Pros of z3

• More specialized and efficient for SMT solving
• Broader language support (C, C++, Python, .NET, OCaml, etc.)
• Extensive documentation and academic research backing

Cons of z3

• Narrower focus on constraint solving
• Steeper learning curve for non-experts in formal methods
• Less integrated with binary analysis workflows

Code Comparison

z3:

from z3 import *

x = Int('x')
y = Int('y')
s = Solver()
s.add(x + y > 5)
s.add(x > 1, y > 1)
print(s.check())
print(s.model())

angr:

import angr

proj = angr.Project('binary')
state = proj.factory.entry_state()
simgr = proj.factory.simulation_manager(state)
simgr.explore(find=lambda s: b"Win" in s.posix.dumps(1))
print(simgr.found[0].posix.dumps(1))

Summary

z3 is a powerful SMT solver with broad language support and extensive documentation, making it ideal for constraint solving tasks. However, it has a steeper learning curve and is less integrated with binary analysis workflows compared to angr. angr, on the other hand, provides a more comprehensive binary analysis framework but may be less efficient for pure constraint solving tasks.

Pros of radare2

• More lightweight and faster execution for basic reverse engineering tasks
• Extensive command-line interface with powerful scripting capabilities
• Broader platform support, including embedded systems and mobile devices

Cons of radare2

• Steeper learning curve due to complex command syntax
• Less advanced symbolic execution and program analysis features
• Limited high-level decompilation capabilities compared to angr

Code Comparison

radare2 (command-line interface):

r2 -A binary
pdf @ main

angr (Python API):

import angr
proj = angr.Project('binary')
cfg = proj.analyses.CFGFast()

Key Differences

radare2 is primarily a reverse engineering framework with a focus on binary analysis and manipulation. It excels in low-level operations and provides a powerful command-line interface.

angr, on the other hand, is a binary analysis platform that emphasizes symbolic execution and program analysis. It offers more advanced features for vulnerability discovery and exploit development.

While radare2 is better suited for quick analysis and scripting tasks, angr provides more sophisticated analysis capabilities at the cost of increased complexity and resource usage.

Pros of Capstone

• Lightweight and focused solely on disassembly
• Supports a wide range of architectures
• Faster execution for simple disassembly tasks

Cons of Capstone

• Limited to disassembly; lacks advanced analysis features
• Requires additional tools for complex reverse engineering tasks
• Less suitable for symbolic execution and program analysis

Code Comparison

Capstone (disassembling x86 code):

from capstone import *

CODE = b"\x55\x48\x8b\x05\xb8\x13\x00\x00"
md = Cs(CS_ARCH_X86, CS_MODE_64)
for i in md.disasm(CODE, 0x1000):
    print("%x:\t%s\t%s" %(i.address, i.mnemonic, i.op_str))

Angr (symbolic execution):

import angr

proj = angr.Project('binary')
state = proj.factory.entry_state()
simgr = proj.factory.simulation_manager(state)
simgr.explore(find=0x400000)

While Capstone excels at efficient disassembly across multiple architectures, Angr provides a comprehensive framework for binary analysis, including symbolic execution and program slicing. Capstone is ideal for projects requiring quick and lightweight disassembly, whereas Angr is better suited for complex reverse engineering tasks and vulnerability research.

Pros of Unicorn

• Lightweight and fast emulation framework
• Supports a wide range of architectures
• Easy to integrate into existing projects

Cons of Unicorn

• Limited symbolic execution capabilities
• Less comprehensive analysis features
• Requires more manual setup for complex scenarios

Code Comparison

Unicorn

from unicorn import *
from unicorn.x86_const import *

# Initialize emulator in X86-32bit mode
mu = Uc(UC_ARCH_X86, UC_MODE_32)

# Map 2MB memory for this emulation
mu.mem_map(ADDRESS, 2 * 1024 * 1024)

# Write machine code to be emulated to memory
mu.mem_write(ADDRESS, X86_CODE32)

Angr

import angr

# Create a project
proj = angr.Project('/bin/true', load_options={'auto_load_libs': False})

# Create a simulation manager
state = proj.factory.entry_state()
simgr = proj.factory.simulation_manager(state)

# Run symbolic execution
simgr.run()

Key Differences

• Unicorn focuses on CPU emulation, while Angr provides a full-fledged binary analysis framework
• Angr offers advanced features like symbolic execution and program slicing, which are not available in Unicorn
• Unicorn is better suited for low-level emulation tasks, while Angr excels in complex program analysis scenarios

Pros of RetDec

• Focuses on machine code decompilation to high-level language
• Supports a wide range of architectures (x86, ARM, MIPS, PIC32, PowerPC)
• Provides a graphical user interface for easier use

Cons of RetDec

• More specialized tool compared to Angr's broader analysis capabilities
• Less active community and fewer updates in recent years
• Limited support for dynamic analysis

Code Comparison

RetDec (C++ decompilation output):

int32_t function_401000(int32_t a1) {
    int32_t v1 = a1 * 2;
    return v1 + 5;
}

Angr (Python-based symbolic execution):

import angr

proj = angr.Project('binary')
state = proj.factory.entry_state()
simgr = proj.factory.simulation_manager(state)
simgr.explore(find=0x401000)

RetDec focuses on producing readable high-level code from binaries, while Angr provides a framework for various binary analysis tasks, including symbolic execution. RetDec's output is typically C or C++ code, whereas Angr uses Python for analysis and exploration of program states.