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Overview

angrop is built on top of angr’s symbolic execution engine. Instead of executing instructions with concrete values, symbolic execution treats program inputs as symbolic variables and tracks all possible states through constraints. This allows angrop to:
  1. Analyze gadget effects - Understand what a gadget does to registers and memory
  2. Find gadget capabilities - Determine which registers can be controlled
  3. Generate chains - Solve constraints to build working ROP payloads
  4. Verify correctness - Ensure generated chains achieve their goals

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Symbolic Execution in angr

From the README:
“angrop uses symbolic execution to understand the effects of gadgets and uses constraint solving and graph search for generating chains.”

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Key Concepts

Symbolic State: Represents program state where values can be symbolic (unknown) rather than concrete. Constraints: Logical conditions that must be satisfied (e.g., rax + rbx == 0x1234). Constraint Solver: Uses SMT solvers (Z3) to find values satisfying all constraints. Unconstrained State: A state where the instruction pointer is symbolic, indicating a controlled jump.

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Analyzing Gadget Effects

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Creating Symbolic States

From builder.py:47-55: 
def make_sim_state(self, pc, stack_gsize):
    # Create state with symbolized general-purpose registers
    state = rop_utils.make_symbolic_state(
        self.project, 
        self.arch.reg_list,  # All GP registers
        stack_gsize          # Symbolic stack slots
    )
    state.stack_pop()  # Emulate 'pop pc'
    state.regs.ip = pc
    return state
This creates a state where:
  • All GP registers are symbolic: rax = sreg_rax_0, rbx = sreg_rbx_0, etc.
  • Stack slots are symbolic: [rsp+0] = symbolic_stack_0, [rsp+8] = symbolic_stack_1, etc.
  • We can track how the gadget transforms these symbols

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Stepping Through Gadgets

From builder.py:419-437: 
# Step through each basic block in the gadget
for addr in gadget.bbl_addrs[1:]:
    succ = state.step()
    # Find successor state matching the next block
    succ_states = [
        s for s in succ.successors 
        if s.solver.is_true(s.ip == addr)
    ]
    state = succ_states[0]

# Step to unconstrained successor (the return)
succ = state.step()
state = succ.unconstrained_successors[0]
What this does:
  1. Execute each instruction symbolically
  2. Track all possible execution paths
  3. Apply constraints from branches
  4. Reach the final state where pc is symbolic (return)

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Extracting Effects

After stepping through a gadget, angrop analyzes the final state: Register effects: 
# Check if register was modified
if final_state.regs.rax != initial_state.regs.rax:
    gadget.changed_regs.add('rax')

# Check if register was popped from stack  
if final_state.regs.rax.symbolic:
    if 'symbolic_stack_0' in str(final_state.regs.rax):
        gadget.popped_regs.add('rax')
Memory effects: 
# Analyze memory actions from execution history
for action in state.history.actions:
    if action.type == 'mem' and action.action == 'write':
        mem_access = RopMemAccess()
        mem_access.addr = action.addr.ast
        mem_access.data = action.data.ast
        # Extract dependencies from symbolic ASTs
        mem_access.addr_dependencies = get_ast_dependency(addr)
        mem_access.data_dependencies = get_ast_dependency(data)
        gadget.mem_writes.append(mem_access)

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Constraint Solving for Chain Generation

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The Core Algorithm

From builder.py:362-420, the chain building process: 
def _build_reg_setting_chain(self, gadgets, register_dict):
    # 1. Create symbolic state with stack and registers symbolic
    test_symbolic_state = rop_utils.make_symbolic_state(
        self.project, self.arch.reg_list, total_sc//arch_bytes+1
    )
    test_symbolic_state.ip = test_symbolic_state.stack_pop()
    
    # 2. Map stack variables to gadgets/values
    stack_var_to_value = {}
    
    # 3. Step through each gadget
    state = test_symbolic_state.copy()
    for gadget in gadgets:
        # Constrain IP to gadget address
        state.solver.add(state.ip == gadget.addr)
        state.ip = gadget.addr
        
        # Step through the gadget
        for addr in gadget.bbl_addrs[1:]:
            state = state.step().successors[0]
        state = state.step().unconstrained_successors[0]
    
    # 4. Constrain final register values
    for reg, val in register_dict.items():
        state.solver.add(state.regs[reg] == val)
    
    # 5. Solve for stack values
    for offset in range(0, total_sc, bytes_per_pop):
        sym_word = test_symbolic_state.stack_read(offset)
        # Solve what value this stack slot needs
        concrete_val = state.solver.eval(sym_word)

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Example: Setting RAX to 0x1234

Let’s trace through finding a chain to set rax = 0x1234: Gadget: pop rax; ret at 0x401000 
# Initial state
state.regs.rax = symbolic('sreg_rax_0')  
state.regs.rsp = symbolic('sreg_rsp_0')
state.memory[rsp+0] = symbolic('symbolic_stack_0')  # Will be gadget address
state.memory[rsp+8] = symbolic('symbolic_stack_1')  # Will be rax value

# Step 1: Pop PC to get gadget address
state.ip = state.stack_pop()  # ip = symbolic_stack_0
state.solver.add(state.ip == 0x401000)  # Constrain to gadget

# Step 2: Execute 'pop rax'
state.step()
state.regs.rax = symbolic_stack_1  # rax now contains stack slot 1
state.regs.rsp += 8

# Step 3: Execute 'ret'
state.step()
state.ip = symbolic_stack_2  # Unconstrained return

# Step 4: Constrain final rax value  
state.solver.add(state.regs.rax == 0x1234)
# This constrains: symbolic_stack_1 == 0x1234

# Step 5: Solve for stack values
stack_0_value = state.solver.eval(symbolic_stack_0)  # = 0x401000
stack_1_value = state.solver.eval(symbolic_stack_1)  # = 0x1234

# Result: payload = p64(0x401000) + p64(0x1234)

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Advanced Constraint Solving

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Rebalancing ASTs

When gadgets transform values, we need to invert the operations: From builder.py:245-359: 
def _rebalance_ast(self, lhs, rhs, mode='stack'):
    # Given: (symbolic_stack_0 + 0x1000) == user_value  
    # Solve: symbolic_stack_0 == (user_value - 0x1000)
    
    while lhs.depth != 1:  # lhs is not just a symbolic variable
        match lhs.op:
            case "__add__":
                # (stack + const) == value
                # => stack == (value - const)
                other = find_concrete_arg(lhs)
                rhs -= other
                lhs = find_symbolic_arg(lhs)
                
            case "__sub__":
                # (stack - const) == value  
                # => stack == (value + const)
                other = find_concrete_arg(lhs)
                rhs += other
                lhs = find_symbolic_arg(lhs)
                
            case "ZeroExt":
                # ZeroExt(N, stack) == value
                # Check leading bits are zero, extract lower bits
                rhs = Extract(rhs.length-lhs.args[0]-1, 0, rhs)
                lhs = lhs.args[1]
                
            # ... handles xor, and, or, shifts, etc.
    
    return lhs, rhs  # lhs is now symbolic_stack_X, rhs is adjusted value
Example: Gadget pop rax; add rax, 0x10; ret 
# After symbolic execution:
state.regs.rax = symbolic_stack_1 + 0x10

# User wants rax = 0x1234
state.solver.add((symbolic_stack_1 + 0x10) == 0x1234)

# Rebalance:
lhs = symbolic_stack_1 + 0x10
rhs = 0x1234
# Process __add__:
rhs = 0x1234 - 0x10 = 0x1224
lhs = symbolic_stack_1

# Solution: stack slot 1 must be 0x1224

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Handling Symbolic Memory Accesses

From builder.py:472-492: 
# Constrain memory access addresses
for action in state.history.actions:
    if action.type == 'mem' and action.addr.symbolic:
        # Memory address depends on symbolic value
        # Need to constrain it to a writable location
        
        if constrained_addrs is not None:
            ptr_bv = constrained_addrs[0]
        else:
            # Find a writable address that doesn't contain badbytes
            ptr_bv = BVV(self._get_ptr_to_writable(action.size//8))
        
        # Rebalance to find what stack value produces this address
        lhs, rhs = self._rebalance_ast(action.addr.ast, ptr_bv)
        # lhs is now symbolic_stack_X
        # rhs is the value that makes the address work
        
        # Store the mapping
        map_stack_var(lhs, RopValue(rhs))
Example: Gadget mov [rax+0x10], rbx; ret 
# After execution:
action.addr = state.regs.rax + 0x10  # = symbolic_stack_1 + 0x10

# We need this to point to a writable address, say 0x601000
ptr_bv = 0x601000

# Rebalance:
# (symbolic_stack_1 + 0x10) == 0x601000
# symbolic_stack_1 == 0x600ff0

# Solution: stack slot 1 must be 0x600ff0

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Verification

After generating a chain, angrop verifies it works: From reg_setter.py:161-200: 
def verify(self, chain, preserve_regs, registers):
    # Execute the chain symbolically
    state = chain.exec()
    
    # Check all actions respect constraints
    for act in state.history.actions:
        if act.type == 'mem':
            # Ensure no out-of-bounds memory access
            if act.addr.ast.variables and \
               any(not x.startswith('sym_addr') for x in act.addr.ast.variables):
                return False
        
        if act.type == 'reg' and act.action == 'write':
            # Ensure preserved registers weren't modified
            reg_name = self.project.arch.translate_register_name(act.offset)
            if reg_name in preserve_regs:
                return False
    
    # Verify final register values
    for reg, val in registers.items():
        bv = getattr(state.regs, reg)
        if state.solver.eval(bv != val.data):
            return False  # Register wasn't set correctly
    
    # Verify next PC is symbolic (in our control)
    if len(state.regs.pc.variables) != 1:
        return False
    pc_var = set(state.regs.pc.variables).pop()
    return pc_var.startswith("next_pc")
This ensures:
  1. No unintended register modifications
  2. No out-of-bounds memory accesses
  3. All target registers reach desired values
  4. Control flow remains in our hands

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Symbolic State Management

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State Copying and Constraints

angr’s symbolic states are copied when exploring multiple paths: 
# Branch creates two states
if_true_state = state.copy()
if_true_state.add_constraints(condition == True)

if_false_state = state.copy()  
if_false_state.add_constraints(condition == False)
angrop uses this for:
  • Exploring conditional gadgets: Try both branches
  • Preserving states: Keep original state while constraining copies
  • Parallel chain attempts: Try multiple gadget combinations

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Constraint Satisfiability

Before building chains, check if constraints are satisfiable: 
if not state.satisfiable():
    raise RopException("Constraints are unsatisfiable")

# Check if specific constraint is possible
if state.solver.satisfiable([new_constraint]):
    state.add_constraints(new_constraint)
This avoids wasting time on impossible chains.

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Performance Considerations

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Constraint Complexity

From builder.py:361: 
@rop_utils.timeout(3)
def _build_reg_setting_chain(self, gadgets, register_dict):
    # Timeout after 3 seconds to avoid hanging on complex constraints
Symbolic execution can be slow when:
  • Many gadgets in a chain (exponential state explosion)
  • Complex arithmetic operations (hard for SMT solver)
  • Many symbolic memory accesses
angrop mitigates this by:
  1. Filtering gadgets early
  2. Limiting chain length
  3. Using timeouts
  4. Caching results

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Caching Symbolic Results

From mem_writer.py:494-515: 
class MemWriteChain:
    def __init__(self, builder, gadget, preserve_regs):
        # Symbolically execute gadget once
        self.addr_bv = BVS("addr", addr_size)
        self.data_bv = BVS("data", data_size)  
        self.chain = self._build_chain()  # Cached symbolic chain
    
    def concretize(self, addr_val, data):
        # Reuse symbolic chain, just substitute concrete values
        chain = self.chain.copy()
        for val in chain._values:
            if 'addr_' in val.variables:
                val.substitute(self.addr_bv, addr_val)
            if 'data_' in val.variables:
                val.substitute(self.data_bv, data)
        return chain
This allows writing to multiple addresses efficiently without re-solving constraints.

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Integration with Chain Building

Symbolic execution enables the entire chain building pipeline:
  1. Gadget Discovery: Symbolically execute candidate gadgets to analyze effects
  2. Capability Analysis: Determine which registers/memory each gadget can control
  3. Graph Construction: Build state-transition graph based on symbolic effects
  4. Path Finding: Search graph for paths that achieve goals
  5. Constraint Solving: Solve for concrete stack values that satisfy all constraints
  6. Verification: Symbolically re-execute to confirm correctness
From the README:
“angrop uses symbolic execution to understand the effects of gadgets and uses constraint solving and graph search for generating chains.”
This combination of techniques allows angrop to automatically build complex ROP chains that would take humans hours to construct manually.

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