创建: 2026-04-27 17:01
更新: 2026-05-11 16:39
目录:
☆ 背景介绍
☆ 准备工作
1) wasm -> c
2) c -> o
3) 反编译.o
☆ h5_worker_cff_pub.py
1) 整体框架
2) 找出有效块(真实块)
3) 找出状态变量state_var
4) 建立有效块到有效块的映射关系
5) 汇编级修改控制流
6) func54_vodplay反CFF结果
☆ AI反CFF
☆ 后记
☆ 背景介绍
参看
《WEB前端逆向TS PES NALU解密》
https://www.52pojie.cn/thread-1957747-1-1.html
https://scz.617.cn/web/202408231518.txt
文中提及h5.worker.wasm，某些解密算法在wasm中实现。逆向工程发现，该wasm用Emscripten开发所得，且被实施过"控制流平坦化"，比如func54_vodplay。
想反CFF，再理解func54_vodplay的逻辑。这是两年前搞wasm逆向工程时冒出来的想法，当时没有这方面经验，未展开，只在Chrome F12中动态调试过该函数。
据说Angr可直接模拟执行wasm，但不如模拟执行ELF成熟，未求证，未实践。
有种变通方案，用wasm2c得到.c，编译出.o，用Angr模拟执行.o，对.o反CFF。
☆ 准备工作
1) wasm -> c
wasm2c -o h5_worker.c h5.worker.wasm
上述命令可得到h5_worker.c、h5_worker.h。比较wasm2c 1.0.34与1.0.36的输出，有差别，但单就所关心的w2c_h50x2Eworker_0x5Fvodplay_0()，完全一样。
2) c -> o
将h5_worker.c编译成h5_worker_pub.o。
3) 反编译.o
用IDA或Ghidra分析h5_worker_pub.o。一般来说，从.c到.o，再反汇编、反编译，不会提供增益信息，相反，可能损失信息。当初这么干，主要是为借助IDA的符号改名、交叉引用、Findcrypt或Signsrch插件，正是这样注意到TEA算法。
wasm-objdump -j Export -x h5.worker.wasm | less
查看wasm导出表，对h5_worker.c/h5_worker_pub.o中若干函数进行简化表述
func54_vodplay w2c_h50x2Eworker_0x5Fvodplay_0
func121_malloc w2c_h50x2Eworker_f121
func123_memcpy w2c_h50x2Eworker_0x5Fmemcpy_0
func30 w2c_h50x2Eworker_f30
func120_free w2c_h50x2Eworker_0x5Ffree_0
func77 w2c_h50x2Eworker_f77
func52 w2c_h50x2Eworker_f52
func125_memset w2c_h50x2Eworker_0x5Fmemset_0
func60_TEA w2c_h50x2Eworker_f60
func59 w2c_h50x2Eworker_f59
func58_TEA w2c_h50x2Eworker_f58
func40 w2c_h50x2Eworker_f40
func114 w2c_h50x2Eworker_f114
在IDA中F5查看func54_vodplay，伪代码约1925行，大量嵌套的while、if以及状态变量，表明其被实施过"控制流平坦化"。
完整测试用例
https://scz.617.cn/web/202604271701.txt
https://scz.617.cn/web/202604271701.7z
☆ h5_worker_cff_pub.py
#!/usr/bin/env python
# -*- coding: cp936 -*-
#
# h5_worker_cff_pub.py
#
import logging
import collections
import ida_funcs, ida_gdl, ida_ua, ida_bytes, ida_lines, ida_idp
import ida_idaapi, ida_kernwin, ida_graph, idautils, idc, ida_nalt
import ida_xref, ida_allins
import keystone
import angr, claripy
def get_block_from_ea ( ea ) :
f = ida_funcs.get_func( ea )
if not f :
assert False
blocks = ida_gdl.FlowChart( f )
for block in blocks :
if block.start_ea <= ea < block.end_ea :
return block
assert False
def set_block_color ( ea, bg_color=0xffcc33 ) :
block = get_block_from_ea( ea )
f = ida_funcs.get_func( block.start_ea )
if not f :
assert False
node_info = ida_graph.node_info_t()
node_info.bg_color \
= bg_color
ida_graph.set_node_info( f.start_ea, block.id, node_info, ida_graph.NIF_BG_COLOR )
def set_insn_color ( ea, color=0x00ffff ):
ida_nalt.set_item_color( ea, color )
def get_dispatchers ( addr ) :
dispatchers = set()
queue = collections.deque()
block = get_block_from_ea( addr )
queue.append( ( block, [] ) )
while len( queue ) > 0 :
block, path = queue.popleft()
if block.start_ea in path :
dispatchers.add( block.start_ea )
continue
path = path + [block.start_ea]
queue.extend( ( succ, path ) for succ in block.succs() )
dispatchers = list( dispatchers )
dispatchers.sort()
return dispatchers
def get_ret_block ( addr ) :
f = ida_funcs.get_func( addr )
if not f :
assert False
blocks = ida_gdl.FlowChart( f )
for block in blocks :
last_insn = ida_bytes.prev_head( block.end_ea, block.start_ea )
if last_insn == ida_idaapi.BADADDR :
continue
insn = ida_ua.insn_t()
if ida_ua.decode_insn( insn, last_insn ) == 0 :
continue
if ida_idp.is_ret_insn( insn ) :
return block.start_ea
return None
def is_block_0 ( block ) :
block_size = block.end_ea - block.start_ea
if block_size == 0x52 :
return False
if block_size < 0x31 :
return False
heads = list( idautils.Heads( block.start_ea, block.end_ea ) )
if len( heads ) < 2 :
return False
ea_last = heads[-1]
ea_prev = heads[-2]
insn_last = ida_ua.insn_t()
if ida_ua.decode_insn( insn_last, ea_last ) <= 0 :
return False
if insn_last.itype != ida_allins.NN_jmp :
return False
insn_prev = ida_ua.insn_t()
if ida_ua.decode_insn( insn_prev, ea_prev ) <= 0 :
return False
if not ida_idp.is_call_insn( insn_prev ) :
return False
target_ea = ida_idaapi.BADADDR
op = insn_prev.ops[0]
if op.type in [ida_ua.o_near, ida_ua.o_far] :
target_ea = op.addr
if target_ea == ida_idaapi.BADADDR :
target_ea = ida_xref.get_first_fcref_from( ea_prev )
if target_ea != ida_idaapi.BADADDR :
name = ida_name.get_name(target_ea)
if name :
clean_name = name.lstrip( '_.' ).split( '@' )[0]
if clean_name == "i32_store" :
return True
return False
def is_block_1 ( block ) :
heads = list( idautils.Heads( block.start_ea, block.end_ea ) )
if len( heads ) < 3 :
return False
ea_last = heads[-1]
ea_prev = heads[-2]
if ida_bytes.get_bytes( ea_last, 2 ) != b'\x74\x05' :
return False
insn_prev = ida_ua.insn_t()
if ida_ua.decode_insn( insn_prev, ea_prev ) <= 0 :
return False
if insn_prev.itype != ida_allins.NN_cmp :
return False
op0 = insn_prev.ops[0]
op1 = insn_prev.ops[1]
if op1.type != ida_ua.o_imm or op1.value != 0 :
return False
if op0.type not in ( ida_ua.o_displ, ida_ua.o_phrase ) :
return False
if op0.addr not in (-0x3c, -60, 0xc4, 0xffffffc4, 0xffffffffffffffc4) :
return False
for ea in reversed( heads[:-2] ) :
insn = ida_ua.insn_t()
if ida_ua.decode_insn( insn, ea ) <= 0 :
continue
if ida_idp.is_call_insn( insn ) :
target_ea = ida_idaapi.BADADDR
op = insn.ops[0]
if op.type in [ida_ua.o_near, ida_ua.o_far] :
target_ea = op.addr
if target_ea == ida_idaapi.BADADDR :
target_ea = ida_xref.get_first_fcref_from( ea )
if target_ea != ida_idaapi.BADADDR :
name = ida_name.get_name( target_ea )
if name :
clean_name = name.lstrip( '_.' ).split( '@' )[0]
if clean_name.startswith( ( "i32_load", "i64_load" ) ) :
return True
return False
return False
def get_real_block ( func_ea ) :
f = ida_funcs.get_func( func_ea )
if not f :
assert False
dispatchers \
= get_dispatchers( func_ea )
print( f"dispatchers[{len(dispatchers)}]:" )
for i, dispatcher in enumerate( dispatchers ) :
print( f"[{i}] {dispatcher:#x}" )
set_block_color( dispatcher, 0xff00ff )
real_block_list \
= [func_ea, 0xbfe7a]
blocks = ida_gdl.FlowChart( f )
for block in blocks :
if block.start_ea in dispatchers :
continue
if is_block_0( block ) :
real_block_list.append( block.start_ea )
elif is_block_1( block ) :
real_block_list.append( block.start_ea )
real_block_list.sort()
ret_block_ea \
= get_ret_block( func_ea )
assert ret_block_ea is not None
real_block_list.append( ret_block_ea )
print( [hex(x) for x in real_block_list] )
print( f"real_block_list[{len(real_block_list)}]:" )
for i, ea in enumerate( real_block_list ) :
set_block_color( ea )
print( f"[{i}] {ea:#x}" )
return real_block_list
def get_info_from_jz_angr ( state ) :
block = state.project.factory.block( state.addr, num_inst=1 )
target_false \
= state.addr + block.size
target_true \
= None
for target in block.vex.constant_jump_targets :
if target != target_false :
target_true = target
break
if target_true is None :
assert False
return ( target_true, target_false )
def find_jz_succ ( proj, base_addr, init_state, target, real_block_list, real_block_ea, flow, debug_ea, debug_path, log ) :
init_state_copy \
= init_state.copy()
if debug_ea is not None :
debug_path[debug_ea].append( 'find_jz_succ' )
# ea = init_state_copy.addr - base_addr
# debug_path[debug_ea].append( hex(ea) )
if log :
print( 'find_jz_succ' )
init_state_copy.regs.rip \
= target
sm = proj.factory.simulation_manager( init_state_copy )
sm.step( num_inst=1 )
state = sm.active[0]
i = -1
while len( sm.active ) > 0 :
for state in sm.active :
i = i + 1
ea = state.addr - base_addr
if debug_ea is not None :
debug_path[debug_ea].append( f"{i} {ea:#x}" )
if log :
print( f"{i} {ea:#x}" )
if ea in real_block_list :
if debug_ea is not None :
debug_path[debug_ea].append( 'find_jz_succ 1' )
if log :
print( 'find_jz_succ 1' )
succs = flow[real_block_ea]
if ea not in succs :
succs.append( ea )
return ea
sm.step( num_inst=1 )
return None
def find_cycle_ex ( sequence, min_occurrences=16 ) :
n = len( sequence )
for i in range( n ) :
for j in range( i+1, n ) :
if sequence[j] == sequence[i] :
cycle_len = j - i
if sequence[i:i+cycle_len] == sequence[j:j+cycle_len] :
occurrences = 2
next_start = j + cycle_len
while (
next_start + cycle_len <= n and
sequence[next_start:next_start+cycle_len] == sequence[i:i+cycle_len]
) :
occurrences += 1
next_start += cycle_len
if occurrences >= min_occurrences :
cycle = sequence[i:i+cycle_len]
return cycle
return None
def remove_cycle_ex ( sequence, min_occurrences=16 ) :
n = len( sequence )
for i in range( n ) :
for j in range( i+1, n ) :
if sequence[j] == sequence[i] :
cycle_len = j - i
if sequence[i:i+cycle_len] == sequence[j:j+cycle_len] :
occurrences = 2
next_start = j + cycle_len
while (
next_start + cycle_len <= n and
sequence[next_start:next_start+cycle_len] == sequence[i:i+cycle_len]
) :
occurrences += 1
next_start += cycle_len
if occurrences >= min_occurrences :
clean = sequence[:i]
return clean
return sequence[:]
def find_next_block ( proj, base_addr, init_state, real_block_ea, real_block_list, flow, debug_ea, debug_path, log ) :
init_state_copy = init_state.copy()
if debug_ea is not None :
debug_path[debug_ea].append( 'find_next_block' )
# ea = init_state_copy.addr - base_addr
# debug_path[debug_ea].append( hex(ea) )
if log :
print( 'find_next_block' )
sm = proj.factory.simulation_manager( init_state_copy )
i = -1
while len( sm.active ) > 0 :
for state in sm.active :
i = i + 1
ea = state.addr - base_addr
if debug_ea is not None :
debug_path[debug_ea].append( f"{i} {ea:#x}" )
if log :
print( f"{i} {ea:#x}" )
if ea == real_block_ea :
if debug_ea is not None :
debug_path[debug_ea].append( 'find_next_block 1' )
if log :
print( 'find_next_block 1' )
block = get_block_from_ea( ea )
heads = list( idautils.Heads( block.start_ea, block.end_ea ) )
if len( heads ) < 2 :
assert False
ea_last = heads[-1]
init_state_2 \
= state.copy()
sm_2 = proj.factory.simulation_manager( init_state_2 )
sm_2.step( num_inst=1 )
internal_path \
= []
j = -1
while len( sm_2.active ) > 0 :
for state_2 in sm_2.active :
j = j + 1
ea_2 = state_2.addr - base_addr
if debug_ea is not None :
debug_path[debug_ea].append( f"{i} {j} {ea_2:#x}" )
if log :
print( f"{i} {j} {ea_2:#x}" )
internal_path.append( ea_2 )
if ea_2 in real_block_list :
if debug_ea is not None :
debug_path[debug_ea].append( 'find_next_block 2' )
if log :
print( 'find_next_block 2' )
print( f"[0] ea_2={ea_2:#x}" )
succs = flow[real_block_ea]
if len( sm_2.active ) > 1 :
next_array = []
for state_3 in sm_2.active :
ea_3 = state_3.addr - base_addr
if ea_3 in real_block_list and ea_3 not in succs :
next_array.append( ea_3 )
if len( next_array ) > 1 :
print( "next_array", [hex(t) for t in next_array] )
assert False
if ea_2 not in succs :
succs.append( ea_2 )
print( f"B {real_block_ea:#x} -> {ea_2:#x}" )
return
if ea_2 != ea_last :
continue
insn = state_2.block().capstone.insns[0]
if insn.mnemonic == "je" :
if debug_ea is not None :
debug_path[debug_ea].append( 'find_next_block 3' )
if log :
print( 'find_next_block 3' )
print( f"[1] ea_2={ea_2:#x}" )
target_true, target_false \
= get_info_from_jz_angr( state_2 )
state_true = state_2.copy()
state_true_succ \
= find_jz_succ( proj, base_addr, state_true, target_true, real_block_list, real_block_ea, flow, debug_ea, debug_path, log )
state_false = state_2.copy()
state_false_succ \
= find_jz_succ( proj, base_addr, state_false, target_false, real_block_list, real_block_ea, flow, debug_ea, debug_path, log )
if state_true_succ is None or state_false_succ is None :
print( f"jz {ea_2:#x} {state_true_succ} {state_true_false}" )
assert False
print( f"jz {real_block_ea:#x} -> {state_true_succ:#x}, {state_false_succ:#x}" )
return
cycle = find_cycle_ex( internal_path, min_occurrences=8 )
if cycle :
for item in cycle :
sm_2.move(
from_stash='active',
to_stash='deadended',
filter_func=lambda s: s.addr - base_addr == item
)
internal_path = remove_cycle_ex( internal_path, min_occurrences=8 )
sm_2.step( num_inst=1 )
return
sm.step( num_inst=1 )
def get_state ( proj, base_addr, init_state, real_block_ea, debug_ea, debug_path, log ) :
init_state_copy = init_state.copy()
if debug_ea is not None :
debug_path[debug_ea].append( 'get_state' )
# ea = init_state.addr - base_addr
# debug_path[debug_ea].append( hex(ea) )
if log :
print( 'get_state' )
sm = proj.factory.simulation_manager( init_state_copy )
i = -1
while len( sm.active ) > 0 :
for state in sm.active :
i = i + 1
ea = state.addr - base_addr
if debug_ea is not None :
debug_path[debug_ea].append( f"{i} {ea:#x}" )
if log :
print( f"{i} {ea:#x}" )
if ea == real_block_ea :
debug_path[debug_ea].append( 'get_state 1' )
if log :
print( 'get_state 1' )
return state.copy()
sm.step( num_inst=1 )
assert False
return None
def find_call ( func_ea ) :
hook_addrs = set()
ihelp_calls = collections.defaultdict( list )
f = ida_funcs.get_func( func_ea )
if not f :
assert False
for ea in idautils.FuncItems( func_ea ) :
insn = ida_ua.insn_t()
if ida_ua.decode_insn( insn, ea ) > 0 and ida_idp.is_call_insn( insn ) :
target_ea = ida_idaapi.BADADDR
op = insn.ops[0]
if op.type in [ida_ua.o_near, ida_ua.o_far]:
target_ea = op.addr
if target_ea == ida_idaapi.BADADDR :
target_ea = ida_xref.get_first_fcref_from( ea )
is_ihelp = False
if target_ea != ida_idaapi.BADADDR :
name = ida_name.get_name( target_ea )
if name :
clean_name = name.lstrip( '_.' )
if clean_name.startswith( ( "i32_", "i64_" ) ) :
ihelp_calls[clean_name].append( ea )
is_ihelp = True
if not is_ihelp :
hook_addrs.add( ea )
return hook_addrs, dict( ihelp_calls )
def get_flow ( real_block_list, func_ea, file_path ) :
logging.getLogger( 'angr' ).setLevel( logging.ERROR )
logging.getLogger( 'angr.project' ).setLevel( logging.ERROR )
logging.getLogger( 'angr.sim_manager' ).setLevel( logging.ERROR )
logging.getLogger( 'angr.engines.successors' ).setLevel( logging.ERROR )
logging.getLogger( 'cle.loader' ).setLevel( logging.ERROR )
logging.getLogger( 'cle.backends.externs' ).setLevel( logging.ERROR )
logging.getLogger( 'cle.backends.elf.elf' ).setLevel( logging.ERROR )
proj = angr.Project(
file_path,
load_options = {
'auto_load_libs' : False,
'main_opts' : {
'base_addr' : 0x400000
}
}
)
base_addr = proj.loader.min_addr
print( f"base_addr={base_addr:#x}" )
init_state = proj.factory.blank_state(
addr = base_addr + func_ea,
add_options = {
angr.options.SYMBOL_FILL_UNCONSTRAINED_MEMORY,
angr.options.SYMBOL_FILL_UNCONSTRAINED_REGISTERS,
angr.options.BYPASS_UNSUPPORTED_SYSCALL,
},
remove_options = {
angr.options.LAZY_SOLVES,
}
)
FAKE_INSTANCE_ADDR = 0x60000000
WASM_G2_BASE = 0x70000000
WASM_MEM_BASE = 0x80000000
init_state.regs.rdi = FAKE_INSTANCE_ADDR
init_state.memory.store(
FAKE_INSTANCE_ADDR+0x18,
claripy.BVV( WASM_MEM_BASE, 64 ),
endness='Iend_LE'
)
init_state.memory.store(
FAKE_INSTANCE_ADDR+0x28,
claripy.BVV( WASM_G2_BASE, 32 ),
endness='Iend_LE'
)
init_state.regs.rsp = 0x7fffffff0000
init_state.regs.rbp = 0x7fffffff0000
flow = {key: [] for key in real_block_list}
ret_block_ea \
= real_block_list[len(real_block_list)-1]
prologue_block \
= get_block_from_ea( func_ea )
prologue_block_last_insn_ea \
= ida_bytes.prev_head( prologue_block.end_ea, prologue_block.start_ea )
print( f"prologue_block_last_insn : {prologue_block_last_insn_ea:#x}" )
assert prologue_block_last_insn_ea != ida_idaapi.BADADDR
prologue_state_dict \
= {}
def retn_procedure ( state ) :
return
hook_addrs, ihelp_calls \
= find_call( func_ea )
print( f"Normal calls to hook [{len(hook_addrs)}]" )
print( [hex(x) for x in sorted( hook_addrs )] )
print( f"ihelp_* functions [{len(ihelp_calls)}]" )
for func_name in sorted( ihelp_calls.keys() ) :
addrs = ihelp_calls[func_name]
count = len( addrs )
haddrs = [hex(a) for a in sorted(addrs)]
print( f"{func_name} [{count}]: {', '.join( haddrs ) }" )
for addr in hook_addrs :
proj.hook( base_addr+addr, retn_procedure, length=5 )
class i32_load ( angr.SimProcedure ) :
def run ( self, mem_ptr, addr ) :
return self.state.memory.load( WASM_MEM_BASE+addr, size=4, endness='Iend_LE' )
class i32_load8_s ( angr.SimProcedure ) :
def run ( self, mem_ptr, addr ) :
val = self.state.memory.load( WASM_MEM_BASE+addr, size=1 )
return val.sign_extend( 24 )
class i32_load8_u ( angr.SimProcedure ) :
def run ( self, mem_ptr, addr ) :
val = self.state.memory.load( WASM_MEM_BASE+addr, size=1 )
return val.zero_extend( 24 )
class i64_load ( angr.SimProcedure ) :
def run ( self, mem_ptr, addr ) :
return self.state.memory.load( WASM_MEM_BASE+addr, size=8, endness='Iend_LE' )
class i32_store ( angr.SimProcedure ) :
def run ( self, mem_ptr, addr, value ) :
self.state.memory.store( WASM_MEM_BASE+addr, value, size=4, endness='Iend_LE' )
return
class i32_store8 ( angr.SimProcedure ) :
def run ( self, mem_ptr, addr, value ) :
self.state.memory.store( WASM_MEM_BASE+addr, value, size=1 )
return
class i64_store ( angr.SimProcedure ) :
def run ( self, mem_ptr, addr, value ) :
self.state.memory.store( WASM_MEM_BASE+addr, value, size=8, endness='Iend_LE' )
return
proc_map = {
'i32_load': i32_load,
'i32_load8_s': i32_load8_s,
'i32_load8_u': i32_load8_u,
'i32_store': i32_store,
'i32_store8': i32_store8,
'i64_load': i64_load,
'i64_store': i64_store
}
for func_name, call_addrs in ihelp_calls.items() :
if func_name in proc_map :
target_ea = ida_name.get_name_ea( ida_idaapi.BADADDR, func_name )
if target_ea == ida_idaapi.BADADDR :
target_ea = ida_name.get_name_ea( ida_idaapi.BADADDR, "_"+func_name )
if target_ea != ida_idaapi.BADADDR :
proj.hook( base_addr+target_ea, proc_map[func_name]() )
else :
assert False
for real_block_ea in real_block_list :
if ret_block_ea == real_block_ea :
continue
init_state_2 = init_state.copy()
print( f"Finding {real_block_ea:#x} -> ..." )
debug_path = {}
debug_ea = None
debug_path[debug_ea] \
= []
log = False
if real_block_ea != func_ea :
if prologue_block_last_insn_ea not in prologue_state_dict :
prologue_state_dict[prologue_block_last_insn_ea] \
= get_state( proj, base_addr, init_state_2, prologue_block_last_insn_ea, debug_ea, debug_path, log )
init_state_2 = prologue_state_dict[prologue_block_last_insn_ea]
init_state_2.regs.pc \
= base_addr + real_block_ea
find_next_block( proj, base_addr, init_state_2, real_block_ea, real_block_list, flow, debug_ea, debug_path, log )
if debug_ea is not None :
print( hex(debug_ea), debug_path[debug_ea] )
flow_dict = {
hex(key): [hex(value) for value in values]
for key, values in flow.items()
}
print( flow_dict )
print( f"flow_dict[{len(flow_dict)}]:" )
for i, ( key, value ) in enumerate( flow_dict.items() ) :
print( f"[{i}] {key} -> {value}" )
return flow_dict
def patch_flow ( ks, flow_dict ) :
for i, ( real_block_ea, succs ) in enumerate( flow_dict.items() ) :
if len( succs ) == 0 :
continue
block = get_block_from_ea( int( real_block_ea, 0 ) )
last_insn = ida_bytes.prev_head( block.end_ea, block.start_ea )
if len( succs ) == 2 :
if ida_ua.ua_mnem( last_insn ) != "jz" :
assert False
print( f"[{i}] [0] patch_flow {real_block_ea} : {last_insn:#x} {block.end_ea:#x}" )
buf, count \
= ks.asm( f'jz {succs[0]}', last_insn )
ida_bytes.patch_bytes( last_insn, bytes( buf ) )
set_insn_color( last_insn )
print( f"Patch Block {last_insn:#x} -> {succs[0]}" )
jz_size = len( bytes( buf ) )
# assert jz_size <= 6
ida_bytes.del_items( last_insn, ida_bytes.DELIT_SIMPLE, jz_size )
ida_ua.create_insn( last_insn )
next_insn \
= last_insn + jz_size
buf2, count2 \
= ks.asm( f'jmp {succs[1]}', next_insn )
ida_bytes.patch_bytes( next_insn, bytes( buf2 ) )
set_insn_color( next_insn )
print( f"Patch Block {next_insn:#x} -> {succs[1]}" )
jmp_size \
= len( bytes( buf2 ) )
ida_bytes.del_items( next_insn, ida_bytes.DELIT_SIMPLE, jmp_size )
ida_ua.create_insn( next_insn )
else :
print( f"[{i}] [2] patch_flow {real_block_ea} : {last_insn:#x}" )
buf, count = ks.asm( f'jmp {succs[0]}', last_insn )
ida_bytes.patch_bytes( last_insn, bytes( buf ) )
set_insn_color( last_insn )
print( f"Patch Block {last_insn:#x} -> {succs[0]}" )
jmp_size = len( bytes( buf ) )
ida_bytes.del_items( last_insn, ida_bytes.DELIT_SIMPLE, jmp_size )
ida_ua.create_insn( last_insn )
def main ( func_ea ) :
file_path = ida_nalt.get_input_file_path()
real_block_list \
= get_real_block( func_ea )
flow_dict = get_flow( real_block_list, func_ea, file_path )
ks = keystone.Ks( keystone.KS_ARCH_X86, keystone.KS_MODE_LITTLE_ENDIAN | keystone.KS_MODE_64 )
patch_flow( ks, flow_dict )
if "__main__" == __name__ :
main( 0xbdcb7 )
1) 整体框架
反CFF的一般流程是
a. 找出有效块(真实块)
b. 找出状态变量state_var
c. 建立有效块到有效块的映射关系
d. 修改控制流
2) 找出有效块(真实块)
PoC只针对func54_vodplay，偷懒了，未实现通用算法，只是一种针对性经验算法。处理其他函数的CFF时，需修改get_real_block。
is_block_0在块尾部找如下模板
call i32_store
jmp
假设有效块干完活都会设置新的状态常量。对块大小作了些过滤，滤掉某些虚假有效块。
is_block_1在块尾部找如下模板
call i32_load / i64_load
...
cmp
jz
这种有效块对应cmov、csel情形。h5_worker_pub.o中没有这种指令，已将它们拆成is_block_1所处理的模板形式。将来Angr模拟执行寻找块映射关系时，对此情形要特殊处理。
反CFF时如何定义有效块，有不同看法。我将绝大多数不对应while、if的块算作有效块，其中一些可能只设置了状态变量就跳转，并未真正干活，但仍视之为有效块。这样做的好处，一是降低因漏报有效块而丢失有效代码的概率，二是不必对块进行太多指令分析。假设非要找出真正干活的块，势必对块进行大量分析，容错性不足。
3) 找出状态变量state_var
本例的.py实现不涉及这点，但找出状态变量，有助于将来清理反CFF结果。F5查看反编译得到的伪代码，有大量代码形如
i32_store(instance->w2c_env_memory, state_var_addr_v77, 0xE9102B37)
这是在设置状态变量。反CFF后这种代码没有意义，可用grep批量移除，不费事。
4) 建立有效块到有效块的映射关系
get_flow进行Angr模拟执行，寻找每个有效块的下一跳(有效块)。
func54_vodplay中有大量call指令，用findcall找出它们，但分了两种情况。一种目标函数是i32、i64_，比如
i32_load
i32_load8_s
i32_load8_u
i32_store
i32_store8
i64_load
i64_store
这种我称之为ihelp函数。另一种目标函数是非ihelp函数。
ihelp函数在访问wasm平坦内存memory。本例的状态变量不是寄存器，而是memory中的内存变量。
非ihelp函数涉及真正的功能，比如"TS PES NALU解密"。
找出这些call后，对ihelp函数进行angr.SimProcedure重载，自己模拟对memory的读写。这步或许有其他选择，比如初始化instance->w2c_env_memory，让Angr引擎直接模拟。我不想分析instance结构定义，故未采用后一方案。
对非ihelp函数则全部hook到空函数retn_procedure，减少"路径爆炸"的可能。
因为我要精细化hook，所以创建init_state时，未指定angr.options.CALLLESS。否则ihelp函数也被NOP化，将导致模拟执行时无法跟踪CFF状态变量，无法获取有效块到有效块的映射关系。
get_flow严重依赖get_real_block，若get_real_block未获取全部有效块，或混入虚假有效块，都将导致get_flow结果偏离预期，进而使得patch_flow结果偏离预期。若反CFF结果不佳，优先检查get_real_block。
5) 汇编级修改控制流
h5_worker_cff_pub.py只演示了汇编级Patch，将有效块们串起来。汇编级Patch可能遭遇"没有足够安全空间用于Patch"，事实上py就碰上了。比如0xbe129处的mov被改成jmp，而这个mov在为某地址变量赋值，后续会用到；mov变jmp后，相当于那个地址变量丢失。尽管如此，整体反CFF结果还可以，有助于理解函数逻辑。
对于x64架构，在IDA数据库中只修改指令字节流，不够，需要脚本完成c操作，否则F5结果很可能不符预期。
IDA微码级Patch天然解决"没有足够安全空间用于Patch"的问题，当然，微码级Patch存在其他劣势。没有完美的Patch方案。
6) func54_vodplay反CFF结果
/*
* grep -v "instance->w2c_env_memory, state_var_addr_v77"
*/
u32 __cdecl func54_vodplay(w2c_h50x2Eworker *instance, u32 var_p0, u32 var_p1, u32 var_p2)
{
/*
* 本例中这是wasm的栈指针SP
*/
w2c_g2 = instance->w2c_g2;
/*
* 在栈上分配96字节的局部变量空间
*/
instance->w2c_g2 = w2c_g2 + 96;
/*
* 在栈上具体分配若干局部变量
*/
v69 = w2c_g2 + 24;
v74 = w2c_g2 + 68;
v70 = w2c_g2 + 64;
v65 = w2c_g2 + 16;
v75 = w2c_g2 + 60;
v76 = w2c_g2 + 56;
v77 = w2c_g2 + 82;
v67 = w2c_g2 + 52;
v78 = w2c_g2 + 81;
v68 = w2c_g2 + 8;
v79 = w2c_g2 + 80;
v80 = w2c_g2 + 79;
v66 = w2c_g2 + 78;
v81 = w2c_g2 + 77;
v82 = w2c_g2 + 76;
v83 = w2c_g2 + 75;
v84 = w2c_g2 + 74;
v64 = w2c_g2 + 48;
i32_store8(instance->w2c_env_memory, w2c_g2 + 72, 1u);
i32_store8(instance->w2c_env_memory, w2c_g2 + 73, 1u);
/*
* 状态变量
*/
state_var_addr_v77 = w2c_g2 + 44;
/*
* 初始化状态变量
*/
i32_store(instance->w2c_env_memory, w2c_g2 + 44, 0x5CA0809Fu);
v71 = w2c_g2 + 40;
v93 = i32_load8_s(instance->w2c_env_memory, w2c_g2 + 72) & 1;
v47 = i32_load8_s(instance->w2c_env_memory, w2c_g2 + 73);
if ( !(v47 & 1 | v93) )
;
v61 = instance->w2c_g2;
instance->w2c_g2 = v61 + 32;
/*
* v64保存某个SP副本
*/
i32_store(instance->w2c_env_memory, v64, v61);
i32_store8(instance->w2c_env_memory, v84, var_p2 - 1 < 0x3FF);
if ( (i32_load8_s(instance->w2c_env_memory, v84) & 1) != 0 )
{
v57 = func121_malloc(instance, var_p2 + 1);
i32_store8(instance->w2c_env_memory, v57 + var_p2, 0);
func123_memcpy(instance, v57, var_p1 + var_p0, var_p2);
v5 = func30(instance, v57);
i32_store8(instance->w2c_env_memory, 0x6920uLL, v5 != 0);
func120_free(instance, v57);
}
if ( !(((int)var_p1 < 32) | (i32_load8_s(instance->w2c_env_memory, 0x6920uLL) == 0)) )
{
/*
* 读取媒体流的开头(NALU头部)，判断类型是否为25，然后将宽高或编码
* 参数提取并存入全局变量0x66E4和0x66E8。
*/
v4 = i32_load8_s(instance->w2c_env_memory, var_p0);
i32_store8(instance->w2c_env_memory, v83, (v4 & 0x1F) == 25);
v49 = i32_load8_s(instance->w2c_env_memory, v83);
if ( (v49 & 1) != 0 )
{
v43 = i32_load8_u(instance->w2c_env_memory, var_p0 + 2LL);
i32_store(instance->w2c_env_memory, 0x66E4uLL, v43);
v44 = i32_load8_u(instance->w2c_env_memory, var_p0 + 3LL);
i32_store(instance->w2c_env_memory, 0x66E8uLL, v44);
/*
* 反CFF带来的异常代码，根本原因在于0xbe129处的mov因Patch被改
* 成jmp，那个mov在为下述地址变量赋值。
*/
i32_store(instance->w2c_env_memory, 0LL, var_p1);
}
else
{
if ( !i64_load(instance->w2c_env_memory, 0x66D0uLL) )
{
v36 = func77(instance, 0x61C0u);
i32_store8(instance->w2c_env_memory, v82, v36 == 0);
v33 = i32_load8_s(instance->w2c_env_memory, v82);
if ( (v33 & 1) != 0 )
{
v34 = i64_load(instance->w2c_env_memory, 0x2EA0uLL);
i64_store(instance->w2c_env_memory, 0x61C0uLL, v34);
v98 = i64_load(instance->w2c_env_memory, 0x2EA8uLL);
i64_store(instance->w2c_env_memory, 0x61C8uLL, v98);
v35 = i32_load(instance->w2c_env_memory, 0x2EB0uLL);
i32_store(instance->w2c_env_memory, 0x61D0uLL, v35);
}
/*
* 从栈上读取某个SP副本
*/
v58 = i32_load(instance->w2c_env_memory, v64);
v72 = i32_load(instance->w2c_env_memory, v64) + 18;
/*
* 逐字节写入混淆字符串，ASCIZ串
*/
v10 = i32_load(instance->w2c_env_memory, v64);
i32_store8(instance->w2c_env_memory, v10 + 17LL, 0);
i32_store8(instance->w2c_env_memory, v72, 0x11u);
v11 = i32_load(instance->w2c_env_memory, v64);
i32_store8(instance->w2c_env_memory, v11 + 16LL, 0x11u);
v12 = i32_load(instance->w2c_env_memory, v64);
i32_store8(instance->w2c_env_memory, v12 + 15LL, 0x1Bu);
v13 = i32_load(instance->w2c_env_memory, v64);
i32_store8(instance->w2c_env_memory, v13 + 14LL, 0x1Cu);
v14 = i32_load(instance->w2c_env_memory, v64);
i32_store8(instance->w2c_env_memory, v14 + 13LL, 0x75u);
v15 = i32_load(instance->w2c_env_memory, v64);
i32_store8(instance->w2c_env_memory, v15 + 12LL, 0x34u);
v16 = i32_load(instance->w2c_env_memory, v64);
i32_store8(instance->w2c_env_memory, v16 + 11LL, 0x2Bu);
v17 = i32_load(instance->w2c_env_memory, v64);
i32_store8(instance->w2c_env_memory, v17 + 10LL, 0x75u);
v18 = i32_load(instance->w2c_env_memory, v64);
i32_store8(instance->w2c_env_memory, v18 + 9LL, 0x34u);
v19 = i32_load(instance->w2c_env_memory, v64);
i32_store8(instance->w2c_env_memory, v19 + 8LL, 0x2Bu);
v20 = i32_load(instance->w2c_env_memory, v64);
i32_store8(instance->w2c_env_memory, v20 + 7LL, 0x62u);
v21 = i32_load(instance->w2c_env_memory, v64);
i32_store8(instance->w2c_env_memory, v21 + 6LL, 0x34u);
v22 = i32_load(instance->w2c_env_memory, v64);
i32_store8(instance->w2c_env_memory, v22 + 5LL, 0x2Cu);
v23 = i32_load(instance->w2c_env_memory, v64);
i32_store8(instance->w2c_env_memory, v23 + 4LL, 0x7Fu);
v24 = i32_load(instance->w2c_env_memory, v64);
i32_store8(instance->w2c_env_memory, v24 + 3LL, 0x78u);
v25 = i32_load(instance->w2c_env_memory, v64);
i32_store8(instance->w2c_env_memory, v25 + 2LL, 0x63u);
v26 = i32_load(instance->w2c_env_memory, v64);
i32_store8(instance->w2c_env_memory, v26 + 1LL, 0x74u);
i32_store8(instance->w2c_env_memory, v58, 0x67u);
v27 = i32_load(instance->w2c_env_memory, v64);
v59 = func40(instance, v27);
v73 = i32_load(instance->w2c_env_memory, 0x66E4uLL);
v85 = i32_load(instance->w2c_env_memory, 0x66E8uLL);
i32_store(instance->w2c_env_memory, v69, 0x61C0u);
i32_store(instance->w2c_env_memory, v69 + 4LL, v73);
i32_store(instance->w2c_env_memory, v69 + 8LL, v85);
func114(instance, v59, v69);
v94 = i64_load(instance->w2c_env_memory, 0x66D0uLL);
i64_store(instance->w2c_env_memory, w2c_g2, v94);
}
v7 = i64_load(instance->w2c_env_memory, w2c_g2);
i64_store(instance->w2c_env_memory, v65, v7);
v96 = i64_load(instance->w2c_env_memory, v65) + 1;
i64_store(instance->w2c_env_memory, 0x66D0uLL, v96);
func52(instance);
i32_store8(instance->w2c_env_memory, v81, (int)var_p1 < 129);
v32 = i32_load8_s(instance->w2c_env_memory, v81);
if ( (v32 & 1) == 0 )
{
v60 = i32_load8_s(instance->w2c_env_memory, 0x5C6DuLL) != 0;
i32_store8(instance->w2c_env_memory, v66, (var_p1 & 1) == 0);
v40 = i32_load8_s(instance->w2c_env_memory, v66);
i32_store8(instance->w2c_env_memory, v80, v40 & 1 & v60);
v50 = i32_load8_s(instance->w2c_env_memory, v80);
if ( (v50 & 1) == 0 )
{
value = i32_load8_s(instance->w2c_env_memory, v66) & 1;
v31 = i32_load8_s(instance->w2c_env_memory, 0x5C6CuLL);
i32_store8(instance->w2c_env_memory, v79, (v31 != 0) & value);
v97 = i64_load(instance->w2c_env_memory, 0x66D0uLL);
i64_store(instance->w2c_env_memory, v68, v97);
if ( (i32_load8_s(instance->w2c_env_memory, v79) & 1) == 0 )
goto LABEL_12;
v95 = i64_load(instance->w2c_env_memory, v68);
v6 = i32_load(instance->w2c_env_memory, 0x66DCuLL);
i32_store8(instance->w2c_env_memory, v78, v95 < (int)(5120 * v6));
v45 = i32_load8_s(instance->w2c_env_memory, v78);
if ( (v45 & 1) != 0 )
{
LABEL_12:
v28 = func121_malloc(instance, var_p1 + 4000);
i32_store(instance->w2c_env_memory, v67, v28);
v29 = i32_load(instance->w2c_env_memory, v67);
func125_memset(instance, v29, 0, var_p1 + 4000);
valuea = i32_load(instance->w2c_env_memory, 0x66E4uLL) == 1;
v30 = i32_load(instance->w2c_env_memory, 0x66E8uLL);
i32_store8(instance->w2c_env_memory, v77, v30 == 6 && valuea);
v51 = i32_load8_s(instance->w2c_env_memory, v77);
if ( (v51 & 1) != 0 )
{
v92 = i32_load(instance->w2c_env_memory, v67);
v38 = i64_load(instance->w2c_env_memory, v68);
v39 = func60_TEA(instance, var_p1, var_p0, v92, v38);
i32_store(instance->w2c_env_memory, v76, v39);
valued = i32_load(instance->w2c_env_memory, v76);
}
else
{
v91 = i32_load(instance->w2c_env_memory, v67);
v8 = i64_load(instance->w2c_env_memory, v68);
v9 = func59(instance, var_p1, var_p0, v91, v8);
i32_store(instance->w2c_env_memory, v75, v9);
valued = i32_load(instance->w2c_env_memory, v75);
}
i32_store(instance->w2c_env_memory, v71, valued);
v48 = i32_load(instance->w2c_env_memory, v71);
i32_store(instance->w2c_env_memory, v70, v48);
if ( i32_load(instance->w2c_env_memory, v70) )
{
func58_TEA(instance, var_p0, var_p1, var_p1);
valuee = i32_load(instance->w2c_env_memory, v67);
v41 = i32_load(instance->w2c_env_memory, v70);
func123_memcpy(instance, var_p0, valuee, v41);
v42 = i32_load(instance->w2c_env_memory, v67);
func120_free(instance, v42);
valuef = i32_load(instance->w2c_env_memory, v70);
/*
* 0xbe129处mov变jmp带来的异常
*/
i32_store(instance->w2c_env_memory, 0LL, valuef);
}
else
{
v37 = i32_load(instance->w2c_env_memory, v67);
func120_free(instance, v37);
/*
* 0xbe129处mov变jmp带来的异常
*/
i32_store(instance->w2c_env_memory, 0LL, var_p1);
}
}
}
}
}
}
/*
* 0xbe129处mov变jmp带来的异常
*/
v46 = i32_load(instance->w2c_env_memory, 0LL);
i32_store(instance->w2c_env_memory, v74, v46);
result = i32_load(instance->w2c_env_memory, v74);
instance->w2c_g2 = w2c_g2;
return result;
}
上述代码已脱去CFF，熟悉wasm的已能看出整体逻辑，可结合Chrome F12动态调试进一步厘清。所有的i_store在设置栈上的局部变量，i_load在读取栈上的局部变量，可用AI对之简化。
$ wasm-objdump -j Global -x h5.worker.wasm
Global[2]:
- global[2] i32 mutable=1 - init i32=28176
- global[3] i32 mutable=1 - init i32=5271056
$ wasm2wat -o h5_worker.wat h5.worker.wasm
(import "env" "__table_base" (global (;0;) i32))
(import "env" "DYNAMICTOP_PTR" (global (;1;) i32))
(import "env" "memory" (memory (;0;) 256))
(import "env" "table" (table (;0;) 160 funcref))
从wat中汇编代码可知，global[2]相当于栈指针SP，但此栈向高址方向增长，不同于x86的栈向低址方向增长。每个函数的局部变量在栈上分配。
本文不对func54_vodplay的功能进一步逆向分析，只演示反CFF部分。事实上这是两年前的wasm，现已发生变化。
☆ AI反CFF
现在AI这么发达，许多反混淆或可用AI完成。
让某AI对func54_vodplay静态反混淆，它说该函数被实施过CFF+BCF，并提供反混淆结果。
u32 func54_vodplay(w2c_h50x2Eworker* instance, u32 var_p0, u32 var_p1, u32 var_p2) {
// 1. 内部调用计数器更新
// 每次进入函数，会将内存 26320 处的 64 位计数器 +1
u64 counter = i64_load(instance->w2c_env_memory, 26320);
i64_store(instance->w2c_env_memory, 26320, counter + 1);
func52(instance);
// ==========================================
// 2. 早期退出检查 (参数校验与流控防刷)
// ==========================================
if (var_p1 < 129) {
return var_p1;
}
bool is_p1_even = !(var_p1 & 1);
// 校验内存开关 23661
u8 flag_23661 = i32_load8_s(instance->w2c_env_memory, 23661);
if (is_p1_even && (flag_23661 != 0)) {
return var_p1;
}
// 校验内存开关 23660 与调用频率限制
u8 flag_23660 = i32_load8_s(instance->w2c_env_memory, 23660);
if (is_p1_even && (flag_23660 != 0)) {
u32 limit_val = i32_load(instance->w2c_env_memory, 26332);
// 如果调用次数超出阈值，直接退出
if (counter >= (s64)(limit_val * 5120)) {
return var_p1;
}
}
// ==========================================
// 3. 上下文完整性校验与状态恢复
// ==========================================
// 检查核心配置结构 (偏移 25024 处的数据)
if (func77(instance, 25024)) {
// [校验失败分支]：构造一段混淆后的异常日志/报错字符串并上报
// 原始代码在此处逐字节写入了混淆字符串 "tcx\x7f,4b+4u+4u\x1c\x1b\x11"
u32 str_buf = instance->w2c_g2 + 48; // 栈上申请字符串对象
func40(instance, str_buf);
func114(instance, str_buf, 25024);
// (注：异常抛出后原代码进入重置循环)
} else {
// [校验成功分支]：从 11936 处的备份区，恢复上下文环境到 25024 处
i64_store(instance->w2c_env_memory, 25024, i64_load(instance->w2c_env_memory, 11936));
i64_store(instance->w2c_env_memory, 25032, i64_load(instance->w2c_env_memory, 11944));
i32_store(instance->w2c_env_memory, 25040, i32_load(instance->w2c_env_memory, 11952));
}
// ==========================================
// 4. 内存分配与核心数据处理
// ==========================================
// 分配解码/解密缓冲区 (大小 = p1 + 4000) 并清零
u32 alloc_size = var_p1 + 4000;
u32 buf_ptr = func121_malloc(instance, alloc_size);
func125_memset(instance, buf_ptr, 0, alloc_size);
// 调用核心处理逻辑 (在不同路径中可能是 f60 或 f59，但参数完全一致)
u32 process_ret = func60_TEA(instance, var_p1, var_p0, buf_ptr, counter);
// ==========================================
// 5. 结果回写与清理
// ==========================================
if (process_ret != 0) {
// 处理成功：回写数据到输出指针 (var_p0)
func58_TEA(instance, var_p0, var_p1, var_p1);
func123_memcpy(instance, var_p0, buf_ptr, process_ret);
func120_free(instance, buf_ptr);
return process_ret; // 返回实际处理的长度
} else {
// 处理失败
func120_free(instance, buf_ptr);
return var_p1;
}
}
AI附了一段说明。
防刷/限流：开头通过记录counter(调用次数)，并结合常量(如5120)计算限流，拦截高频的非法扫描或异常调用。
防篡改与完整性校验：通过f77校验内存区块25024的特征。若不匹配，会生成异常信息"tcx\x7f,4b+4u+4u\x1c\x1b\x11"(这是加密字符串，解密后很可能类似exception...或某个日志标识)。若正常则从安全的备份区(11936)还原状态。
核心解密：使用f121申请了比输入长度大4000字节的buf_ptr。调用的f60/f59就是真正执行解密运算的函数。
结果回写：解密成功后，用f58和memcpy将解密后的明文数据写回var_p0对应的内存中，最后返回解密数据的实际长度。
未细究AI的反混淆结果，只是尝试一种可能。
☆ 后记
本文简单探索针对wasm反CFF的技术方案，提供测试用例及PoC。以前觉得wasm2c没啥大用，现在看来，用于反CFF倒是不错。
另一方面，如今AI大行其道，这些古法逆向工程技能已日暮西山、明日黄花，被淘汰的大势已来，以后会变成特别小众的手搓技能，失去实战价值。
发表于 2026-5-11 17:11
