SKILL: Symbolic Execution Tools — Expert Analysis Playbook

AI LOAD INSTRUCTION: Expert symbolic execution techniques using angr, Z3, and Unicorn Engine. Covers CTF challenge automation, constraint solving patterns, function hooking, SimProcedure replacement, and emulation-based unpacking. Base models often produce broken angr scripts due to incorrect state initialization or missing hooks for libc functions.

0. RELATED ROUTING

anti-debugging-techniques when anti-debug checks need to be symbolically bypassed
code-obfuscation-deobfuscation when using symbolic execution for deobfuscation
vm-and-bytecode-reverse when applying angr to custom VM challenges

Advanced Reference

Also load ANGR_COOKBOOK.md when you need:

15+ ready-to-use angr script patterns for common CTF challenges
Hook templates for scanf, printf, malloc, strcmp
Symbolic file input, stdin, argv patterns
Optimization tricks for path explosion management

When to use which tool

Scenario	Best Tool	Why
Pure math / equation system	Z3	Direct constraint solving, no binary needed
Binary with control flow	angr	Explores paths, manages constraints automatically
Emulate specific code region	Unicorn	Fast, no symbolic overhead, good for unpacking
Complex binary + custom VM	angr + Unicorn (combo)	angr for control flow, Unicorn for VM handlers
Kernel / firmware code	Qiling	Full system emulation with OS awareness

1. ANGR — CORE CONCEPTS

1.1 Pipeline

Project(binary)
  → Factory.entry_state() / blank_state(addr=)
    → SimulationManager(state)
      → explore(find=target, avoid=bad)
        → found[0].solver.eval(symbolic_var)

1.2 Essential Setup

python

import angr
import claripy

proj = angr.Project('./challenge', auto_load_libs=False)

# Entry state: start from program entry point
state = proj.factory.entry_state()

# Blank state: start from arbitrary address
state = proj.factory.blank_state(addr=0x401000)

# Full init state: with command-line args
state = proj.factory.full_init_state(args=['./challenge', arg1_sym])

simgr = proj.factory.simulation_manager(state)
simgr.explore(find=0x401234, avoid=[0x401300])

if simgr.found:
    found = simgr.found[0]
    solution = found.solver.eval(symbolic_input, cast_to=bytes)
    print(f"Solution: {solution}")

1.3 Symbolic Variables (claripy)

python

# 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

1.4 Symbolic stdin

python

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)

1.5 Hooking Functions

python

# 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']())

1.6 Memory Operations

python

# 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)

2. Z3 CONSTRAINT SOLVING

2.1 Core API

python

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]}")

2.2 Common CTF Patterns

python

# 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)

2.3 Optimization

python

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())

3. UNICORN ENGINE — CODE EMULATION

3.1 Basic Setup

python

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)

3.2 Hooking Memory & Instructions

python

# 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)

3.3 Use Cases

Use Case	Approach
Unpack shellcode	Map shellcode, emulate, dump decoded payload
Decrypt strings	Emulate decryption function with controlled inputs
Brute-force short keys	Loop emulation with different key inputs
Analyze obfuscated function	Emulate function, observe register/memory state
Firmware code emulation	Map firmware memory layout, emulate routines

4. ANGR EXPLORATION STRATEGIES

4.1 find/avoid

python

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
)

4.2 Managing Path Explosion

Strategy	Implementation
Constrain input space	Add constraints (printable, length limits)
Avoid dead-end paths	Use `avoid=` for known failure addresses
Hook complex functions	Replace with simplified SimProcedure
Limit loop iterations	`state.options.add(angr.options.LAZY_SOLVES)`
Use veritesting	`simgr.explore(..., technique=angr.exploration_techniques.Veritesting())`
DFS instead of BFS	`simgr.use_technique(angr.exploration_techniques.DFS())`
Timeout per path	`simgr.explore(..., num_find=1)` + timeout wrapper

4.3 Concrete + Symbolic Hybrid

python

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

This dramatically speeds up execution: concrete code runs natively via Unicorn, switching to symbolic only when symbolic variables are involved.

5. PRACTICAL WORKFLOW

5.1 CTF Binary Solving Workflow

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()

6. DECISION TREE

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)

7. COMMON PITFALLS & FIXES

Problem	Cause	Fix
angr hangs forever	Path explosion in loops	Add `avoid=` for loop-back edges, or hook the loop
Z3 returns `unknown`	Non-linear constraints too complex	Simplify, split into sub-problems, use `set_param("timeout", 5000)`
Unicorn crashes on syscall	Syscall not handled	Hook syscall interrupt, handle or skip
angr wrong result	Incorrect state initialization	Verify initial memory layout matches actual binary
Symbolic memory too large	Unbounded symbolic reads	Concretize array indices where possible
SimProcedure wrong types	Argument type mismatch	Check calling convention (cdecl vs fastcall)
angr can't load binary	Missing libraries	Use `auto_load_libs=False` + hook needed symbols

8. TOOL VERSIONS & INSTALLATION

bash

# 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

symbolic-execution-tools

NPX Install

Tags

SKILL.md Content

SKILL: Symbolic Execution Tools — Expert Analysis Playbook

0. RELATED ROUTING

Advanced Reference

When to use which tool

1. ANGR — CORE CONCEPTS

1.1 Pipeline

1.2 Essential Setup

1.3 Symbolic Variables (claripy)

1.4 Symbolic stdin

1.5 Hooking Functions

1.6 Memory Operations

2. Z3 CONSTRAINT SOLVING

2.1 Core API

2.2 Common CTF Patterns

2.3 Optimization

3. UNICORN ENGINE — CODE EMULATION

3.1 Basic Setup

3.2 Hooking Memory & Instructions

3.3 Use Cases

4. ANGR EXPLORATION STRATEGIES

4.1 find/avoid

4.2 Managing Path Explosion

4.3 Concrete + Symbolic Hybrid

5. PRACTICAL WORKFLOW

5.1 CTF Binary Solving Workflow

6. DECISION TREE

7. COMMON PITFALLS & FIXES

8. TOOL VERSIONS & INSTALLATION