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utils/ai.py
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99
utils/ai.py
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#region Imports
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import tomllib
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import requests
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from utils.logger import Log
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#endregion
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#region Variables
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# Load configuration from the config.toml file
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with open("./config.toml", "rb") as f:
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data = tomllib.load(f)
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enabled = data["INTEGRATION"]["AI"]["enabled"]
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generate_endpoint = data["INTEGRATION"]["AI"]["generate_endpoint"]
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generate_model = data["INTEGRATION"]["AI"]["generate_model"]
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use_groq = data["INTEGRATION"]["AI"]["use_groq"]
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groq_api = data["INTEGRATION"]["AI"]["groq_api_token"]
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prompt = data["INTEGRATION"]["AI"]["prompt"]
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# If Groq is enabled, update the generate endpoint
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if use_groq:
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generate_endpoint = "https://api.groq.com/openai/v1/chat/completions"
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#endregion
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def generate_response(data):
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"""Generate a response using the Groq API."""
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try:
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# Create headers
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headers = {
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"Content-Type": "application/json",
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}
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# Add authorization header if using Groq
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if use_groq:
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headers["Authorization"] = f"Bearer {groq_api}"
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# Create payload
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payload = {
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"model": generate_model,
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"temperature": 1,
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"max_completion_tokens": 1024,
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"top_p": 1,
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"stream": False,
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"stop": None,
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}
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# Conditional message structure for Groq
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if use_groq:
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payload["messages"] = [
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{
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"role": "user",
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"content": f"Using this data: {data}. Respond to this prompt: {prompt}"
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}
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]
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else:
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payload["prompt"] = f"Using this data: {data}. Respond to this prompt: {prompt}"
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response = requests.post(generate_endpoint, json=payload, headers=headers)
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response.raise_for_status()
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if use_groq:
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return response.json()["choices"][0]["message"]["content"]
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else:
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return response.json()
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except requests.exceptions.RequestException as e:
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Log.e(f"Failed to generate response: {e}")
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return None
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def ai_analyse(file_path):
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"""Analyze a file and generate a response based on the user's input."""
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if enabled:
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try:
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# Open and read file data
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with open(file_path, "r", encoding="utf-8") as file:
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file_data = file.read()
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# Generate response using the file data
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response = generate_response(file_data)
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if response:
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#Log.s(f"Generated Response: {response}")
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return response
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else:
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Log.e("AI did not respond.")
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except FileNotFoundError:
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Log.e(f"File not found: {file_path}")
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except Exception as e:
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Log.e(f"Unexpected error: {e}")
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else:
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return "AI integration is disabled in the configuration, enable AI integration for AI File Analyse."
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return None
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# Example usage
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if __name__ == "__main__":
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file_path = "example.txt" # Replace with your input file path
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result = ai_analyse(file_path)
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if result:
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print("[INFO] Analysis Result:")
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print(result)
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104
utils/discord.py
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utils/discord.py
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import tomllib
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import time
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import requests
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from utils.logger import Log
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def load_config(file_path):
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"""Load configuration from a TOML file."""
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try:
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with open(file_path, "rb") as f:
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data = tomllib.load(f)
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return data
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except FileNotFoundError:
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Log.e(f"Config file {file_path} not found.")
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return None
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except tomllib.TOMLDecodeError as e:
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Log.e(f"Failed to parse TOML file: {e}")
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return None
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def truncate_text(text, limit):
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"""Truncate text to the specified character limit with an ellipsis."""
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if len(text) > limit:
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return text[:limit - 3] + "..." # Truncate and add ellipsis
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return text
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def split_text(text, limit):
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"""Split text into chunks of a specified character limit."""
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return [text[i:i + limit] for i in range(0, len(text), limit)]
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def load_config_values(data):
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"""Extract relevant values from the loaded configuration."""
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try:
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enabled = data["INTEGRATION"]["DISCORD"]["enabled"]
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discord_webhook_url = data["INTEGRATION"]["DISCORD"]["webhook_url"]
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ai_integration = data["INTEGRATION"]["AI"]["enabled"]
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truncate_text_flag = data["INTEGRATION"]["DISCORD"].get("truncate_text", True)
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return enabled, discord_webhook_url, ai_integration, truncate_text_flag
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except KeyError as e:
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Log.e(f"Missing key in config: {e}")
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return None, None, None, True
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def webhook(file_path, yara_matches, ai=""):
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"""Send a webhook to Discord with the given parameters."""
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config_file_path = "./config.toml"
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config_data = load_config(config_file_path)
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if config_data is None:
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Log.e("Failed to load configuration.")
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return
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enabled, discord_webhook_url, ai_integration, truncate_text_flag = load_config_values(config_data)
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if enabled:
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description = ai #if ai_integration and ai else "No Description Provided for this action."
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# Handle truncation or splitting based on config
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if truncate_text_flag:
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description = truncate_text(description, 4092)
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else:
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description_chunks = split_text(description, 4092)
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# Create embeds
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embeds = []
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if truncate_text_flag:
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# Single embed if truncated
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embeds.append({
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"title": "⚠️ WATCHDOG ALERT ⚠️",
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"description": description,
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"color": 65280,
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"fields": yara_matches,
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"author": {
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"name": file_path
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},
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"thumbnail": {
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"url": "https://images-ext-1.discordapp.net/external/ZdQffnnucK3DWYPeokYDWnFPATtlvszVNozmNhOdXBg/https/upload.wikimedia.org/wikipedia/commons/5/59/Empty.png?format=webp&quality=lossless"
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}
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})
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else:
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# Multiple embeds if split
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for idx, chunk in enumerate(description_chunks):
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embeds.append({
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"title": f"⚠️ WATCHDOG ALERT ⚠️ (Part {idx + 1})",
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"description": chunk,
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"color": 65280,
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"fields": yara_matches if idx == 0 else [], # Fields only in the first embed
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"author": {
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"name": file_path if idx == 0 else None
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},
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"thumbnail": {
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"url": "https://images-ext-1.discordapp.net/external/ZdQffnnucK3DWYPeokYDWnFPATtlvszVNozmNhOdXBg/https/upload.wikimedia.org/wikipedia/commons/5/59/Empty.png?format=webp&quality=lossless"
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}
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})
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# Construct the payload
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payload = {
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"content": "",
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"embeds": embeds[:10], # Discord allows a maximum of 10 embeds per payload
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"attachments": []
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}
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try:
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# Send POST request to Discord webhook
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response = requests.post(discord_webhook_url, json=payload)
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response.raise_for_status() # Raise exception for HTTP errors
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Log.v(f"Report sent to Discord webhook for {file_path}")
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except requests.exceptions.RequestException as e:
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Log.e(f"Report was not sent to Discord webhook, error: {e}")
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18
utils/logger.py
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18
utils/logger.py
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from pystyle import Colors, Colorate
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from datetime import datetime
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import time
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class Log:
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@staticmethod
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def s(text): # success
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time_now = datetime.fromtimestamp(time.time()).strftime('%H:%M')
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print(Colors.gray + time_now + " " + Colorate.Horizontal(Colors.green_to_cyan, "SUCCESS", 1) + Colors.gray + " > " + Colors.light_gray + text + Colors.reset)
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@staticmethod
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def e(text): # error
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time_now = datetime.fromtimestamp(time.time()).strftime('%H:%M')
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print(Colors.gray + time_now + " " + Colorate.Horizontal(Colors.red_to_purple, " ERROR ", 1) + Colors.gray + " > " + Colors.light_gray + text + Colors.reset)
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@staticmethod
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def v(data): # verbose
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time_now = datetime.fromtimestamp(time.time()).strftime('%H:%M')
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print(Colors.gray + time_now + " " + Colorate.Horizontal(Colors.blue_to_white, "VERBOSE", 1) + Colors.gray + " > " + Colors.light_gray + data + Colors.reset)
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utils/scanner.py
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45
utils/scanner.py
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#region Imports
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import os
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import yara
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import tomllib
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#endregion
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#region Variables
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scanned_files_map = set()
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ignored_files = {}
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ignored_directories = {}
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with open("./config.toml", "rb") as f:
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data = tomllib.load(f)
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#endregion
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#region scanfile
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def scan(file_path):
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"""
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Scan a file with YARA rules and return the matches.
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Args:
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file_path (str): The path to the file to be scanned.
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Returns:
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matches[filename], error_message
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"""
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matches = {}
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error_messages = {}
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for filename in os.listdir(data['DETECTION']['SignaturePath']):
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if filename.endswith((".yara")):
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rule_path = os.path.join(data['DETECTION']['SignaturePath'], filename)
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try:
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rules = yara.compile(filepath=rule_path)
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file_matches = rules.match(file_path)
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if file_matches:
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matches[filename] = file_matches
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# for match in file_matches:
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# print(f" - Rule: {match.rule}")
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except yara.Error as e:
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error_messages[filename] = e
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return matches, error_messages
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#endregion
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