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cyy_mac
310
tools/analyze_blowfish_dump.py
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310
tools/analyze_blowfish_dump.py
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#!/usr/bin/env python3
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"""
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分析从狗上提取的Blowfish密钥dump
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1. 在内存中搜索Blowfish P-array (key schedule后的运行时值)
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2. 提取P-array和S-box,反推原始密钥
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3. 用提取的P-array+S-box解密已知密文,验证正确性
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"""
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import os
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import sys
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import struct
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import tarfile
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import argparse
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from pathlib import Path
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# Blowfish标准初始常数 (从π推导)
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P_INITIAL = [
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0x243f6a88, 0x85a308d3, 0x13198a2e, 0x03707344,
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0xa4093822, 0x299f31d0, 0x082efa98, 0xec4e6c89,
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0x452821e6, 0x38d01377, 0xbe5466cf, 0x34e90c6c,
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0xc0ac29b7, 0xc97c50dd, 0x3f84d5b5, 0xb5470917,
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0x9216d5d9, 0x8979fb1b
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]
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S0_INITIAL_FIRST = 0xd1310ba6
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S1_INITIAL_FIRST = 0x4b7a70e9
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S2_INITIAL_FIRST = 0xe93d5a68
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S3_INITIAL_FIRST = 0x3a39ce37
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# 已知明文-密文对 (用于验证)
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KNOWN_PAIRS = [
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(bytes.fromhex('0000000000000000'), bytes.fromhex('12aa0354236b66e3')),
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(bytes.fromhex('feefff0004020305'), bytes.fromhex('5d9874ad7eee5851')),
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(bytes.fromhex('0802010000010900'), bytes.fromhex('c5ce0887ceb86f91')),
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]
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def find_pattern_locations(data, pattern_bytes):
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"""在data中查找pattern的所有位置"""
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positions = []
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pos = 0
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while True:
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p = data.find(pattern_bytes, pos)
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if p < 0:
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break
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positions.append(p)
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pos = p + 1
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return positions
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def looks_like_blowfish_buffer(data, offset):
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"""
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检查offset处是否看起来像Blowfish state:
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布局假设: P[18] + S0[256] + S1[256] + S2[256] + S3[256]
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总大小: (18 + 4*256) * 4 = 4168 字节
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"""
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BUF_SIZE = 4168
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if offset + BUF_SIZE > len(data):
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return False
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# 取出整个buffer
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buf = data[offset:offset+BUF_SIZE]
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# 解释为uint32列表
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words = struct.unpack(f'<{BUF_SIZE//4}I', buf)
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# 检查S-box是否包含至少部分初始值
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# 运行时,P和S-box已被key schedule修改,但所有1042个值的统计特性应该接近随机
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# 这里只能做粗略的"看起来像加密表"的检查
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# 唯一性检查: 1042个32位值应该几乎都不同
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unique = len(set(words))
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if unique < 1000: # 太多重复说明不是表
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return False
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# 零值检查: 不应该有很多0
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zeros = sum(1 for w in words if w == 0)
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if zeros > 50:
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return False
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# 熵检查: 字节分布应该均匀
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byte_counts = [0] * 256
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for b in buf:
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byte_counts[b] += 1
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max_count = max(byte_counts)
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if max_count > len(buf) // 50: # 任何字节出现频率超过2%可疑
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return False
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return True
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def encrypt_with_state(plaintext_8, P, S0, S1, S2, S3, endian='<'):
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"""
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用给定的P-array + S-box加密8字节明文
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endian: '<' (LE - Unitree style) 或 '>' (BE - standard)
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"""
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pack_fmt = endian + 'II'
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L, R = struct.unpack(pack_fmt, plaintext_8)
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for i in range(16):
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L ^= P[i]
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# F(L)
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a = (L >> 24) & 0xff
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b = (L >> 16) & 0xff
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c = (L >> 8) & 0xff
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d = L & 0xff
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F = (((S0[a] + S1[b]) & 0xFFFFFFFF) ^ S2[c]) & 0xFFFFFFFF
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F = (F + S3[d]) & 0xFFFFFFFF
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R ^= F
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L, R = R, L
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L, R = R, L
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R ^= P[16]
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L ^= P[17]
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return struct.pack(pack_fmt, L, R)
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def try_buffer_as_blowfish(buf_data, label="?"):
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"""
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把一个4168字节的buffer解释为Blowfish state
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用已知明密文对验证
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"""
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if len(buf_data) < 4168:
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return None
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# 假设布局1: P[0..17] + S0[0..255] + S1[0..255] + S2[0..255] + S3[0..255]
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words = struct.unpack('<1042I', buf_data[:4168])
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P = list(words[0:18])
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S0 = list(words[18:18+256])
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S1 = list(words[18+256:18+512])
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S2 = list(words[18+512:18+768])
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S3 = list(words[18+768:18+1024])
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# 测试已知对 - 同时尝试LE和BE两种字节序
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best_matches = 0
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best_endian = None
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for endian in ['<', '>']:
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matches = 0
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for pt, ct_expected in KNOWN_PAIRS:
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try:
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ct = encrypt_with_state(pt, P, S0, S1, S2, S3, endian=endian)
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if ct == ct_expected:
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matches += 1
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except Exception:
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pass
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if matches > best_matches:
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best_matches = matches
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best_endian = endian
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return best_matches, best_endian
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def search_blowfish_state(data, source_name, verbose=False, save_candidates=False):
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"""在内存dump中搜索Blowfish状态"""
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print(f"\n=== 在 {source_name} 中搜索 Blowfish state ===")
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print(f" 大小: {len(data)} 字节")
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candidates_looking_like_bf = []
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# 策略1: 找S-box[0]首字 (运行时S-box会被修改,但偶尔可能未变)
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# 实际上key schedule后S-box几乎肯定变化,所以这条不会找到
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s0_pos = find_pattern_locations(data, struct.pack('<I', S0_INITIAL_FIRST))
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s1_pos = find_pattern_locations(data, struct.pack('<I', S1_INITIAL_FIRST))
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print(f" S0初始首字位置数: {len(s0_pos)}")
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print(f" S1初始首字位置数: {len(s1_pos)}")
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if s0_pos:
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# 这些是静态初始值,在二进制中也有 (说明是从binary mmap过来的)
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for p in s0_pos[:3]:
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print(f" 初始S0@0x{p:x}")
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# 策略2: 暴力搜索 - 假设 4168字节连续Blowfish state
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# 每4字节对齐位置都试
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print(f" 暴力搜索 Blowfish state (4字节对齐)...")
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found = []
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STEP = 4
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count = 0
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total = (len(data) - 4168) // STEP
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for offset in range(0, len(data) - 4168, STEP):
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count += 1
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if count % 50000 == 0:
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print(f" 进度: {count}/{total} ({100*count/total:.1f}%)")
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# 快速过滤: 必须looks_like
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if not looks_like_blowfish_buffer(data, offset):
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continue
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candidates_looking_like_bf.append(offset)
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if save_candidates and len(candidates_looking_like_bf) <= 20:
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cand_path = f'data/lowlevel_captures/candidate_{source_name}_{offset:08x}.bin'
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os.makedirs('data/lowlevel_captures', exist_ok=True)
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with open(cand_path, 'wb') as fp:
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fp.write(data[offset:offset+4168])
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print(f" 💾 保存候选 -> {cand_path}")
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result = try_buffer_as_blowfish(data[offset:offset+4168])
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if result is None:
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continue
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matches, endian = result
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if matches and matches > 0:
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endian_name = 'LE' if endian == '<' else 'BE'
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print(f"\n ⭐ offset 0x{offset:x}: {matches}/3 已知对匹配! ({endian_name})")
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found.append((offset, matches, endian))
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if matches == 3:
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return found, data[offset:offset+4168]
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if candidates_looking_like_bf:
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print(f" 共找到 {len(candidates_looking_like_bf)} 个通过启发式过滤的候选位置")
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return found, None
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def extract_key_from_state(state_buf):
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"""从运行时P-array+S-box反推原始密钥 (这是Blowfish key schedule的逆过程)
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技术: Blowfish key schedule是
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1. P[i] = P_INITIAL[i] ^ key_word(i % keylen)
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2. 用此P和初始S-box加密 (0,0), 用结果替换P[0..1]
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3. 继续加密前一次结果,替换P[2..3], 直到P[16..17]
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4. 同样替换S-box
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要反推key: 由于步骤2-4都是确定性的,只要知道key就能复现
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而key只在步骤1出现, P[i] XOR P_INITIAL[i] = key_word(i % keylen)
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"""
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words = struct.unpack('<1042I', state_buf[:4168])
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P_runtime = list(words[0:18])
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# 但是! P-array在步骤2-4也被修改了, 所以不能直接 P XOR P_INITIAL
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# 实际上, 步骤1后的P[i]立即在步骤2中被覆盖, 我们看到的是覆盖后的值
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# 这意味着无法从最终state直接反推key
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# 但我们已经有了完整的state, 不需要key就能加密/解密!
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print("\n=== 提取的密钥state ===")
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print(f"P-array (18 uint32):")
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for i in range(18):
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print(f" P[{i:2d}] = 0x{P_runtime[i]:08x}")
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return P_runtime
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def main():
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parser = argparse.ArgumentParser()
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parser.add_argument('input', help='blowfish_dump.tar.gz 或 包含.bin文件的目录')
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parser.add_argument('--verbose', '-v', action='store_true')
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parser.add_argument('--save-candidates', action='store_true',
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help='保存所有"看起来像Blowfish state"的候选 (即使不能验证已知对)')
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args = parser.parse_args()
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input_path = Path(args.input)
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# 提取/找到所有bin文件
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bin_files = []
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if input_path.is_file() and input_path.suffix == '.gz':
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# tar.gz - 解压到临时目录
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import tempfile
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tmpdir = tempfile.mkdtemp(prefix='blowfish_analyze_')
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print(f"解压到 {tmpdir}...")
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with tarfile.open(input_path) as tf:
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tf.extractall(tmpdir)
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for f in Path(tmpdir).rglob('*.bin'):
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bin_files.append(f)
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elif input_path.is_dir():
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for f in input_path.rglob('*.bin'):
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bin_files.append(f)
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else:
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print(f"❌ 不支持的输入: {input_path}")
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return 1
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print(f"找到 {len(bin_files)} 个 .bin 文件")
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for f in bin_files:
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size = f.stat().st_size
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print(f" {f.name} ({size} 字节)")
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# 按大小排序,先搜索小的 (更快)
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bin_files.sort(key=lambda f: f.stat().st_size)
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found_state = None
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for f in bin_files:
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with open(f, 'rb') as fp:
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data = fp.read()
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results, state = search_blowfish_state(data, f.name, verbose=args.verbose, save_candidates=args.save_candidates)
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if state is not None:
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found_state = state
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print(f"\n🎉 在 {f.name} 中找到完整匹配!")
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break
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elif results:
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print(f"\n⚠️ 在 {f.name} 中找到 {len(results)} 个部分匹配位置")
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if found_state:
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P = extract_key_from_state(found_state)
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# 保存
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outpath = 'data/lowlevel_captures/blowfish_state.bin'
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os.makedirs('data/lowlevel_captures', exist_ok=True)
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with open(outpath, 'wb') as fp:
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fp.write(found_state)
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print(f"\n✅ Blowfish state已保存到 {outpath}")
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print(f" 可以用此state直接加密/解密通信数据")
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return 0
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else:
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print("\n❌ 未找到完整匹配的Blowfish state")
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print(" 可能原因:")
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print(" 1. 状态不在已dump的内存区域")
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print(" 2. 状态被分散存储 (不连续)")
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print(" 3. dump时机不对 (state还未初始化)")
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return 1
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if __name__ == '__main__':
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sys.exit(main())
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