Working with Data and Conventions

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Frequently, you'll want to access structured data from the program you're analyzing. angr has several features to make this less of a headache.
Working with types
angr has a system for representing types. These SimTypes are found in angr.types - an instance of any of these classes represents a type. Many of the types are incomplete unless they are supplamented with a SimState - their size depends on the architecture you're running under. You may do this with ty.with_arch(arch), which returns a copy of itself, with the architecture specified.
angr also has a light wrapper around pycparser, which is a C parser. This helps with getting instances of type objects:
>>> import angr, monkeyhex
​
# note that SimType objects have their __repr__ defined to return their c type name,
# so this function actually returned a SimType instance.
>>> angr.types.parse_type('int')
int
​
>>> angr.types.parse_type('char **')
char**
​
>>> angr.types.parse_type('struct aa {int x; long y;}')
struct aa
​
>>> angr.types.parse_type('struct aa {int x; long y;}').fields
OrderedDict([('x', int), ('y', long)])
Additionally, you may parse C definitions and have them returned to you in a dict, either of variable/function declarations or of newly defined types:
>>> angr.types.parse_defns("int x; typedef struct llist { char* str; struct llist *next; } list_node; list_node *y;")
{'x': int, 'y': struct llist*}
​
>>> defs = angr.types.parse_types("int x; typedef struct llist { char* str; struct llist *next; } list_node; list_node *y;")
>>> defs
{'struct llist': struct llist, 'list_node': struct llist}
​
# if you want to get both of these dicts at once, use parse_file, which returns both in a tuple.
>>> angr.types.parse_file("int x; typedef struct llist { char* str; struct llist *next; } list_node; list_node *y;")
({'x': int, 'y': struct llist*},
 {'struct llist': struct llist, 'list_node': struct llist})
​
>>> defs['list_node'].fields
OrderedDict([('str', char*), ('next', struct llist*)])
​
>>> defs['list_node'].fields['next'].pts_to.fields
OrderedDict([('str', char*), ('next', struct llist*)])
​
# If you want to get a function type and you don't want to construct it manually,
# you can use parse_type
>>> angr.types.parse_type("int (int y, double z)")
(int, double) -> int
And finally, you can register struct definitions for future use:
>>> angr.types.register_types(angr.types.parse_type('struct abcd { int x; int y; }'))
>>> angr.types.register_types(angr.types.parse_types('typedef long time_t;'))
>>> angr.types.parse_defns('struct abcd a; time_t b;')
{'a': struct abcd, 'b': long}
These type objects aren't all that useful on their own, but they can be passed to other parts of angr to specify data types.
Accessing typed data from memory
Now that you know how angr's type system works, you can unlock the full power of the state.mem interface! Any type that's registered with the types module can be used to extract data from memory.
>>> p = angr.Project('examples/fauxware/fauxware')
>>> s = p.factory.entry_state()
>>> s.mem[0x601048]
<<untyped> <unresolvable> at 0x601048>
​
>>> s.mem[0x601048].long
<long (64 bits) <BV64 0x4008d0> at 0x601048>
​
>>> s.mem[0x601048].long.resolved
<BV64 0x4008d0>
​
>>> s.mem[0x601048].long.concrete
0x4008d0
​
>>> s.mem[0x601048].struct.abcd
<struct abcd {
  .x = <BV32 0x4008d0>,
  .y = <BV32 0x0>
} at 0x601048>
​
>>> s.mem[0x601048].struct.abcd.x
<int (32 bits) <BV32 0x4008d0> at 0x601048>
​
>>> s.mem[0x601048].struct.abcd.y
<int (32 bits) <BV32 0x0> at 0x60104c>
​
>>> s.mem[0x601048].deref
<<untyped> <unresolvable> at 0x4008d0>
​
>>> s.mem[0x601048].deref.string
<string_t <BV64 0x534f534e45414b59> at 0x4008d0>
​
>>> s.mem[0x601048].deref.string.resolved
<BV64 0x534f534e45414b59>
​
>>> s.mem[0x601048].deref.string.concrete
b'SOSNEAKY'
The interface works like this:
You first use [array index notation] to specify the address you'd like to load from
If at that address is a pointer, you may access the deref property to return a SimMemView at the address present in memory.
You then specify a type for the data by simply accessing a property of that name. For a list of supported types, look at state.mem.types.
You can then refine the type. Any type may support any refinement it likes. Right now the only refinements supported are that you may access any member of a struct by its member name, and you may index into a string or array to access that element.
If the address you specified initially points to an array of that type, you can say .array(n) to view the data as an array of n elements.
Finally, extract the structured data with .resolved or .concrete. .resolved will return bitvector values, while .concrete will return integer, string, array, etc values, whatever best represents the data.
Alternately, you may store a value to memory, by assigning to the chain of properties that you've constructed. Note that because of the way Python works, x = s.mem[...].prop; x = val will NOT work, you must say s.mem[...].prop = val.
If you define a struct using register_types(parse_type(struct_expr)), you can access it here as a type:
>>> s.mem[p.entry].struct.abcd
<struct abcd {
  .x = <BV32 0x8949ed31>,
  .y = <BV32 0x89485ed1>
} at 0x400580>
Working with Calling Conventions
A calling convention is the specific means by which code passes arguments and return values through function calls. angr's abstraction of calling conventions is called SimCC. You can construct new SimCC instances through the angr object factory, with p.factory.cc(...). This will give a calling convention which is guessed based your guest architecture and OS. If angr guesses wrong, you can explicitly pick one of the calling conventions in the angr.calling_conventions module.
If you have a very wacky calling convention, you can use angr.calling_conventions.SimCCUsercall. This will ask you to specify locations for the arguments and the return value. To do this, use instances of the SimRegArg or SimStackArg classes. You can find them in the factory - p.factory.cc.Sim*Arg.
Once you have a SimCC object, you can use it along with a SimState object and a function prototype (a SimTypeFunction) to extract or store function arguments more cleanly. Take a look at the API documentation for details. Alternately, you can pass it to an interface that can use it to modify its own behavior, like p.factory.call_state, or...
Callables
Callables are a Foreign Functions Interface (FFI) for symbolic execution. Basic callable usage is to create one with myfunc = p.factory.callable(addr), and then call it! result = myfunc(args, ...) When you call the callable, angr will set up a call_state at the given address, dump the given arguments into memory, and run a path_group based on this state until all the paths have exited from the function. Then, it merges all the result states together, pulls the return value out of that state, and returns it.
All the interaction with the state happens with the aid of a SimCC and a SimTypeFunction, to tell where to put the arguments and where to get the return value. It will try to use a sane default for the architecture, but if you'd like to customize it, you can pass a SimCC object in the cc keyword argument when constructing the callable. The SimTypeFunction is required - you must pass the prototype parameter. If you pass a string to this parameter it will be parsed as a function declaration.
You can pass symbolic data as function arguments, and everything will work fine. You can even pass more complicated data, like strings, lists, and structures as native Python data (use tuples for structures), and it'll be serialized as cleanly as possible into the state. If you'd like to specify a pointer to a certain value, you can wrap it in a PointerWrapper object, available as p.factory.callable.PointerWrapper. The exact semantics of how pointer-wrapping work are a little confusing, but they can be boiled down to "unless you specify it with a PointerWrapper or a specific SimArrayType, nothing will be wrapped in a pointer automatically unless it gets to the end and it hasn't yet been wrapped in a pointer yet and the original type is a string, array, or tuple." The relevant code is actually in SimCC - it's the setup_callsite function.
If you don't care for the actual return value of the call, you can say func.perform_call(arg, ...), and then the properties func.result_state and func.result_path_group will be populated. They will actually be populated even if you call the callable normally, but you probably care about them more in this case!
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Outline
Working with types
Accessing typed data from memory
Working with Calling Conventions
Callables