* You can pass a list of arguments through `args` and a dictionary of environment variables through `env` into `entry_state` and `full_init_state`. The values in these structures can be strings or bitvectors, and will be serialized into the state as the arguments and environment to the simulated execution. The default `args` is an empty list, so if the program you're analyzing expects to find at least an `argv[0]`, you should always provide that!
* If you'd like to have `argc` be symbolic, you can pass a symbolic bitvector as `argc` to the `entry_state` and `full_init_state` constructors. Be careful, though: if you do this, you should also add a constraint to the resulting state that your value for argc cannot be larger than the number of args you passed into `args`.
* To use the call state, you should call it with `.call_state(addr, arg1, arg2, ...)`, where `addr` is the address of the function you want to call and `argN` is the Nth argument to that function, either as a python integer, string, or array, or a bitvector. If you want to have memory allocated and actually pass in a pointer to an object, you should wrap it in an PointerWrapper, i.e. `angr.PointerWrapper("point to me!")`. The results of this API can be a little unpredictable, but we're working on it.
bv = state.solver.BVV(0x1234, 32) #Create BV of 32bits with the value "0x1234"
state.solver.eval(bv) #Convert BV to python int
bv.zero_extend(30) #Will add 30 zeros on the left of the bitvector
bv.sign_extend(30) #Will add 30 zeros or ones on the left of the BV extending the sign
```
### Symbolic BitVectors & Constraints
```python
x = state.solver.BVS("x", 64) #Symbolic variable BV of length 64
y = state.solver.BVS("y", 64)
#Symbolic oprations
tree = (x + 1) / (y + 2)
tree #<BV64(x_9_64+0x1)/(y_10_64+0x2)>
tree.op #'__floordiv__' Access last operation
tree.args #(<BV64x_9_64+0x1>, <BV64y_10_64+0x2>)
tree.args[0].op #'__add__' Access of dirst arg
tree.args[0].args #(<BV64x_9_64>, <BV640x1>)
tree.args[0].args[1].op #'BVV'
tree.args[0].args[1].args #(1, 64)
#Symbolic constraints solver
state = proj.factory.entry_state() #Get a fresh state without constraints
input = state.solver.BVS('input', 64)
operation = (((input + 4) * 3) >> 1) + input
output = 200
state.solver.add(operation == output)
state.solver.eval(input) #0x3333333333333381
state.solver.add(input <2**32)
state.satisfiable() #False
#Solver solutions
solver.eval(expression) #one possible solution
solver.eval_one(expression) #solution to the given expression, or throw an error if more than one solution is possible.
solver.eval_upto(expression, n) #n solutions to the given expression, returning fewer than n if fewer than n are possible.
solver.eval_atleast(expression, n) #n solutions to the given expression, throwing an error if fewer than n are possible.
solver.eval_exact(expression, n) #n solutions to the given expression, throwing an error if fewer or more than are possible.
solver.min(expression) #minimum possible solution to the given expression.
solver.max(expression) #maximum possible solution to the given expression.
```
### Hooking
```python
>>> stub_func = angr.SIM_PROCEDURES['stubs']['ReturnUnconstrained'] # this is a CLASS
>>> proj.hook(0x10000, stub_func()) # hook with an instance of the class
>>> proj.is_hooked(0x10000) # these functions should be pretty self-explanitory
True
>>> proj.hooked_by(0x10000)
<ReturnUnconstrained>
>>> proj.unhook(0x10000)
>>> @proj.hook(0x20000, length=5)
... def my_hook(state):
... state.regs.rax = 1
>>> proj.is_hooked(0x20000)
True
```
Furthermore, you can use `proj.hook_symbol(name, hook)`, providing the name of a symbol as the first argument, to hook the address where the symbol lives
