url.cpp in libproxy through 0.4.15 is prone to a buffer overflow when PAC is enabled, as demonstrated by a large PAC file that is delivered without a Content-length header.
CVSS Score
9.8
EPSS Score
0.009
Published
2020-09-30
In rfb/CSecurityTLS.cxx and rfb/CSecurityTLS.java in TigerVNC before 1.11.0, viewers mishandle TLS certificate exceptions. They store the certificates as authorities, meaning that the owner of a certificate could impersonate any server after a client had added an exception.
CVSS Score
8.1
EPSS Score
0.007
Published
2020-09-27
In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, when determining the common dimension size of two tensors, TFLite uses a `DCHECK` which is no-op outside of debug compilation modes. Since the function always returns the dimension of the first tensor, malicious attackers can craft cases where this is larger than that of the second tensor. In turn, this would result in reads/writes outside of bounds since the interpreter will wrongly assume that there is enough data in both tensors. The issue is patched in commit 8ee24e7949a203d234489f9da2c5bf45a7d5157d, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.
CVSS Score
7.4
EPSS Score
0.003
Published
2020-09-25
In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, a crafted TFLite model can force a node to have as input a tensor backed by a `nullptr` buffer. This can be achieved by changing a buffer index in the flatbuffer serialization to convert a read-only tensor to a read-write one. The runtime assumes that these buffers are written to before a possible read, hence they are initialized with `nullptr`. However, by changing the buffer index for a tensor and implicitly converting that tensor to be a read-write one, as there is nothing in the model that writes to it, we get a null pointer dereference. The issue is patched in commit 0b5662bc, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.
CVSS Score
5.9
EPSS Score
0.004
Published
2020-09-25
In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, if a TFLite saved model uses the same tensor as both input and output of an operator, then, depending on the operator, we can observe a segmentation fault or just memory corruption. We have patched the issue in d58c96946b and will release patch releases for all versions between 1.15 and 2.3. We recommend users to upgrade to TensorFlow 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.
CVSS Score
6.5
EPSS Score
0.003
Published
2020-09-25
In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code.
CVSS Score
4.8
EPSS Score
0.003
Published
2020-09-25
In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the `Shard` API in TensorFlow expects the last argument to be a function taking two `int64` (i.e., `long long`) arguments. However, there are several places in TensorFlow where a lambda taking `int` or `int32` arguments is being used. In these cases, if the amount of work to be parallelized is large enough, integer truncation occurs. Depending on how the two arguments of the lambda are used, this can result in segfaults, read/write outside of heap allocated arrays, stack overflows, or data corruption. The issue is patched in commits 27b417360cbd671ef55915e4bb6bb06af8b8a832 and ca8c013b5e97b1373b3bb1c97ea655e69f31a575, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.
CVSS Score
9.0
EPSS Score
0.005
Published
2020-09-25
In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, by controlling the `fill` argument of tf.strings.as_string, a malicious attacker is able to trigger a format string vulnerability due to the way the internal format use in a `printf` call is constructed. This may result in segmentation fault. The issue is patched in commit 33be22c65d86256e6826666662e40dbdfe70ee83, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.
CVSS Score
7.5
EPSS Score
0.004
Published
2020-09-25
In eager mode, TensorFlow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1 does not set the session state. Hence, calling `tf.raw_ops.GetSessionHandle` or `tf.raw_ops.GetSessionHandleV2` results in a null pointer dereference In linked snippet, in eager mode, `ctx->session_state()` returns `nullptr`. Since code immediately dereferences this, we get a segmentation fault. The issue is patched in commit 9a133d73ae4b4664d22bd1aa6d654fec13c52ee1, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.
CVSS Score
5.3
EPSS Score
0.002
Published
2020-09-25
In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the `data_splits` argument of `tf.raw_ops.StringNGrams` lacks validation. This allows a user to pass values that can cause heap overflow errors and even leak contents of memory In the linked code snippet, all the binary strings after `ee ff` are contents from the memory stack. Since these can contain return addresses, this data leak can be used to defeat ASLR. The issue is patched in commit 0462de5b544ed4731aa2fb23946ac22c01856b80, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.
CVSS Score
9.0
EPSS Score
0.005
Published
2020-09-25