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# Kullanım:
# python cnn_main.py dosya.png
#
# NOT:
# - İlk çalıştırmada MNIST veri seti indirilecek ve CNN modeli eğitilecek.
# - Bu yüzden internet bağlantısı gereklidir.
# - Gerekli kütüphaneler:
#   python -m venv venv
#   source venv/bin/activate  # Linux/macOS
#   venv\Scripts\activate     # Windows

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from fastmcp import FastMCP
import json
import platform
import subprocess
import requests

# fastmcp run main.py --transport sse --port 8000

"""
{

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# Kullanım:
# python main.py dosya.png
#
# NOT:
# - İlk çalıştırmada MNIST veri seti indirilecek ve model eğitilecek.
# - Bu yüzden internet bağlantısı gereklidir.
# - Gerekli kütüphaneler:
#   python -m venv venv
#   source venv/bin/activate  # Linux/macOS
#   venv\Scripts\activate     # Windows

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from flask import Flask, request
import sqlite3

app = Flask(__name__)

# Set up database
def init_db():
    conn = sqlite3.connect('users.db')
    c = conn.cursor()
    c.execute('CREATE TABLE IF NOT EXISTS users (username TEXT, password TEXT)')

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1. Overview

A security vulnerability has been discovered in Akaunting, an open-source online accounting software. The issue allows an attacker to install paid applications for free, leading to further exploitation, including remote code execution (RCE) and local privilege escalation (LPE). This report outlines the details of the vulnerability, its impact, and potential mitigation steps.

---

2. Description of the Vulnerability

2.1 Business Logic Bug in Application Purchase System

Akaunting offers an App Store where users can purchase and install various applications to extend functionality. A flaw in the request processing allows an attacker to:

• Modify request parameters (name, version, path) to install paid applications for free.

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CVE Description: File Upload Bypass in NestJS Due to Insufficient MIME Type Validation

CVE-2024-29409
File Upload Bypass Vulnerability in NestJS file-type.validator.ts

Vulnerability Summary: A file upload bypass vulnerability exists in the file-type.validator.ts implementation within the NestJS framework (versions prior to 10.x.x) that allows attackers to bypass MIME type checks during file uploads. The flaw arises from an incorrect or insufficient validation of the Content-Type header and the actual file content type. This allows an attacker to upload files with a mismatched Content-Type, leading to possible security issues, such as remote code execution, file disclosure, or data exfiltration.

Vulnerable Component:

• Component: @nestjs/common

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Vulnerability Report: Bypass in isPrivate() Function

CVE-2024-28607

Summary: An issue in the isPrivate() function of the NPM ip-utils package (version 2.4.0 and before) allows an attacker to bypass private IP detection, potentially leading to security risks such as unauthorized access, SSRF, or information leakage.

• https://github.com/librasean/IP-Utils/blob/4f88799f94f21efe6ea9135129ab2bbeb0c58edc/src/IsPrivate.ts#L4

---

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Vulnerability Report: Execution of Arbitrary Code via scontrol show hostname Command in BasicSR

Vulnerability Overview: An issue has been identified in BasicSR version 1.4.2 and earlier versions, maintained by XPixelGroup, which allows a local attacker to execute arbitrary code through the scontrol show hostname command within the init_dist_slurm function. This vulnerability exists due to insufficient input validation in handling environment variables used for CUDA device setup.

Vulnerability Details:

• CVE-ID: CVE-2024-27763
• CVSS Score: (Pending assessment)
• Impact: Local attackers can exploit this vulnerability to execute arbitrary code within the context of the application, potentially leading to unauthorized access, privilege escalation, or denial of service.

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from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import LinearRegression
import pandas as pd
import numpy as np

df = pd.read_csv('W.csv',sep = ',')
df['Height']= df.Height*2.54
df['Weight'] = df.Weight*0.45
Gender = df[df.columns[0]]
HW = df[df.columns[1:3]]

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from numpy import exp,array,random,dot

class NeuralNetwork():
	def __init__(self):
		random.seed(1)
		self.synaptic_weights = 2* random.random((3,1))-1

	def __sigmoid(self,x):
		return 1/(1+exp(-x))