1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
|
from test_support import verbose
import random
# From SF bug #422121: Insecurities in dict comparison.
# Safety of code doing comparisons has been an historical Python weak spot.
# The problem is that comparison of structures written in C *naturally*
# wants to hold on to things like the size of the container, or "the
# biggest" containee so far, across a traversal of the container; but
# code to do containee comparisons can call back into Python and mutate
# the container in arbitrary ways while the C loop is in midstream. If the
# C code isn't extremely paranoid about digging things out of memory on
# each trip, and artificially boosting refcounts for the duration, anything
# from infinite loops to OS crashes can result (yes, I use Windows <wink>).
#
# The other problem is that code designed to provoke a weakness is usually
# white-box code, and so catches only the particular vulnerabilities the
# author knew to protect against. For example, Python's list.sort() code
# went thru many iterations as one "new" vulnerability after another was
# discovered.
#
# So the dict comparison test here uses a black-box approach instead,
# generating dicts of various sizes at random, and performing random
# mutations on them at random times. This proved very effective,
# triggering at least six distinct failure modes the first 20 times I
# ran it. Indeed, at the start, the driver never got beyond 6 iterations
# before the test died.
# The dicts are global to make it easy to mutate tham from within functions.
dict1 = {}
dict2 = {}
# The current set of keys in dict1 and dict2. These are materialized as
# lists to make it easy to pick a dict key at random.
dict1keys = []
dict2keys = []
# Global flag telling maybe_mutate() wether to *consider* mutating.
mutate = 0
# If global mutate is true, consider mutating a dict. May or may not
# mutate a dict even if mutate is true. If it does decide to mutate a
# dict, it picks one of {dict1, dict2} at random, and deletes a random
# entry from it.
def maybe_mutate():
if not mutate:
return
if random.random() < 0.5:
return
if random.random() < 0.5:
target, keys = dict1, dict1keys
else:
target, keys = dict2, dict2keys
if keys:
i = random.randrange(len(keys))
key = keys[i]
del target[key]
# CAUTION: don't use keys.remove(key) here. Or do <wink>. The
# point is that .remove() would trigger more comparisons, and so
# also more calls to this routine. We're mutating often enough
# without that.
del keys[i]
# A horrid class that triggers random mutations of dict1 and dict2 when
# instances are compared.
class Horrid:
def __init__(self, i):
# Comparison outcomes are determined by the value of i.
self.i = i
# An artificial hashcode is selected at random so that we don't
# have any systematic relationship between comparison outcomes
# (based on self.i and other.i) and relative position within the
# hash vector (based on hashcode).
self.hashcode = random.randrange(1000000000)
def __hash__(self):
return self.hashcode
def __cmp__(self, other):
maybe_mutate() # The point of the test.
return cmp(self.i, other.i)
def __repr__(self):
return "Horrid(%d)" % self.i
# Fill dict d with numentries (Horrid(i), Horrid(j)) key-value pairs,
# where i and j are selected at random from the candidates list.
# Return d.keys() after filling.
def fill_dict(d, candidates, numentries):
d.clear()
for i in xrange(numentries):
d[Horrid(random.choice(candidates))] = \
Horrid(random.choice(candidates))
return d.keys()
# Test one pair of randomly generated dicts, each with n entries.
# Note that dict comparison is trivial if they don't have the same number
# of entires (then the "shorter" dict is instantly considered to be the
# smaller one, without even looking at the entries).
def test_one(n):
global mutate, dict1, dict2, dict1keys, dict2keys
# Fill the dicts without mutating them.
mutate = 0
dict1keys = fill_dict(dict1, range(n), n)
dict2keys = fill_dict(dict2, range(n), n)
# Enable mutation, then compare the dicts so long as they have the
# same size.
mutate = 1
if verbose:
print "trying w/ lengths", len(dict1), len(dict2),
while dict1 and len(dict1) == len(dict2):
if verbose:
print ".",
c = cmp(dict1, dict2)
if verbose:
print
# Run test_one n times. At the start (before the bugs were fixed), 20
# consecutive runs of this test each blew up on or before the sixth time
# test_one was run. So n doesn't have to be large to get an interesting
# test.
# OTOH, calling with large n is also interesting, to ensure that the fixed
# code doesn't hold on to refcounts *too* long (in which case memory would
# leak).
def test(n):
for i in xrange(n):
test_one(random.randrange(1, 100))
# See last comment block for clues about good values for n.
test(100)
|