tokenizer (transformers.PreTrainedTokenizer): Loaded tokenizer.
40 | device (str): Device to run the model on.
41 | messages (list): List of input messages.
42 |
43 | Returns:
44 | str: Generated response.
45 | """
46 | encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt")
47 | model_inputs = encodeds.to(device)
48 | generated_ids = model.generate(model_inputs, max_new_tokens=max_size, do_sample=False, pad_token_id=50256)
49 | decoded = tokenizer.batch_decode(generated_ids, skip_special_tokens=False)
50 | return decoded[0]
51 |
52 | def _get_args(self):
53 |
54 | # Create an argument parser with a description
55 | parser = argparse.ArgumentParser(description="A tool for automatically generating initial fuzzing input corpus test cases")
56 |
57 | # Add command line arguments
58 | parser.add_argument("--input_folder", "-i", type=str, help="The input folder to base generated corpus files off. If no prompt is given, the folder needs at least 1 file.")
59 | parser.add_argument("--output_folder", "-o", type=str, help="The folder to save generated corpus files to (will default to input folder).")
60 | parser.add_argument("--number_of_corpus_files", "-n", type=int, default=5, help="The number of corpus files to generate")
61 | parser.add_argument("--prompt", "-p", type= str, help="A sentence defining what the corpus files are for. This helps steer generation.")
62 | parser.add_argument("--size", "-s", type= int, default=500, help="Max size of tokens created by the LLM")
63 | parser.add_argument("--verbose", "-v", action="store_true", help="Provides verbose outputs")
64 |
65 | # Parse the arguments
66 | args = parser.parse_args()
67 |
68 | # Check if the number of corpus files to generate is above 0
69 | if args.number_of_corpus_files < 1:
70 | raise Exception("Number of generated corpus files should be above 0!")
71 |
72 | # Check if neither input folder nor prompt is provided, set input folder to current working directory
73 | if not args.input_folder and not args.prompt:
74 | args.input_folder = os.getcwd()
75 | print("No input or prompt provided, generating prompts from files in CWD: '{}".format(args.input_folder))
76 |
77 |
78 | # If an input folder is provided, validate it and set output folder to input folder
79 | if args.input_folder:
80 | if not os.path.exists(args.input_folder) or not os.path.isdir(args.input_folder):
81 | raise Exception("Input folder: '{}' is not valid dir path!".format(args.input_folder))
82 |
83 | if not args.output_folder:
84 | args.output_folder = args.input_folder
85 |
86 | # If a prompt is provided but no output folder, raise an exception
87 | if args.prompt and not args.output_folder:
88 | raise Exception("You must provide an output folder when not using an input folder!")
89 |
90 | # Check if the output folder is provided and validate it
91 | if args.output_folder:
92 | if not os.path.exists(args.output_folder) or not os.path.isdir(args.output_folder):
93 | raise Exception("Output folder: '{}' is not a valid directory path!".format(args.output_folder))
94 |
95 | # If an input folder is provided but no prompt, check if it contains at least one file
96 | if args.input_folder and not args.prompt:
97 | files = os.listdir(args.input_folder)
98 | if not files:
99 | raise Exception("No prompt given (--prompt) and input corpus 0. Either provide a prompt or an initial corpus file.")
100 |
101 | return args
102 |
103 | def _remove_inst_tags(self, text):
104 | """
105 | Remove instruction tags from the given text.
106 |
107 | Args:
108 | text (str): Input text containing instruction tags.
109 |
110 | Returns:
111 | str: Text with instruction tags removed.
112 | """
113 | pattern = r'\[INST\].*?\[/INST\]'
114 | clean_text = re.sub(pattern, '', text, flags=re.DOTALL)
115 | return clean_text.replace("", "").replace("", "").replace("Explanation:", "").strip()
116 |
117 | def sample_inputs(self, input_folder, number=4):
118 | # Check if the input folder exists
119 | if not os.path.exists(input_folder):
120 | raise FileNotFoundError(f"Input folder '{input_folder}' not found")
121 |
122 | # List all files in the input folder
123 | files = [f for f in os.listdir(input_folder) if os.path.isfile(os.path.join(input_folder, f))]
124 |
125 | # Check if there are enough files to sample
126 | if len(files) < number:
127 | number = len(files)
128 |
129 | # Randomly sample 'number' of files
130 | sampled_files = random.sample(files, number)
131 |
132 | # Read and concatenate contents of sampled files
133 | sampled_contents = ""
134 | for file_name in sampled_files:
135 | file_path = os.path.join(input_folder, file_name)
136 | with open(file_path, 'r') as file:
137 | sampled_contents += file.read()
138 |
139 | return sampled_contents
140 |
141 | def entry(self):
142 | """
143 | Entry point of the program.
144 | """
145 | args = self._get_args()
146 | console = Console()
147 | device = "cuda" if torch.cuda.is_available() else "cpu"
148 | model_name = "mistralai/Mistral-7B-Instruct-v0.1"
149 | model, tokenizer = self._load_model(model_name, device)
150 | console.clear()
151 |
152 | first_run = True
153 | tasks = [f"task {n}" for n in range(1, args.number_of_corpus_files+1)]
154 |
155 | # Generate prompt for status
156 | prompt_text = f"[bold green]Creating {args.number_of_corpus_files} test cases in '{args.output_folder}'"
157 | if args.prompt:
158 | # Input files and prompt
159 | if args.input_folder:
160 | prompt_text = prompt_text + f" from input folder '{args.input_folder}' and prompt '{args.prompt}'."
161 | # No input files and just prompt
162 | else:
163 | prompt_text = prompt_text + f" from prompt '{args.prompt}'."
164 | # Just input files
165 | else:
166 | prompt_text = prompt_text + f" from input folder '{args.input_folder}."
167 |
168 | console.clear()
169 | with console.status(prompt_text) as status:
170 | while tasks:
171 | # Generate question based on inputs
172 | question = f""
173 | if args.prompt:
174 | # Input files and prompt
175 | if args.input_folder:
176 |
177 | question = f"You have been asked to create the contents of a valid file for '{args.prompt}'. To follow are several example of other types of file that you should follow the format of the samples, but make sure that your data is unique to the samples and valid - ensure to use different types, formats, etc when possible. Only return the newly generated file contents and no other information. \n Samples: \n {self.sample_inputs(args.input_folder)}"
178 | # No input files and just prompt
179 | else:
180 | # If second run start using others as inpiration
181 | if first_run:
182 | question = f"You have been asked to create the contents of a valid file for '{args.prompt}'. This file will be used as a corpus and should be a ligitimate version of the file. Only return the newly generated file contents and no other information."
183 | else:
184 | question = f"You have been asked to create the contents of a valid file for '{args.prompt}'. To follow are several example of other types of file that you should follow the format of the samples, but make sure that your data is unique and valid. Only return the newly generated file contents and no other information. \n Samples: \n {self.sample_inputs(args.input_folder)}"
185 |
186 | # Just input files
187 | else:
188 | question = f"You have been asked to create a new corpus file based off the following file samples. The created file should follow the format of the samples, but make sure that your data is unique to the samples and valid - ensure to use different types, formats, etc when possible. Only return the newly generated file contents and no other information. \n Samples: \n {self.sample_inputs(args.input_folder)}"
189 |
190 | question = question + " Only return the file, no additional commentary or explanation!"
191 |
192 | if args.verbose:
193 | console.log(question)
194 |
195 | # Ask LLM for output files
196 | result = self._generate_dialogue_response(model, tokenizer, device, [{"role": "user", "content": question}], args.size)
197 | result = self._remove_inst_tags(result)
198 |
199 | if args.verbose:
200 | console.log(result)
201 |
202 | # Share progress
203 | task = tasks.pop(0)
204 |
205 | # Save results to file
206 | epoch_time = int(time.time()) # Get current epoch time
207 | file_path = os.path.join(args.output_folder, f"test_case_{task}_{epoch_time}.bin")
208 |
209 | if isinstance(result, bytes):
210 | # Write content to the file in binary mode ('wb')
211 | with open(file_path, 'wb') as file:
212 | file.write(result)
213 | else:
214 | # Convert the string to bytes and then write to the file
215 | with open(file_path, 'wb') as file:
216 | file.write(result.encode('utf-8'))
217 |
218 | console.clear()
219 | console.print(f"[bold green]Finished test case {task} of {args.number_of_corpus_files}!")
220 |
221 | first_run = False
222 |
223 | def run():
224 | auto_corpus = AutoCorpus()
225 | auto_corpus.entry()
226 |
227 | if __name__ == "__main__":
228 | run()
229 |
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622 |
623 | How to Apply These Terms to Your New Programs
624 |
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630 | to attach them to the start of each source file to most effectively
631 | state the exclusion of warranty; and each file should have at least
632 | the "copyright" line and a pointer to where the full notice is found.
633 |
634 | A tool backed by LLMs for generating fuzzing corpus files.
635 | Copyright (C) 2024 James Stevenson
636 |
637 | This program is free software: you can redistribute it and/or modify
638 | it under the terms of the GNU General Public License as published by
639 | the Free Software Foundation, either version 3 of the License, or
640 | (at your option) any later version.
641 |
642 | This program is distributed in the hope that it will be useful,
643 | but WITHOUT ANY WARRANTY; without even the implied warranty of
644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
645 | GNU General Public License for more details.
646 |
647 | You should have received a copy of the GNU General Public License
648 | along with this program. If not, see .
649 |
650 | Also add information on how to contact you by electronic and paper mail.
651 |
652 | If the program does terminal interaction, make it output a short
653 | notice like this when it starts in an interactive mode:
654 |
655 | AutoCorpus Copyright (C) 2024 James Stevenson
656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
657 | This is free software, and you are welcome to redistribute it
658 | under certain conditions; type `show c' for details.
659 |
660 | The hypothetical commands `show w' and `show c' should show the appropriate
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665 | if any, to sign a "copyright disclaimer" for the program, if necessary.
666 | For more information on this, and how to apply and follow the GNU GPL, see
667 | .
668 |
669 | The GNU General Public License does not permit incorporating your program
670 | into proprietary programs. If your program is a subroutine library, you
671 | may consider it more useful to permit linking proprietary applications with
672 | the library. If this is what you want to do, use the GNU Lesser General
673 | Public License instead of this License. But first, please read
674 | .
675 |
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/README.md:
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1 |
2 | 
3 |
4 |
5 | 🤖 Automated Fuzzing Corpus Generation 📁
6 |
7 |
8 |
9 | 
10 | 
11 | 
12 | 
13 |
14 |
15 |
16 |
17 | AutoCorpus is a tool backed by a large language model (LLM) for automatically generating corpus files for fuzzing.
18 |
19 | AutoCorpus works best when generating corpus files that are based on natural language, such as JSON, XML, or other config files.
20 |
21 | # ⚙️ Setup
22 |
23 | ## System Requirements
24 | AutoCorpus utilizes the Mistral-7B-Instruct-v0.2 model and, where possible, offloads processing to your system's GPU. It is recommended to run AutoCorpus on a machine with a minimum of 16GB of RAM and a dedicated Nvidia GPU with at least 4GB of memory. However, it can run on lower-spec machines, albeit at a significantly slower pace.
25 |
26 | **AutoCorpus has been tested on Windows 11; however, it should be compatible with Unix and other systems.**
27 |
28 | ## Dependencies
29 | AutoCorpus requires **Nvidia CUDA** for enhanced LLM performance. Follow the steps below:
30 | - Ensure your Nvidia drivers are up to date: [Nvidia Drivers](https://www.nvidia.com/en-us/geforce/drivers/)
31 | - Install the appropriate dependencies from [here](https://pytorch.org/get-started/locally/)
32 | - Validate CUDA installation by running the following command and receiving a prompt: ```python -c "import torch; print(torch.rand(2,3).cuda())"```
33 |
34 | Python dependencies can be found in the `requirements.txt` file:
35 |
36 | ```
37 | pip install -r requirements.txt
38 | ```
39 |
40 | AutoCorpus can then be installed using the ```./setup.py``` script as below:
41 |
42 | ```
43 | python -m pip install .
44 | ```
45 |
46 | ## Running
47 |
48 | AutoCorpus can generate corpus files via three different scenarios:
49 |
50 | ### A Single Prompt
51 | For example asking AutoCorpus to generate an XML file would be as follows:
52 | ```
53 | AutoCorpus.exe -o "out" -p "xml file"
54 | ```
55 | ### Existing Corpus File(s)
56 | AutoCorpus can base new corpus files off existing ones.
57 | ```
58 | AutoCorpus.exe -i "input_folder" -o "out"
59 | ```
60 | ### Both Existing Corpus Files And a Prompt.
61 | Generation can be run by using both an existing corpus and a prompt.
62 | ```
63 | AutoCorpus.exe -i "input_folder" -o "out" -p "xml file"
64 | ```
65 |
66 | ### Usage
67 | ```
68 | usage: AutoCorpus [-h] [--input_folder INPUT_FOLDER] [--output_folder OUTPUT_FOLDER] [--number_of_corpus_files NUMBER_OF_CORPUS_FILES] [--prompt PROMPT]
69 | [--size SIZE] [--verbose]
70 |
71 | A tool for automatically generating initial fuzzing input corpus test cases
72 |
73 | optional arguments:
74 | -h, --help show this help message and exit
75 | --input_folder INPUT_FOLDER, -i INPUT_FOLDER
76 | The input folder to base generated corpus files off. If no prompt is given, the folder needs at least 1 file.
77 | --output_folder OUTPUT_FOLDER, -o OUTPUT_FOLDER
78 | The folder to save generated corpus files to (will default to input folder).
79 | --number_of_corpus_files NUMBER_OF_CORPUS_FILES, -n NUMBER_OF_CORPUS_FILES
80 | The number of corpus files to generate
81 | --prompt PROMPT, -p PROMPT
82 | A sentence defining what the corpus files are for. This helps steer generation.
83 | --size SIZE, -s SIZE Max size of tokens created by the LLM
84 | --verbose, -v Provides verbose outputs
85 | ```
86 |
87 | ### Examples
88 |
89 | #### JSON Corpus Generation
90 | Generates 5 corpus files solely on the prompt ```complex json files with varying data```.
91 | ```
92 | AutoCorpus.exe -o "out" -p "complex json files with varying data"
93 | ```
94 |
95 | ```
96 | [{"id": 1, "name": "John Doe", "age": 30, "city": "New York"},
97 |
98 | {"id": 2, "name": "Jane Smith", "age": 28, "city": "Los Angeles"},
99 |
100 | {"id": 3, "name": "Mike Johnson", "age": 35, "city": "Chicago"},
101 |
102 | {"id": 4, "name": "Emma Watson", "age": 27, "city": "London"}]
103 | ```
104 |
105 | #### AWK Config Corpus Generation
106 |
107 | Creates an AWK config based on existing example awk configs in the ``` ..\corpus\awk\``` directory along with the prompt ```config file for busybox awk```.
108 | ```
109 | AutoCorpus.exe -i ..\corpus\awk\ -p "config file for busybox awk" -n 10 -s 700
110 | ```
111 | ```
112 | ```bash
113 | #!/usr/bin/awk -f
114 |
115 | BEGIN {
116 | FS="\t"
117 | if (ARGC != 3) {
118 | print "Usage: awk-script.awk "
119 | exit 1
120 | }
121 | print "Input file:", ARGV[1]
122 | print "Field to print:", ARGC[2]
123 | print "Delimiter:", ARGC[3]
124 |
125 | FILENAME = ARGV[1]
126 | if (open(FILENAME, "r")) {
127 | while ((getline line < FILENAME) > 0) {
128 | gsub(/[[:space:]]+/, "", line) # remove whitespaces
129 | split(line, fields, FS)
130 | for (i = 1; i <= NF; i++) {
131 | if (length(fields[i]) > 0 && fields[i] ~ /\Q"[\"']"ARGV[2]"\Q/ && i != ARGC[3]) {
132 | next
133 | }
134 | if (i == ARGC[3] || i == NF) {
135 | print fields[i]
136 | break
137 | }
138 | }
139 | }
140 | close(FILENAME)
141 | } else {
142 | print "Error opening file:", FILENAME
143 | exit 1
144 | }
145 | }```
146 | ```
147 |
148 | # 🤖 Mistral-7B-Instruct-v0.2
149 | Behind the scenes AutoCorpus uses the ```Mistral-7B-Instruct-v0.2``` model from The Mistral AI Team - see [here](https://arxiv.org/abs/2310.06825). The Mistral-7B-Instruct-v0.2 Large Language Model (LLM) is an instruct fine-tuned version of the Mistral-7B-v0.2. More can be found on the model [here!](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2).
150 | - 7.24B params
151 | - Tensor type: BF16
152 | - 32k context window (vs 8k context in v0.1)
153 | - Rope-theta = 1e6
154 | - No Sliding-Window Attention
155 |
156 | # 🙏 Contributions
157 | AutoCorpus is an open-source project and welcomes contributions from the community. If you would like to contribute to
158 | AutoCorpus, please follow these guidelines:
159 |
160 | - Fork the repository to your own GitHub account.
161 | - Create a new branch with a descriptive name for your contribution.
162 | - Make your changes and test them thoroughly.
163 | - Submit a pull request to the main repository, including a detailed description of your changes and any relevant documentation.
164 | - Wait for feedback from the maintainers and address any comments or suggestions (if any).
165 | - Once your changes have been reviewed and approved, they will be merged into the main repository.
166 |
167 | # ⚖️ Code of Conduct
168 | AutoCorpus follows the Contributor Covenant Code of Conduct. Please make sure to review and adhere to this code of conduct when contributing to AutoCorpus.
169 |
170 | # 🐛 Bug Reports and Feature Requests
171 | If you encounter a bug or have a suggestion for a new feature, please open an issue in the GitHub repository. Please provide as much detail as possible, including steps to reproduce the issue or a clear description of the proposed feature. Your feedback is valuable and will help improve AutoCorpus for everyone.
172 |
173 | # 📜 License
174 |
175 | [GNU General Public License v3.0](https://choosealicense.com/licenses/gpl-3.0/)
176 |
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/logo.gif:
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https://raw.githubusercontent.com/user1342/AutoCorpus/0b8e0b9883d542326a5ef9024a22d3c3151fc1a9/logo.gif
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/requirements.txt:
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1 | argparse
2 | rich
3 | setuptools
4 | huggingface_hub
5 | numpy
6 | accelerate
7 | pyyaml
8 | torchvision -index-url https://download.pytorch.org/whl/cu118
9 | torchaudio -index-url https://download.pytorch.org/whl/cu118
10 | bitsandbytes>=0.39.0
11 | accelerate>=0.16.0,<1
12 | transformers[torch]>=4.28.1
13 | torch>=1.13.1
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/setup.py:
--------------------------------------------------------------------------------
1 | from setuptools import setup
2 |
3 | setup(
4 | name='AutoCorpus',
5 | version='0.1',
6 | install_requires=[
7 | 'argparse',
8 | 'rich',
9 | 'setuptools',
10 | 'huggingface_hub',
11 | 'numpy',
12 | 'pyyaml',
13 | 'torchvision',
14 | 'torchaudio',
15 | 'bitsandbytes',
16 | 'accelerate',
17 | 'transformers',
18 | 'torch'
19 | ],
20 | entry_points={
21 | 'console_scripts': [
22 | 'AutoCorpus = AutoCorpus.auto_corpus:run'
23 | ]
24 | }
25 | )
--------------------------------------------------------------------------------