xunge

#!/usr/bin/env python3
import os
import json
import hashlib
import tempfile
import shutil
import logging
import subprocess as sp
from pathlib import Path
from email.utils import parsedate_to_datetime

xunge

import time
import shutil
import os

import torch
import torch.nn as nn
import torchvision.datasets as datasets
import torchvision.transforms as transforms
import torchvision.models as models
import torch.backends.cudnn as cudnn

xunge

import sqlite3
import multiprocessing as mp
from itertools import repeat


def build_db(db_name):
    conn = sqlite3.connect(db_name)
    cursor = conn.cursor()
    cursor.execute('''CREATE TABLE  IF NOT EXISTS EMPLOYEE (
       ID INTEGER PRIMARY KEY,

xunge

# Copyright (C) 2017
#
# Written by Ashley Lesdalons <ashley.lesdalons@etu.univ-grenoble-alpes.fr>
#
# ========LICENCE========
# This script is free software: you can redistribute it and/or modify
# it under the terms of the  GNU Lesser General Public
# License as published by the Free Software Foundation; either
# version 2.1 of the License, or (at your option) any later version.
#

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# -----------------------------------------------------------------------
# This script computes a weight value for each basis block of each functions. the algorithm is:
# 1. for each outgoing edge (i->j) of a BB i, assign a equal probability Eij, i.e. Eij= 1/n for a "n edges" BB.
# 2. assign root BB a weight of 1 (this is always reachable).
# 3. for each BB j, its weight is: W(j) = SUM (over i \in Pred(j)) W(i)*Eij
# after completion, it creates a pickle file that contains weights of BBs.
##Addintion: it also scans each function to find CMD instruction and check if it has some byte to compare with. All such bytes are saved in a pickle file that will be used to mutate inputs.


import idaapi

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#TODO This script performs the static analysis part of VUzzer. For a given binary, it computes a weight of each basic block of each function. It also extracts immediates from each CMP instruction.
#@author: Sanjay Rawat 
#@category: VUzzer Static Analysis
#@keybinding 
#@menupath tools.Static Analysis.VUzzer
#@toolbar 


#TODO Add User Code Here
#from ghidra.program.model.block import *

xunge

from tokenizers import Tokenizer
from tokenizers.models import WordLevel
from tokenizers.trainers import WordLevelTrainer
from tokenizers.pre_tokenizers import WhitespaceSplit
from tokenizers.processors import RobertaProcessing
from model.roberta import RobertaTokenizerFast

SPECIAL_TOKENS = ["<s>", "<pad>", "</s>", "<unk>", "<mask>"]
UNK_TOKENS = "<unk>"

xunge

import tensorflow as tf

# 输入训练数据，这里是python的list, 也可以定义为numpy的ndarray
x_data = [[1., 0.], [0., 1.], [0., 0.], [1., 1.]]
x = tf.placeholder(tf.float32, shape=[None, 2])  # 定义占位符，占位符在运行图的时候必须feed数据
y_data = [[1], [1], [0], [0]]  # 训练数据的标签，注意维度
y = tf.placeholder(tf.float32, shape=[None, 1])
# 定义variables，在运行图的过程中会被按照优化目标改变和保存
weights = {'w1': tf.Variable(tf.random_normal([2, 16])),
           'w2': tf.Variable(tf.random_normal([16, 1]))}

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with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    for i in range(1000):
        for j in range(4):
            sess.run(train, feed_dict={x: np.expand_dims(X[j], 0), y: np.expand_dims(Y[j], 0)})
        loss_ = sess.run(loss, feed_dict={x: X, y: Y})
        print("step: %d, loss: %.3f" % (i, loss_))
    print("X: %r" % X)
    print("pred: %r" % sess.run(out, feed_dict={x: X}))

xunge

import tensorflow as tf
import numpy as np

X = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])
Y = np.array([[0], [1], [1], [0]])

x = tf.placeholder(tf.float32, [None, 2])
y = tf.placeholder(tf.float32, [None, 1])

w = tf.Variable(tf.random_normal([2, 1]))