前言
angr是很有名的二进制符号执行工具，网上有许多关于angr的源码解析的文章。但是好像还没有关于angr模拟执行模块的解析。而模拟执行部分也是angr中相当重要的一个部分。因此，本文将解析angr模拟执行部分的源码，来帮助大家了解angr模拟执行的基本原理。
概述
当我们用angr去符号执行的时候，最基本的几个操作如下面代码所示：导入代码（第1行）、导入二进制（第2行）、确定初始状态（第3行）、构建simulation_manager对象（第4行）、模拟执行（第5行）。而到底angr是怎么符号执行的呢？因此就需要深入simulation_manager的源码（sim_manager.py）去一探究竟了。
import angr
p = angr.Project("xxxx")
entry_state = p.factory.entry_state()
simgr = p.factory.simgr(entry_state)#simgr是simulation_manager的别名
simgr.explore(find=xxxx)
simulation_manager这个类位于angr/sim_manager.py文件里。
simulation_manager是angr中模拟执行管理器。主要的操作对象是程序的状态对象（sim_state）。状态都被放在stash里，可以往前执行、过滤、合并或者移到别的stash里。stash里可以理解为是放状态的一个列表，stash有这么几种，分别表示状态的状态：
(1) active：保存接下来要执行的状态
(2) deadended：由于某些原因不能再继续执行下去，比如没有合法的指令、下个节点的状态不可解，或者有一个非法的指令指针。
(3) pruned：当使用lazy_sovles的策略时，只有在必要的时候才去检查状态是否可解。当发现一个不可求解的节点后，将其后面的节点都优化掉，放在pruned里。
(4) unconstrained：比如PC被用户数据或者其他类型的符号变量所控制，导致不知道执行哪个指令。
(5) unsat：不可求解的状态。比如，输入同时为AAAA和BBBB。
接下来看看源码，源码中提示我们看simulation_manager的三个重要方法：step、explore、use_technique。
use_technique
angr里有自带很多启发式的路径探索方法。这个函数就是让simulation_manager能够调用外部写好的启发式路径搜索方法。官方给出的几个样例里，除了经典的深度优先搜索、也有检测内存使用情况、CMU论文里的Veritest（合并循环的状态）等等策略。
代码首先先判断tech是否属于ExplorationTechnique这个类。然后setup方法开始初始化。然后把tech防到techniques列表中去，这也意味着可以使用多种策略。这里的hookset暂时没有看懂。
def use_technique(self, tech):
"""
Use an exploration technique with this SimulationManager.
Techniques can be found in :mod:`angr.exploration_techniques`.
:param tech: An ExplorationTechnique object that contains code to modify
this SimulationManager's behavior.
:type tech: ExplorationTechnique
:return: The technique that was added, for convenience
"""
if not isinstance(tech, ExplorationTechnique):
raise SimulationManagerError
# XXX: as promised
tech.project = self._project
tech.setup(self)
HookSet.install_hooks(self, **tech._get_hooks())
self._techniques.append(tech)
return tech
explore
先来看看看explore函数的参数，有stash，n，find，avoid等参数。explore函数的功能是从某个类型的stash，比如active，开始寻找满足find条件的，需要避免avoid条件的状态，直到找了n次，或者找到了num_find个状态。然后找到的状态都会塞到find_stash里，筛选的状态都会放在avoid_stash里。
其中find和avoid参数可以是一个地址，或者一堆地址的集合或者列表，甚至可以是一个函数，以状态为输入，输出True 或者False，来表示该状态是否是要寻找的状态。如果angr的CFG作为cfg的参数并且find是一个地址或者一个列表或者集合，那么到达不了目标状态的状态就会先把提前筛选掉。
def explore(self, stash='active', n=None, find=None, avoid=None, find_stash='found', avoid_stash='avoid', cfg=None,
num_find=1, **kwargs):
"""
Tick stash "stash" forward (up to "n" times or until "num_find" states are found), looking for condition "find",
avoiding condition "avoid". Stores found states into "find_stash' and avoided states into "avoid_stash".
The "find" and "avoid" parameters may be any of:
- An address to find
- A set or list of addresses to find
- A function that takes a state and returns whether or not it matches.
If an angr CFG is passed in as the "cfg" parameter and "find" is either a number or a list or a set, then
any states which cannot possibly reach a success state without going through a failure state will be
preemptively avoided.
"""
num_find += len(self._stashes[find_stash]) if find_stash in self._stashes else 0
tech = self.use_technique(Explorer(find, avoid, find_stash, avoid_stash, cfg, num_find))
# Modify first Veritesting so that they can work together.
deviation_filter_saved = None
for t in self._techniques:
if isinstance(t,Veritesting):
deviation_filter_saved = t.options.get("deviation_filter",None)
if deviation_filter_saved is not None:
t.options["deviation_filter"] = lambda s: tech.find(s) or tech.avoid(s) or deviation_filter_saved(s)
else:
t.options["deviation_filter"] = lambda s: tech.find(s) or tech.avoid(s)
break
try:
self.run(stash=stash, n=n, **kwargs)
finally:
self.remove_technique(tech)
for t in self._techniques:
if isinstance(t,Veritesting):
if deviation_filter_saved is None:
del t.options["deviation_filter"]
else:
t.options["deviation_filter"] = deviation_filter_saved
break
return self
宏观来看explore函数分为三部分：初始化，兼容veritest策略，探索（run）。兼容veritest策略的代码占了很多，对于理解veritest策略与其他策略的关系很有帮助，但是对我们理解符号执行帮助较小，这里就不赘述了。
首先，更新num_find的参数为设定的num_find参数加上找到的状态。接着，用传入的参数find，avoid等生成Explorer对象，然后再用use_technique方法生成一个tech对象。这里为什么要生成Explore对象，然后再用use_technique方法？
Explorer对象继承了ExplorationTechnique类，所以他也是一种探索策略，并且是一种最基础的策略。
而符号执行过程中，可以使用多种策略，那么如何综合这些策略呢？angr是把他们都放在了simulationmanager里的.techniques列表里，而use_technique方法的作用正是把策略对象放进这个techniques列表里。
num_find += len(self._stashes[find_stash]) if find_stash in self._stashes else 0
tech = self.use_technique(Explorer(find, avoid, find_stash, avoid_stash, cfg, num_find))
初始化后，接下来就是去探索状态部分。简单的一个try，finally语句。不论run的结果如何，最后都把基础探索策略移出_techniques列表里。
try:
self.run(stash=stash, n=n, **kwargs)
finally:
self.remove_technique(tech)
run函数的代码如下，思路很简单，根据当前的探索策略，一直探索，直到到达一个完整的状态。如果策略里没定义完整的策略，那就把stash里的状态都跑完。run里涉及到了后面会讲的step函数，这里可以先简单理解为单步符号执行。
def run(self, stash='active', n=None, until=None, **kwargs):
"""
Run until the SimulationManager has reached a completed state, according to
the current exploration techniques. If no exploration techniques that define a completion
state are being used, run until there is nothing left to run.
:param stash: Operate on this stash
:param n: Step at most this many times
:param until: If provided, should be a function that takes a SimulationManager and
returns True or False. Stepping will terminate when it is True.
:return: The simulation manager, for chaining.
:rtype: SimulationManager
"""
for _ in (itertools.count() if n is None else range(0, n)):
if not self.complete() and self._stashes[stash]:
self.step(stash=stash, **kwargs)
if not (until and until(self)):
continue
break
return self
step
最后就是这个比较复杂的step函数了，可以看作是符号执行的基本单元了。相比explore函数的参数多了selector_func，step_func，successor_func，filter_func，until。这些参数的意思代码注释写得比较清楚了，就简单翻译一下。这些参数都是一个以状态为输入，返回各种东西（比如bool值，后继节点等）的一个函数，类似下面的代码。
def fun(state):
if state.addr == xxxx:
return True
else:
return False
- selector_func：如果为True，将会继续步进，反之会被保留。
- successor_func：返回的是后继节点，后面将会使用这些后继节点去符号执行。反之，则是使用project.factory.successors的后继节点。
- filter_func：返回的是stash的名字。filter_func的主要作用是给状态分类，分到各个stash里去。
- step_func：与前面参数不同，输入是为simulation_manger对象，并返回simulation_manager对象。这个函数会在simulation_manager对象每次step的时候被调用。
def step(self, stash='active', n=None, selector_func=None, step_func=None,
successor_func=None, until=None, filter_func=None, **run_args):
"""
Step a stash of states forward and categorize the successors appropriately.
The parameters to this function allow you to control everything about the stepping and
categorization process.
:param stash: The name of the stash to step (default: 'active')
:param selector_func: If provided, should be a function that takes a state and returns a
boolean. If True, the state will be stepped. Otherwise, it will be
kept as-is.
:param step_func: If provided, should be a function that takes a SimulationManager and
returns a SimulationManager. Will be called with the SimulationManager
at every step. Note that this function should not actually perform any
stepping - it is meant to be a maintenance function called after each step.
:param successor_func: If provided, should be a function that takes a state and return its successors.
Otherwise, project.factory.successors will be used.
:param filter_func: If provided, should be a function that takes a state and return the name
of the stash, to which the state should be moved.
:param until: (DEPRECATED) If provided, should be a function that takes a SimulationManager and
returns True or False. Stepping will terminate when it is True.
:param n: (DEPRECATED) The number of times to step (default: 1 if "until" is not provided)
Additionally, you can pass in any of the following keyword args for project.factory.successors:
:param jumpkind: The jumpkind of the previous exit
:param addr: An address to execute at instead of the state's ip.
:param stmt_whitelist: A list of stmt indexes to which to confine execution.
:param last_stmt: A statement index at which to stop execution.
:param thumb: Whether the block should be lifted in ARM's THUMB mode.
:param backup_state: A state to read bytes from instead of using project memory.
:param opt_level: The VEX optimization level to use.
:param insn_bytes: A string of bytes to use for the block instead of the project.
:param size: The maximum size of the block, in bytes.
:param num_inst: The maximum number of instructions.
:param traceflags: traceflags to be passed to VEX. Default: 0
:returns: The simulation manager, for chaining.
:rtype: SimulationManager
"""
l.info("Stepping %s of %s", stash, self)
# 8<----------------- Compatibility layer -----------------
if n is not None or until is not None:
if once('simgr_step_n_until'):
print("\x1b[31;1mDeprecation warning: the use of `n` and `until` arguments is deprecated. "
"Consider using simgr.run() with the same arguments if you want to specify "
"a number of steps or an additional condition on when to stop the execution.\x1b[0m")
return self.run(stash, n, until, selector_func=selector_func, step_func=step_func,
successor_func=successor_func, filter_func=filter_func, **run_args)
# ------------------ Compatibility layer ---------------->8
bucket = defaultdict(list)
for state in self._fetch_states(stash=stash):
goto = self.filter(state, filter_func=filter_func)
if isinstance(goto, tuple):
goto, state = goto
if goto not in (None, stash):
bucket[goto].append(state)
continue
if not self.selector(state, selector_func=selector_func):
bucket[stash].append(state)
continue
pre_errored = len(self._errored)
successors = self.step_state(state, successor_func=successor_func, **run_args)
# handle degenerate stepping cases here. desired behavior:
# if a step produced only unsat states, always add them to the unsat stash since this usually indicates a bug
# if a step produced sat states and save_unsat is False, drop the unsats
# if a step produced no successors, period, add the original state to deadended
# first check if anything happened besides unsat. that gates all this behavior
if not any(v for k, v in successors.items() if k != 'unsat') and len(self._errored) == pre_errored:
# then check if there were some unsats
if successors.get('unsat', []):
# only unsats. current setup is acceptable.
pass
else:
# no unsats. we've deadended.
bucket['deadended'].append(state)
continue
else:
# there were sat states. it's okay to drop the unsat ones if the user said so.
if not self._save_unsat:
successors.pop('unsat', None)
for to_stash, successor_states in successors.items():
bucket[to_stash or stash].extend(successor_states)
self._clear_states(stash=stash)
for to_stash, states in bucket.items():
self._store_states(to_stash or stash, states)
if step_func is not None:
return step_func(self)
return self
首先，从stash里取出一个状态，调用filter函数看下该状态最后要去哪个stash里，如果不是当前的stash，则把该状态塞到应该放的stash的地方，然后取下一个状态。调用selector函数，选择要保留的状态。
bucket = defaultdict(list)
# 依次从stash里取出状态
for state in self._fetch_states(stash=stash):
goto = self.filter(state, filter_func=filter_func) # 返回的是个元组，（状态该去的stash，状态）
if isinstance(goto, tuple):
goto, state = goto
#如果要去的stash不是当前的stash，也不是None，
if goto not in (None, stash):
# 那么把他放进该去的stash里，就不管他了。也就筛选掉了。
bucket[goto].append(state) #
continue
# 如果selector函数返回False，则需要保留该状态到当前的stash
if not self.selector(state, selector_func=selector_func):
# 保留状态
bucket[stash].append(state)
continue
如果没有触发selector或者filter，就去找后继节点。这里调用了step_state函数。
for state in self._fetch_states(stash=stash):
...
successors = self.step_state(state, successor_func=successor_func, **run_args)
step_state函数如下所示，这个函数主要是处理后继节点的状态。将不可解的状态，无约束的状态都放在相应的stash里。
def step_state(self, state, successor_func=None, **run_args):
"""
Don't use this function manually - it is meant to interface with exploration techniques.
"""
try:
successors = self.successors(state, successor_func=successor_func, **run_args)
stashes = {None: successors.flat_successors,
'unsat': successors.unsat_successors,
'unconstrained': successors.unconstrained_successors}
except:
...
return stashes
由于step_state函数可能会发生很多错误，因此后续的代码是去做后继节点错误状态的处理。
for state in self._fetch_states(stash=stash):
...
#如果有后继节点有任何一个unsat状态或者发生了新的错误
if not any(v for k, v in successors.items() if k != 'unsat') and len(self._errored) == pre_errored:
#对于unsat状态，就先不管他
if successors.get('unsat', []):
# only unsats. current setup is acceptable.
pass
else:
#如果不是unsat，那说明遇到某些原因终止了，把该状态加到deadended的stash里去。
bucket['deadended'].append(state)
continue
else:
# 如果没有设置保留unsat状态，就把后继节点的unsat状态丢出去。
if not self._save_unsat:
successors.pop('unsat', None)
接下来就把后继节点加到bucket的to_stash或者stash里去。自此，这个for循环就结束了。
for state in self._fetch_states(stash=stash):
...
for to_stash, successor_states in successors.items():
bucket[to_stash or stash].extend(successor_states)
剩下就是一些收尾工作，清空当前stash里的状态，然后再把bucket的内容存到simulation_manager对象的stash里去。
self._clear_states(stash=stash)
for to_stash, states in bucket.items():
self._store_states(to_stash or stash, states)
如果有设置step_func，就去调用step_func。由此也能看到step_func是在step函数最后调用的。
if step_func is not None:
return step_func(self)
总结
angr模拟执行部分的主要代码就解析到这里了，希望大家能够对angr的模拟执行有更深的理解。在理解了angr这部分的内容之后，应该能够比较容易地扩展angr的探索策略。
本文只涉及了sim_manager.py中的几个重要地方，如果要熟练使用simulation_manager的各种功能的话，比如split，merge等等，还需要再看看源码。
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