symbolic-execution-tools

# Bitvector (fixed-size integer)
sym_input = claripy.BVS("input", 64)        # 64-bit symbolic
sym_byte = claripy.BVS("byte", 8)           # 8-bit symbolic
sym_buf = claripy.BVS("buffer", 8 * 32)     # 32-byte buffer
 
# Concrete bitvector
concrete = claripy.BVV(0x41, 8)             # concrete value 0x41
 
# Constraints
state.solver.add(sym_input > 0)
state.solver.add(sym_input < 100)
state.solver.add(sym_byte >= 0x20)           # printable ASCII
state.solver.add(sym_byte <= 0x7e)
 
# Evaluate
value = state.solver.eval(sym_input)
all_values = state.solver.eval_upto(sym_input, 10)  # up to 10 solutions

flag_len = 32
sym_stdin = claripy.BVS("stdin", 8 * flag_len)
 
state = proj.factory.entry_state(stdin=sym_stdin)
 
# Constrain to printable ASCII
for i in range(flag_len):
    byte = sym_stdin.get_byte(i)
    state.solver.add(byte >= 0x20)
    state.solver.add(byte <= 0x7e)

# Hook by address (skip N bytes of original code)
@proj.hook(0x401100, length=5)
def skip_check(state):
    state.regs.eax = 1  # force success
 
# SimProcedure: replace library function
class MyStrcmp(angr.SimProcedure):
    def run(self, s1, s2):
        return claripy.If(
            self.state.memory.load(s1, 32) == self.state.memory.load(s2, 32),
            claripy.BVV(0, 32),
            claripy.BVV(1, 32)
        )
 
proj.hook_symbol('strcmp', MyStrcmp())
 
# Hook common problematic functions
proj.hook_symbol('printf', angr.SIM_PROCEDURES['libc']['printf']())
proj.hook_symbol('scanf', angr.SIM_PROCEDURES['libc']['scanf']())
proj.hook_symbol('puts', angr.SIM_PROCEDURES['libc']['puts']())

# Read memory (symbolic-aware)
data = state.memory.load(addr, size)          # returns BV
data_concrete = state.solver.eval(data, cast_to=bytes)
 
# Write memory
state.memory.store(addr, claripy.BVV(0x41, 8))
state.memory.store(addr, sym_buf)
 
# Read/write registers
rax = state.regs.rax
state.regs.rdi = claripy.BVV(0x1000, 64)

from z3 import *
 
# Sorts
x = BitVec('x', 32)    # 32-bit bitvector
y = Int('y')             # arbitrary precision integer
b = Bool('b')            # boolean
 
# Solver
s = Solver()
s.add(x + y == 42)
s.add(x > 0)
s.add(y > 0)
 
if s.check() == sat:
    m = s.model()
    print(f"x = {m[x]}, y = {m[y]}")

# Serial key validation: each char satisfies constraints
key = [BitVec(f'k{i}', 8) for i in range(16)]
s = Solver()
for k in key:
    s.add(k >= 0x30, k <= 0x7a)  # alphanumeric-ish
 
# XOR key recovery
plaintext = b"known_plaintext"
ciphertext = b"\x12\x34..."
key_byte = BitVec('key', 8)
s = Solver()
for p, c in zip(plaintext, ciphertext):
    s.add(p ^ key_byte == c)
 
# System of linear equations (modular)
a, b, c = BitVecs('a b c', 32)
s = Solver()
s.add(3*a + 5*b + 7*c == 0x12345678)
s.add(2*a + 4*b + 6*c == 0xDEADBEEF)
s.add(a ^ b ^ c == 0xCAFEBABE)

from z3 import Optimize
 
opt = Optimize()
x = BitVec('x', 32)
opt.add(x > 0)
opt.add(x < 1000)
opt.minimize(x)  # find smallest satisfying value
opt.check()
print(opt.model())

from unicorn import *
from unicorn.x86_const import *
from capstone import Cs, CS_ARCH_X86, CS_MODE_64
 
mu = Uc(UC_ARCH_X86, UC_MODE_64)
 
CODE_ADDR = 0x400000
STACK_ADDR = 0x7fff0000
STACK_SIZE = 0x10000
 
mu.mem_map(CODE_ADDR, 0x10000)
mu.mem_map(STACK_ADDR, STACK_SIZE)
 
mu.mem_write(CODE_ADDR, code_bytes)
mu.reg_write(UC_X86_REG_RSP, STACK_ADDR + STACK_SIZE - 0x1000)
mu.reg_write(UC_X86_REG_RBP, STACK_ADDR + STACK_SIZE - 0x1000)
 
mu.emu_start(CODE_ADDR, CODE_ADDR + len(code_bytes))
 
result = mu.reg_read(UC_X86_REG_RAX)

# Hook memory access
def hook_mem(uc, access, address, size, value, user_data):
    if access == UC_MEM_WRITE:
        print(f"Write {value:#x} to {address:#x}")
    elif access == UC_MEM_READ:
        print(f"Read from {address:#x}")
 
mu.hook_add(UC_HOOK_MEM_READ | UC_HOOK_MEM_WRITE, hook_mem)
 
# Hook specific instruction (for tracing)
def hook_code(uc, address, size, user_data):
    code = uc.mem_read(address, size)
    md = Cs(CS_ARCH_X86, CS_MODE_64)
    for insn in md.disasm(bytes(code), address):
        print(f"  {insn.address:#x}: {insn.mnemonic} {insn.op_str}")
 
mu.hook_add(UC_HOOK_CODE, hook_code)

simgr.explore(
    find=lambda s: b"Correct" in s.posix.dumps(1),   # stdout contains "Correct"
    avoid=lambda s: b"Wrong" in s.posix.dumps(1)      # avoid "Wrong" output
)

state = proj.factory.entry_state(
    add_options={angr.options.UNICORN}  # use Unicorn for concrete regions
)

1. Static analysis: identify input method, success/fail conditions
   └─ Find "Correct" / "Wrong" strings → get their xref addresses

2. Choose tool:
   ├─ Pure math (no binary needed) → Z3
   ├─ Small binary, clear success/fail → angr explore
   └─ Specific function to emulate → Unicorn

3. Set up symbolic input:
   ├─ stdin → claripy.BVS + entry_state(stdin=)
   ├─ argv → full_init_state(args=[...])
   ├─ file input → SimFile
   └─ specific memory → state.memory.store(addr, sym)

4. Hook problematic functions:
   ├─ printf/puts → SimProcedure or no-op
   ├─ scanf → custom handler
   ├─ time/random → return concrete value
   └─ anti-debug → skip entirely

5. Explore and extract:
   └─ simgr.explore(find=, avoid=) → solver.eval()

Need to solve a reversing challenge?
│
├─ Is the challenge pure math / equations?
│  └─ Yes → Z3
│     ├─ Linear equations → BitVec + Solver
│     ├─ Modular arithmetic → BitVec (natural mod 2^n)
│     ├─ Boolean logic → Bool + Solver
│     └─ Optimization → Optimize + minimize/maximize
│
├─ Is it a compiled binary with clear success/fail?
│  └─ Yes → angr
│     ├─ Input via stdin → symbolic stdin
│     ├─ Input via argv → full_init_state with symbolic args
│     ├─ Input via file → SimFile
│     ├─ Path explosion → add constraints, avoid paths, hook loops
│     └─ Complex library calls → hook with SimProcedure
│
├─ Need to emulate a specific function/region?
│  └─ Yes → Unicorn Engine
│     ├─ Decryption routine → map code + data, emulate, read result
│     ├─ Shellcode analysis → map shellcode, hook syscalls
│     └─ Key schedule → emulate with different inputs
│
├─ Need to analyze firmware / exotic arch?
│  └─ Yes → Qiling (full system emulation with OS support)
│
├─ Binary has VM protection?
│  └─ angr for handler analysis + Z3 for bytecode constraints
│
└─ None of the above working?
   ├─ Combine: Unicorn for concrete regions + Z3 for constraints
   ├─ Manual reverse engineering with debugger
   └─ Side-channel approach (timing, power analysis for hardware)

# angr (Python 3.8+)
pip install angr
 
# Z3
pip install z3-solver
 
# Unicorn Engine
pip install unicorn
 
# Capstone (disassembly, pairs with Unicorn)
pip install capstone
 
# Keystone (assembly)
pip install keystone-engine