plistlib creates a new objects when read references instead of using
already read object.

As result it doesn't preserve identity:

>>> import plistlib
>>> a = [['spam']]*2
>>> a[0] is a[1]
True
>>> b = plistlib.loads(plistlib.dumps(a, fmt=plistlib.FMT_BINARY))
>>> b == a
True
>>> b[0] is b[1]
False

And plistlib.loads() is vulnerable to plists containing cyclic
references (as was exposed in issue31897). For example,
plistlib.loads(b'bplist00\xa1\x00\x08\x00\x00\x00\x00\x00\x00\x01\x01\x00\x00\x00\x00\x00\x00\x00\x01\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x0a')
could return a list containing itself, but it is failed with
RecursionError.

plistlib.dumps() preserves reference in the output, but it saves
redundant copies. For example plistlib.dumps([[]]*5,
fmt=plistlib.FMT_BINARY) saves a list containing 5 identical empty
lists, but it saves an empty list 5 times, and only the last copy is
used. The other 4 copies are not referenced and just spent the file
volume and the space of reference numbers. Saving
[[[[['spam']*100]*100]*100]*100]*100 will result in a multigigabyte,
while less than a kilobyte would be enough for saving it. Loading
properly saved [[[[['spam']*100]*100]*100]*100]*100 withe the current
plistlib.loads() will cause consuming many gigabytes of memory.

1. The issues with plistlib.dumps() are:
1a) Inefficient saving data with references. This is minor resource usage issue.
1b) Impossibility to save a data with cyclic references. This is a
lack of a feature.

2. The issues with plistlib.loads() are:
2a) Inefficient loading data with references. This can be not just a
resource usage issue, but a security issue. Loading an malicious input
data smaller than 100 byte ([[[...]*2]*2]*2) can cause consuming many
gigabytes of memory.
2b) Impossibility to load a data with cyclic references. This is a
lack of a feature, but can be lesser security issue. Small malicious
input can cause RecursionError. If the recursion limit is set high and
you are unlucky it can cause a stack overflow.

Security issues affect you only when you load plists from untrusted sources.

Adding the proper support of references could be considered a new
feature, but taking to account security issues it should be backported
up to 3.4 when the support of binary plists was added.

Ronald, could you please make a review? I want to merge this before 3.7.0a3.

New changeset a897aeeef647259a938a36cb5eb6680c86021c6a by Serhiy Storchaka in branch 'master':
bpo-32072: Fix issues with binary plists. (#4455)
https://github.com/python/cpython/commit/a897aeeef647259a938a36cb5eb6680c86021c6a

New changeset 8cd31082fba88cf0064590fd3d55b6c1c964f11c by Serhiy Storchaka (Miss Islington (bot)) in branch '3.6':
bpo-32072: Fix issues with binary plists. (GH-4455) (#4654)
https://github.com/python/cpython/commit/8cd31082fba88cf0064590fd3d55b6c1c964f11c

For example:

a = []
for i in range(22):
    a = [a, a]

b = plistlib.dumps(a, fmt=plistlib.FMT_BINARY)

The result is 130 bytes long on patched plistlib. But plistlib.dumps(b) will expand to a structure consuming almost a gigabyte of memory on unpatched plistlib. Increasing the level of nesting by one will duplicate memory consumption, so it is easy to consume all available memory on any computer.

New changeset c59731d92dc73111d224876f1caa064097aad786 by larryhastings (Serhiy Storchaka) in branch '3.4':
[3.4] bpo-32072: Fix issues with binary plists. (GH-4455) (#4658)
https://github.com/python/cpython/commit/c59731d92dc73111d224876f1caa064097aad786

New changeset 43f014d3f12468edf61046f0612edc7660042fd5 by larryhastings (Serhiy Storchaka) in branch '3.5':
[3.5] bpo-32072: Fix issues with binary plists. (GH-4455) (#4656)
https://github.com/python/cpython/commit/43f014d3f12468edf61046f0612edc7660042fd5

Thanks Larry.

Thank you for the fix!  I just wish I knew what plists were ;-)

@larry: plists are Apple's equivalent to Windows INI files ;-)