Theta-kt-attack.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using Known text attack to identity XOR obsfuscation keys\n",
    "\n",
    "In this notebook, we use two approach to run known text attack (KT attacks) against the firmware files.\n",
    " 1. Run an heuristic that assumes the most prevalent byte value is 0x00\n",
    " 2. Run a real KT attack on sections of the files that alternate between 0xff and 0x00 values\n",
    "\n",
    "But first we need to look at the structure of the firmware files.\n",
    "\n",
    "## Firmware files structure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5,0M firmwares/ap1_v110.frm  6,2M firmwares/gy1_v121.frm\r\n",
      "5,0M firmwares/ap1_v130.frm  6,2M firmwares/gy1_v130.frm\r\n",
      "5,0M firmwares/ap1_v131.frm\r\n"
     ]
    }
   ],
   "source": [
    "%run ThetaS.ipynb\n",
    "!ls -sh firmwares/*.frm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have 5 files, 3 for theta m15 and 2 for the theta S\n",
    "\n",
    "In each file we can see a part delimitor 'UNITY FILE V1.10'.\n",
    "\n",
    "The part header seem to be 0x800 bytes long. Then XOR obfuscation starts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2,7M firmwares/parts/ap1_v110.frm.part1\r\n",
      "2,3M firmwares/parts/ap1_v110.frm.part2\r\n",
      "2,7M firmwares/parts/ap1_v130.frm.part1\r\n",
      "2,4M firmwares/parts/ap1_v130.frm.part2\r\n",
      "2,7M firmwares/parts/ap1_v131.frm.part1\r\n",
      "2,4M firmwares/parts/ap1_v131.frm.part2\r\n",
      "3,6M firmwares/parts/gy1_v121.frm.part1\r\n",
      "2,6M firmwares/parts/gy1_v121.frm.part2\r\n",
      "3,6M firmwares/parts/gy1_v130.frm.part1\r\n",
      "2,6M firmwares/parts/gy1_v130.frm.part2\r\n"
     ]
    }
   ],
   "source": [
    "# break the firmware in their two parts\n",
    "import os, mmap\n",
    "firmwares = [\"ap1_v110.frm\", \"ap1_v130.frm\", \"ap1_v131.frm\", \"gy1_v121.frm\", \"gy1_v130.frm\"]\n",
    "for f in firmwares:\n",
    "    fname = os.path.sep.join(['firmwares', f])\n",
    "    with open(fname, \"rb\") as fin:\n",
    "        partnum = 1\n",
    "        s = mmap.mmap(fin.fileno(), 0 , access = mmap.ACCESS_READ)\n",
    "        last = i = s.find('UNITY', 0)\n",
    "        while i >= 0:\n",
    "            # print f, i\n",
    "            i = s.find('UNITY', i+1)\n",
    "            outname = os.path.sep.join(['firmwares', 'parts', f])\n",
    "            with open(\"%s.part%d\" % (outname, partnum), \"wb\") as fout:\n",
    "                fout.write(s[last:i])\n",
    "                last = i\n",
    "                partnum += 1\n",
    "        s.close()\n",
    "!ls -sh firmwares/parts/*.part[1,2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have 5 firmwares\n",
    "3 for m15 and 2 for S\n",
    "we can see that \n",
    "- there are section in these files with very low entropy (see below)\n",
    "- we verified that the same XOR keys generator is used for both version of product.\n",
    "- we verified these low entropy sections to be alternatinf 0x380 blocks of 0x00 and 0xff.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<Part 'gy1_v121.frm.part2', size: 0x00286000\n",
      " embed:\tSubPart y02firm8 size:0x285654 >\n",
      "<Part 'gy1_v130.frm.part1', size: 0x00619e00\n",
      " embed:\tSubPart y02firm6 size:0x393580\n",
      "\tSubPart gy1_v130up size:0x286000 >\n",
      "<Part 'gy1_v130.frm.part2', size: 0x00286000\n",
      " embed:\tSubPart y02firm8 size:0x285654 >\n",
      "<Part 'gy1_v121.frm.part1', size: 0x00619e00\n",
      " embed:\tSubPart y02firm6 size:0x393580\n",
      "\tSubPart gy1_v121up size:0x286000 >\n",
      "<Part 'ap1_v130.frm.part2', size: 0x0024dc00\n",
      " embed:\tSubPart z01firm8 size:0x24d254 >\n",
      "<Part 'ap1_v130.frm.part1', size: 0x004f3800\n",
      " embed:\tSubPart z01firm6 size:0x2a5240\n",
      "\tSubPart ap1_v130up size:0x24dc00 >\n",
      "<Part 'ap1_v110.frm.part1', size: 0x004ef400\n",
      " embed:\tSubPart z01firm6 size:0x2a5240\n",
      "\tSubPart ap1_v110up size:0x249800 >\n",
      "<Part 'ap1_v110.frm.part2', size: 0x00249800\n",
      " embed:\tSubPart z01firm8 size:0x248e54 >\n",
      "<Part 'ap1_v131.frm.part2', size: 0x0024dc00\n",
      " embed:\tSubPart z01firm8 size:0x24d254 >\n",
      "<Part 'ap1_v131.frm.part1', size: 0x004f3800\n",
      " embed:\tSubPart z01firm6 size:0x2a5240\n",
      "\tSubPart ap1_v131up size:0x24dc00 >\n"
     ]
    }
   ],
   "source": [
    "# the firmware\n",
    "firmwares = [\"ap1_v110.frm\", \"ap1_v130.frm\", \"ap1_v131.frm\", \"gy1_v121.frm\", \"gy1_v130.frm\"]\n",
    "simple_filenames = [f+'.part1' for f in firmwares] + [f+'.part2' for f in firmwares]\n",
    "filenames = [os.path.sep.join(['firmwares', 'parts', f]) for f in simple_filenames]\n",
    "\n",
    "firmwares_parts = dict([(os.path.basename(fname),FirmwarePart(fname)) for fname in filenames])\n",
    "firmwares_parts_m15 = dict([(fname, part) for fname, part in firmwares_parts.items() if \"ap1\" in fname])\n",
    "firmwares_parts_s = dict([(fname, part) for fname, part in firmwares_parts.items() if \"gy1\" in fname])\n",
    "\n",
    "for x in firmwares_parts.values():\n",
    "    print x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## XOR key heuristic based on NULL values\n",
    "\n",
    "The first heuristic is too pretend that the most common byte value is 0x00. The function find_xor_key_3 is the heuristic implementation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gy1_v121.frm.part2 ... 5164 sections/keys and 5164 distinct keys\n",
      "wrote 5164 keys to firmwares/keys/gy1_v121.frm.part2.pkeys\n",
      "gy1_v130.frm.part1 ... 7323 sections/keys and 7323 distinct keys\n",
      "wrote 7323 keys to firmwares/keys/gy1_v130.frm.part1.pkeys\n",
      "gy1_v130.frm.part2 ... 5164 sections/keys and 5164 distinct keys\n",
      "wrote 5164 keys to firmwares/keys/gy1_v130.frm.part2.pkeys\n",
      "gy1_v121.frm.part1 ... 7323 sections/keys and 7323 distinct keys\n",
      "wrote 7323 keys to firmwares/keys/gy1_v121.frm.part1.pkeys\n",
      "ap1_v130.frm.part2 ... 4714 sections/keys and 4714 distinct keys\n",
      "wrote 4714 keys to firmwares/keys/ap1_v130.frm.part2.pkeys\n",
      "ap1_v130.frm.part1 ... 5418 sections/keys and 5418 distinct keys\n",
      "wrote 5418 keys to firmwares/keys/ap1_v130.frm.part1.pkeys\n",
      "ap1_v110.frm.part1 ... 5418 sections/keys and 5418 distinct keys\n",
      "wrote 5418 keys to firmwares/keys/ap1_v110.frm.part1.pkeys\n",
      "ap1_v110.frm.part2 ... 4680 sections/keys and 4680 distinct keys\n",
      "wrote 4680 keys to firmwares/keys/ap1_v110.frm.part2.pkeys\n",
      "ap1_v131.frm.part2 ... 4714 sections/keys and 4714 distinct keys\n",
      "wrote 4714 keys to firmwares/keys/ap1_v131.frm.part2.pkeys\n",
      "ap1_v131.frm.part1 ... 5418 sections/keys and 5418 distinct keys\n",
      "wrote 5418 keys to firmwares/keys/ap1_v131.frm.part1.pkeys\n"
     ]
    }
   ],
   "source": [
    "# apply find_xor_key_3 xor heuristic to all section in memory\n",
    "for fname, firm in firmwares_parts.items():\n",
    "    print fname,\"...\",\n",
    "    firm.search_xor_keys(find_xor_key_3)\n",
    "    unxor_fname = os.path.sep.join(['firmwares', 'unxor', fname])\n",
    "    firm.write_to_file(unxor_fname+'.unxor.orig')\n",
    "    # firm.apply_xor(keys)\n",
    "    fullname = os.path.sep.join(['firmwares', 'keys', fname])\n",
    "    key_manager.add_keys(fname, firm.get_section_keys())\n",
    "    # save keys to file\n",
    "    key_manager.save_keys_to_file(fname, fname)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## KT attacks gain low entropy sections of alternating 0x00 0xff\n",
    "\n",
    "In this heuristic, we will looks at low entropy sections of the firmware. The sections, in both the m15 and the theta S, are composed of alternating blocks of 0x380 bytes of 0xff or 0x00 bytes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ricoh M15 Firmwares part\n",
      "Low entropy from 0x9b0a0 -> 0x15e2a0 # 620 kB-> 1400 kB\n"
     ]
    },
    {
     "data": {
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mcrTOLrvQ64sucj/wjBxJ8Xy832wdbGlRx5i3MXeuula8YniTQgSgIPjAF7gI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\nMQVe6/wAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import collections\n",
    "# the alternating 0x00, 0xff blocks indexes\n",
    "alt_blocks = {}\n",
    "# show entropy from binwalk\n",
    "from IPython.display import Image\n",
    "# 0x99e00 starts a section of 0x00 for 0x580 bytes\n",
    "# M15 firmware low entropy section.\n",
    "# 0x99e00 -> 0x158000+ is a good know text attack\n",
    "# most easy from 0x9d000-> 0x158000+\n",
    "# we _know_ that 0xf98a0 is a start of block (00)\n",
    "known_good_start = 0xf98a0\n",
    "supposed_start = 0x9ae00\n",
    "start_blocks_addr = [i for i in range(known_good_start, supposed_start, -0x380)][-1]\n",
    "# part2 confirms with clear text at 0x9b7e0\n",
    "# finishing is not confirmed. Its weird down there.\n",
    "supposed_stop = 0x15e600\n",
    "stop_blocks_addr = [i for i in range(known_good_start, supposed_stop, 0x380)][-1]\n",
    "# print hex(stop_blocks_addr) # 0x15e2a0\n",
    "print \"Ricoh M15 Firmwares part\\nLow entropy from 0x%x -> 0x%x # %d kB-> %d kB\" % (start_blocks_addr, stop_blocks_addr, start_blocks_addr/1024, stop_blocks_addr/1024)\n",
    "alt_blocks['m15'] = (start_blocks_addr, stop_blocks_addr)\n",
    "# entropy.png was generated from binwalk\n",
    "Image(filename='firmwares/parts/entropy.ap1_v110.frm.part1.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ricoh Theta S Firmware Parts\n",
      "Low entropy from 0x17c0b0 -> 0x23ddb0 # 1520 kB-> 2295 kB\n"
     ]
    },
    {
     "data": {
      "image/png": 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A6ULLRG6g8sNlAZAbOpuERc2g8QGsX1Rr9qqiEsUBggM+5GPee885KImfjYYx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\nAdQaQNX44fdajPIhfojPX36ODaEBnDx5Mk466SS89dZbmDdvHgqFAs4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vNNEQBGs2699qzSmjVARUX45YTi9YrxDRNVvRuOctxDo5w65R/42ih6x1K8x1gyMoBkbZbMAPbv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\nrkJggg==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# MS firmware low entropy section.\n",
    "ap1_v130p1 = firmwares_parts['ap1_v130.frm.part1']\n",
    "ap1_v130p2 = firmwares_parts['ap1_v130.frm.part2']\n",
    "gy1_v121p1 = firmwares_parts['gy1_v121.frm.part1']\n",
    "gy1_v121p2 = firmwares_parts['gy1_v121.frm.part2']\n",
    "\n",
    "# low entropy at 0x95800->0xe8e00, probably a lot of Null bytes.\n",
    "# 0x0017be00 starts a 0x380 section of 0x00. \n",
    "# then the pattern of alternating 0x380 blocks\n",
    "# -> 0023e000\n",
    "\n",
    "# good decryption in part2\n",
    "known_good_start = 0x00185ab0\n",
    "# print \"start at 0x%x end expected at 0x%x\" %(known_good_start, (0x185ab0 + 0x380)) #\n",
    "supposed_start = 0x0017c000\n",
    "start_blocks_addr = [i for i in range(known_good_start, supposed_start, -0x380)][-1]\n",
    "\n",
    "# finishing is not confirmed. Its weird down there.\n",
    "supposed_stop = 0x23e000\n",
    "stop_blocks_addr = [i for i in range(known_good_start, supposed_stop, 0x380)][-1]\n",
    "#print \"start at 0x%x end expected at 0x%x\" %(known_good_start, (0x185ab0 + 0x380)) #\n",
    "\n",
    "print \"Ricoh Theta S Firmware Parts\\nLow entropy from 0x%x -> 0x%x # %d kB-> %d kB\" % (start_blocks_addr, stop_blocks_addr, start_blocks_addr/1024, stop_blocks_addr/1024)\n",
    "alt_blocks['s'] = (start_blocks_addr, stop_blocks_addr)\n",
    "Image(filename='firmwares/parts/entropy.gy1_v130.frm.part1.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# TODO SANDBOX\n",
    "# use the alternating pattern of part1.\n",
    "# 0x99e00 -> 0x158000+ is a good know text attack\n",
    "# most easy from 0x9d000-> 0x158000+\n",
    "# change_key (, '4b0f08fe') # kt @0x9d000\n",
    "\n",
    "# we have a very good pattern match vfor bit1, Except at 0x000d4600 +0x200*32 (0x000d8600)\n",
    "if False:\n",
    "    addr = 0x38800\n",
    "    print \n",
    "    s1 = firmwares_parts['gy1_v121.frm.part1']\n",
    "    s2 = firmwares_parts['gy1_v121.frm.part2']\n",
    "    if False:\n",
    "        print hexdump_kt(s1[addr], s2[addr], addr)\n",
    "        addr += 0x200\n",
    "        print hexdump_kt(s1[addr], s2[addr], addr)\n",
    "        addr += 0x200\n",
    "        print hexdump_kt(s1[addr], s2[addr], addr)\n",
    "    else:\n",
    "        s1, s2, c = comp_at(s1, s2, addr)\n",
    "        #s1.reset_xor(find_xor_key_3)\n",
    "        #s2.reset_xor(find_xor_key_3)\n",
    "        #print try_reverse_key(s1)\n",
    "        #print try_reverse_key(s1, 'fefefefe')\n",
    "        print try_key(s2, 'a2dd405c')\n",
    "        #s1, s2, c = comp_at(ap1_v130p1, ap1_v130p2, 0x000f9a00)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1562 keys updated from 0x9b0a0 (1236) to 0x15e2a0 (2797) in ap1_v130.frm.part1\n",
      "1551 keys updated from 0x17c0b0 (3036) to 0x23ddb0 (4586) in gy1_v121.frm.part1\n",
      "KeyManager: replacing key list for  gy1_v121.frm.part2\n",
      "KeyManager: replacing key list for  gy1_v130.frm.part1\n",
      "KeyManager: replacing key list for  gy1_v130.frm.part2\n",
      "KeyManager: replacing key list for  gy1_v121.frm.part1\n",
      "KeyManager: replacing key list for  ap1_v130.frm.part2\n",
      "KeyManager: replacing key list for  ap1_v130.frm.part1\n",
      "KeyManager: replacing key list for  ap1_v110.frm.part1\n",
      "KeyManager: replacing key list for  ap1_v110.frm.part2\n",
      "KeyManager: replacing key list for  ap1_v131.frm.part2\n",
      "KeyManager: replacing key list for  ap1_v131.frm.part1\n"
     ]
    }
   ],
   "source": [
    "def kt_attack_alt_0f_blocks(start_address, stop_address, kt_firmware, firm_list):\n",
    "    ''' Runs a known text attack on low entropy sections of the firmware parts.\n",
    "    '''\n",
    "    # we know there are blocks of favorable 0x00 then 0xff\n",
    "    # we look at each block, and find_xor for the most common word in that block, in that section.\n",
    "    # taking in account that block can overlap two section (different xor keys between sections)\n",
    "    # we weight each key, and take the most promising for that section.\n",
    "    block_length = 0x380\n",
    "    \n",
    "    kt_keys_2 = collections.defaultdict(list)\n",
    "\n",
    "    # for addr in range(0xf98a0, 0x000fb200, block_length): # works\n",
    "    # for addr in range(0xb9000, 0xba000, block_length):\n",
    "    # for addr in range(0x9d020, 0xad000, block_length): # works\n",
    "    # for addr in range(start_blocks_addr, start_blocks_addr+0x1000, block_length):\n",
    "    # for addr in range(stop_blocks_addr1, stop_blocks_addr, block_length):\n",
    "\n",
    "    for addr in range(start_address, stop_address, block_length):    \n",
    "        section_start = kt_firmware[addr]._start\n",
    "        block_start = addr\n",
    "        ## print \"Block start:\", hex(addr), \"Section Start:\", hex(section_start)\n",
    "\n",
    "        # we expected that the most common xor key will be \n",
    "        pt = '\\x00\\x00\\x00\\x00'\n",
    "        # for addr-> min(addr+0x1c0, end_ofsection)\n",
    "        start = addr\n",
    "        target_end = addr+0x1c0\n",
    "        end_section = section_start + kt_firmware[start]._length\n",
    "        end = min(end_section, target_end)\n",
    "        if target_end == end:\n",
    "            # block is not between section\n",
    "            hkey1, stats = kt_firmware.search_xor_keys_by_addr(find_xor_key, start, end)\n",
    "            weight1 = float(end-start)/block_length\n",
    "            kt_keys_2[section_start].append((weight1,hkey1))\n",
    "            ## print \"kt_keys[0x%x] = %s # kt blocks 1 analysis (0x00) at @0x%x weight:%0.2f\" % (section_start, hkey1, start, weight1) \n",
    "        else:\n",
    "            # block is between section\n",
    "            next_section_start = kt_firmware[target_end]._start\n",
    "            hkey1, stats1 = kt_firmware.search_xor_keys_by_addr(find_xor_key, start, end)\n",
    "            hkey12, stats12 = kt_firmware.search_xor_keys_by_addr(find_xor_key, end, target_end)\n",
    "            # we have to merge stats12 with the below stats2\n",
    "            weight1 = float(end-start)/block_length\n",
    "            weight12 = float(target_end-end)/block_length\n",
    "            kt_keys_2[section_start].append((weight1,hkey1))\n",
    "            kt_keys_2[next_section_start].append((weight12,hkey12))\n",
    "            ## print \"kt_keys[0x%x] = %s # kt blocks 1 analysis (0x00) at @0x%x weight:%0.2f\" % (section_start, hkey1, start, weight1) \n",
    "            ## print \"kt_keys[0x%x] = %s # kt blocks 1.2 analysis (0x00) at @0x%x weight:%0.2f\" % (next_section_start, hkey12, start, weight12) \n",
    "\n",
    "        ## look for \n",
    "        pt = '\\xff\\xff\\xff\\xff'\n",
    "        start = addr+0x1c0\n",
    "        section_start = kt_firmware[start]._start\n",
    "        target_end = addr+0x380\n",
    "        end_section = section_start + kt_firmware[start]._length\n",
    "        end = min(end_section, target_end)\n",
    "        if target_end == end:\n",
    "            # block is not between section\n",
    "            hkey2, stats2 = kt_firmware.search_xor_keys_by_addr(find_xor_key, start, end)\n",
    "            hkey2 = hkey_reverse(hkey2)\n",
    "            weight2 = float(end-start)/block_length\n",
    "            kt_keys_2[section_start].append((weight2,hkey2))\n",
    "            ## print \"kt_keys[0x%x] = %s # kt blocks 2 analysis (0xff) at @0x%x weight:%0.2f\" % (section_start, hkey2, start, weight2) \n",
    "        else:\n",
    "            # block is between section\n",
    "            next_section_start = kt_firmware[target_end]._start\n",
    "            hkey2, stats2 = kt_firmware.search_xor_keys_by_addr(find_xor_key, start, end)\n",
    "            hkey22, stats22 = kt_firmware.search_xor_keys_by_addr(find_xor_key, end, target_end)\n",
    "            hkey2 = hkey_reverse(hkey2)\n",
    "            hkey22 = hkey_reverse(hkey22)\n",
    "            weight2 = float(end-start)/block_length\n",
    "            weight22 = float(target_end-end)/block_length\n",
    "            kt_keys_2[section_start].append((weight2,hkey2))\n",
    "            kt_keys_2[next_section_start].append((weight22,hkey22))\n",
    "            ## print \"kt_keys[0x%x] = %s # kt blocks 2 analysis (0xff) at @0x%x weight:%0.2f\" % (section_start, hkey2, start, weight2) \n",
    "            ## print \"kt_keys[0x%x] = %s # kt blocks 2.2 analysis (0xff) at @0x%x weight:%0.2f\" % (next_section_start, hkey22, start, weight22) \n",
    "\n",
    "    # sort by address\n",
    "    kt_keys = kt_keys_2.items()\n",
    "    kt_keys.sort(key=lambda x: x[0])\n",
    "    for addr, wkeys in kt_keys:\n",
    "        # add similar key's weight\n",
    "        weights = collections.defaultdict(float)\n",
    "        for weight, hkey in wkeys:\n",
    "            weights[hkey] += weight\n",
    "        weights = weights.items()\n",
    "        # sort by weight\n",
    "        weights.sort(key=lambda x:x[1], reverse=True)\n",
    "        hkey = weights[0][0]\n",
    "        i = get_i_from_address(addr)\n",
    "        ## print change_string(i, hkey, addr, r='kt blocks')\n",
    "        for firm in firm_list:\n",
    "            try:\n",
    "                firm[addr].xor(binascii.unhexlify(hkey))\n",
    "            except IndexError:\n",
    "                pass\n",
    "    start_i = get_i_from_address(kt_keys[0][0])\n",
    "    stop_i = get_i_from_address(kt_keys[-1][0])\n",
    "    print \"%d keys updated from 0x%x (%d) to 0x%x (%d) in %s\" % (len(kt_keys), start_address, start_i, stop_address, stop_i, kt_firmware.name)\n",
    "    # DEBUG\n",
    "    # for addr, hkey in kt_keys:\n",
    "    #     print hexdump_kt(ap1_v130p1[addr], ap1_v130p2[addr], addr)\n",
    "    return\n",
    "\n",
    "# run the KT attack from m15 firmwares KT to all firmwares\n",
    "start_blocks_addr, stop_blocks_addr = alt_blocks['m15']\n",
    "kt_attack_alt_0f_blocks(start_blocks_addr, stop_blocks_addr, ap1_v130p1, firmwares_parts_m15.values() + firmwares_parts_s.values())\n",
    "start_blocks_addr, stop_blocks_addr = alt_blocks['s']\n",
    "kt_attack_alt_0f_blocks(start_blocks_addr, stop_blocks_addr, gy1_v121p1, firmwares_parts_m15.values() + firmwares_parts_s.values())\n",
    "\n",
    "# update our key store\n",
    "for fname, firm in firmwares_parts.items():\n",
    "    key_manager.add_keys(fname, firm.get_section_keys())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Merging both heuristic\n",
    "\n",
    "The heuristic have been applied to all firmwares files. We merge results by choosing the most common key used at each section across all firmwares parts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrote 7323 keys to firmwares/keys/consensus.pkeys\n",
      "Consensus keys: 7323 keys save to firmwares/keys/consensus.pkeys\n"
     ]
    }
   ],
   "source": [
    "# read all keys, take the most common key shared by all firmware parts\n",
    "# we will call that list the consensus list, from our xor heuristic + KT attack.\n",
    "consensus_keys = []\n",
    "consensus_alt = []\n",
    "keylist_names = key_manager.keylist.keys()\n",
    "for i in range(0, len(key_manager.get_keys('gy1_v130.frm.part1'))):\n",
    "    _keys = []\n",
    "    for fname in keylist_names:\n",
    "        temp_keylist = key_manager.get_keys(fname)\n",
    "        if len(temp_keylist) > i:\n",
    "            _keys.append(temp_keylist[i])\n",
    "    consensus_keys.append(collections.Counter(_keys).most_common(1)[0][0])\n",
    "    consensus_alt.append(collections.Counter(_keys))\n",
    "#\n",
    "key_manager.add_keys('consensus', consensus_keys)\n",
    "# save keys to file\n",
    "nb, fname = key_manager.save_keys_to_file('consensus', 'consensus')\n",
    "print 'Consensus keys: %d keys save to %s' % (len(consensus_keys), fname)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gy1_v121.frm.part2 ... 5164/5164 sections Xor-ed\n",
      "wrote to firmwares/unxor/gy1_v121.frm.part2.unxor.2\n",
      "gy1_v130.frm.part1 ... 7323/7323 sections Xor-ed\n",
      "wrote to firmwares/unxor/gy1_v130.frm.part1.unxor.2\n",
      "gy1_v130.frm.part2 ... 5164/5164 sections Xor-ed\n",
      "wrote to firmwares/unxor/gy1_v130.frm.part2.unxor.2\n",
      "gy1_v121.frm.part1 ... 7323/7323 sections Xor-ed\n",
      "wrote to firmwares/unxor/gy1_v121.frm.part1.unxor.2\n",
      "ap1_v130.frm.part2 ... 4714/4714 sections Xor-ed\n",
      "wrote to firmwares/unxor/ap1_v130.frm.part2.unxor.2\n",
      "ap1_v130.frm.part1 ... 5418/5418 sections Xor-ed\n",
      "wrote to firmwares/unxor/ap1_v130.frm.part1.unxor.2\n",
      "ap1_v110.frm.part1 ... 5418/5418 sections Xor-ed\n",
      "wrote to firmwares/unxor/ap1_v110.frm.part1.unxor.2\n",
      "ap1_v110.frm.part2 ... 4680/4680 sections Xor-ed\n",
      "wrote to firmwares/unxor/ap1_v110.frm.part2.unxor.2\n",
      "ap1_v131.frm.part2 ... 4714/4714 sections Xor-ed\n",
      "wrote to firmwares/unxor/ap1_v131.frm.part2.unxor.2\n",
      "ap1_v131.frm.part1 ... 5418/5418 sections Xor-ed\n",
      "wrote to firmwares/unxor/ap1_v131.frm.part1.unxor.2\n"
     ]
    }
   ],
   "source": [
    "# save the previous unxor to file\n",
    "for fname, firm in firmwares_parts.items():\n",
    "    print fname, \"...\",\n",
    "    firm.apply_xor(key_manager.get_keys('consensus'))\n",
    "    unxor_filename = os.path.sep.join(['firmwares', 'unxor', fname]) + '.unxor.2'\n",
    "    firm.write_to_file(unxor_filename)\n",
    "    print 'wrote to', unxor_filename"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# TODO send to sandbox\n",
    "# depends on consensus_keys, consensus_alt\n",
    "if False:\n",
    "    # show alternate keys for address.\n",
    "    address = 0x186e00\n",
    "    for fname, firm in firmwares_parts.items():\n",
    "        if address <= len(firm):\n",
    "            previously, _ = firm.search_xor_keys_by_addr(find_xor_key, address, address+0x200)\n",
    "            i = get_i_from_address(address)\n",
    "            consensus = consensus_keys[i]\n",
    "            print fname, \"key:\" , firm[address].hex_key, \"previous autokey:\", previously, \"rev:\",hkey_reverse(previously), \"cons:\", consensus\n",
    "    final_i = get_i_from_address(address)\n",
    "    print consensus_alt[final_i]\n",
    "\n",
    "    # ap1_v130.frm.part2.unxor.2 seems to disagree on 0x189800 key given by kt_attack_alt_0f_blocks\n",
    "    # TODO use kt_attack_alt_0f_blocks on easy KT, but on all part1 individually as KT.\n",
    "    k2 = '2d736b6a'\n",
    "    for firm in firmwares_parts.values():\n",
    "        print firm.name\n",
    "        #try_key(firm[address], k2)\n"
   ]
  }
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