Exercise 1: Constructing C Code From x86 Assembly

by | May 31, 2022 | Exclusive Articles, Featured, Reverse Engineering

Let’s try to construct C code from the corresponding x86 assembly code. In this exercise, we will look into the x86 assembly code and try to construct the corresponding C code.

Let’s look into the following piece of assembly code:

0000000000400546 <main>:
  400546:	push   rbp
  400547:	mov    rbp,rsp
  40054a:	sub    rsp,0x20
  40054e:	mov    rax,QWORD PTR fs:0x28
  400555:	00 00 
  400557:	mov    QWORD PTR [rbp-0x8],rax
  40055b:	xor    eax,eax
  40055d:	mov    DWORD PTR [rbp-0x10],0x6c6c6548
  400564:	mov    WORD PTR [rbp-0xc],0x6f
  40056a:	mov    DWORD PTR [rbp-0x18],0x0
  400571:	jmp    400577 <main+0x31>
  400573:	add    DWORD PTR [rbp-0x18],0x1
  400577:	mov    eax,DWORD PTR [rbp-0x18]
  40057a:	cdqe   
  40057c:	movzx  eax,BYTE PTR [rbp+rax*1-0x10]
  400581:	test   al,al
  400583:	jne    400573 <main+0x2d>
  400585:	mov    eax,DWORD PTR [rbp-0x18]
  400588:	mov    DWORD PTR [rbp-0x14],eax
  40058b:	mov    eax,0x0
  400590:	mov    rdx,QWORD PTR [rbp-0x8]
  400594:	xor    rdx,QWORD PTR fs:0x28
  40059b:	00 00 
  40059d:	je     4005a4 <main+0x5e>
  40059f:	call   400420 <__stack_chk_fail@plt>
  4005a4:	leave  
  4005a5:	ret    
  4005a6:	nop    WORD PTR cs:[rax+rax*1+0x0]
  4005ad:	00 00 00 

This article is accessible to premium members only. To access this article, please purchase The Security Buddy Premium Membership Plan.

Facebooktwitterredditpinterestlinkedinmail

Calculate the pseudoinverse of a matrix using Python

What is the pseudoinverse of a matrix? We know that if A is a square matrix with full rank, then A-1 is said to be the inverse of A if the following condition holds: $latex AA^{-1}=A^{-1}A=I $ The pseudoinverse or the Moore-Penrose inverse of a matrix is a...

Cholesky decomposition using Python

What is Cholesky decomposition? A square matrix A is said to have Cholesky decomposition if it can be written as a product of a lower triangular matrix and its conjugate transpose. $latex A=LL^{*} $ If all the entries of A are real numbers, then the conjugate...

Tensor Hadamard Product using Python

In one of our previous articles, we already discussed what the Hadamard product in linear algebra is. We discussed that if A and B are two matrices of size mxn, then the Hadamard product of A and B is another mxn matrix C such that: $latex H_{i,j}=A_{i,j} \times...

Perform tensor addition and subtraction using Python

We can use numpy nd-array to create a tensor in Python. We can use the following Python code to perform tensor addition and subtraction. import numpy A = numpy.random.randint(low=1, high=10, size=(3, 3, 3)) B = numpy.random.randint(low=1, high=10, size=(3, 3, 3)) C =...

How to create a tensor using Python?

What is a tensor? A tensor is a generalization of vectors and matrices. It is easily understood as a multidimensional array. For example, in machine learning, we can organize data in an m-way array and refer it as a data tensor. Data related to images, sounds, movies,...

How to combine NumPy arrays using horizontal stack?

We can use the hstack() function from the numpy module to combine two or more NumPy arrays horizontally. For example, we can use the following Python code to combine three NumPy arrays horizontally. import numpy A = numpy.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) B =...

How to combine NumPy arrays using vertical stack?

Let’s say we have two or more NumPy arrays. We can combine these NumPy arrays vertically using the vstack() function from the numpy module. For example, we can use the following Python code to combine three NumPy arrays vertically. import numpy A = numpy.array([[1, 2,...

Singular Value Decomposition (SVD) using Python

What is Singular Value Decomposition (SVD)? Let A be an mxn rectangular matrix. Using Singular Value Decomposition (SVD), we can decompose the matrix A in the following way: $latex A_{m \times n}=U_{m \times m}S_{m \times n}V_{n \times n}^T $ Here, U is an mxm matrix....

Eigen decomposition of a square matrix using Python

Let A be a square matrix. Let’s say A has k eigenvalues λ1, λ2, ... λk. And the corresponding eigenvectors are X1, X2, ... Xk. $latex X_1=\begin{bmatrix} x_{11} \\ x_{21} \\ x_{31} \\ ... \\ x_{k1} \end{bmatrix} \\ X_2=\begin{bmatrix} x_{12} \\ x_{22} \\ x_{32} \\ ......

How to calculate eigenvalues and eigenvectors using Python?

In our previous article, we discussed what eigen values and eigenvectors of a square matrix are and how we can calculate the eigenvalues and eigenvectors of a square matrix mathematically. We discussed that if A is a square matrix, then $latex (A- \lambda I) \vec{u}=0...

Amrita Mitra

Author

Ms. Amrita Mitra is an author, who has authored the books “Cryptography And Public Key Infrastructure“, “Web Application Vulnerabilities And Prevention“, “A Guide To Cyber Security” and “Phishing: Detection, Analysis And Prevention“. She is also the founder of Asigosec Technologies, the company that owns The Security Buddy.

0 Comments

Submit a Comment

This site uses Akismet to reduce spam. Learn how your comment data is processed.

Not a premium member yet?

Please follow the link below to buy The Security Buddy Premium Membership.

Featured Posts

Translate »