Vulnerabilities (CVE)

Mozilla developers and community members reported memory safety bugs present in Firefox 81 and Firefox ESR 78.3. Some of these bugs showed evidence of memory corruption and we presume that with enough effort some of these could have been exploited to run arbitrary code. This vulnerability affects Firefox ESR < 78.4, Firefox < 82, and Thunderbird < 78.4.

When recursing through graphical layers while scrolling, an iterator may have become invalid, resulting in a potential use-after-free. This occurs because the function APZCTreeManager::ComputeClippedCompositionBounds did not follow iterator invalidation rules. This vulnerability affects Firefox < 81, Thunderbird < 78.3, and Firefox ESR < 78.3.

By exploiting an Open Redirect vulnerability on a website, an attacker could have spoofed the site displayed in the download file dialog to show the original site (the one suffering from the open redirect) rather than the site the file was actually downloaded from. This vulnerability affects Firefox < 81, Thunderbird < 78.3, and Firefox ESR < 78.3.

Firefox sometimes ran the onload handler for SVG elements that the DOM sanitizer decided to remove, resulting in JavaScript being executed after pasting attacker-controlled data into a contenteditable element. This vulnerability affects Firefox < 81, Thunderbird < 78.3, and Firefox ESR < 78.3.

Mozilla developers reported memory safety bugs present in Firefox 80 and Firefox ESR 78.2. Some of these bugs showed evidence of memory corruption and we presume that with enough effort some of these could have been exploited to run arbitrary code. This vulnerability affects Firefox < 81, Thunderbird < 78.3, and Firefox ESR < 78.3.

Mozilla developers and community members reported memory safety bugs present in Firefox 78 and Firefox ESR 78.0. Some of these bugs showed evidence of memory corruption and we presume that with enough effort some of these could have been exploited to run arbitrary code. This vulnerability affects Firefox < 79, Firefox ESR < 68.11, Firefox ESR < 78.1, Thunderbird < 68.11, and Thunderbird < 78.1.

JIT optimizations involving the Javascript arguments object could confuse later optimizations. This risk was already mitigated by various precautions in the code, resulting in this bug rated at only moderate severity. This vulnerability affects Firefox ESR < 78.1, Firefox < 79, and Thunderbird < 78.1.

A redirected HTTP request which is observed or modified through a web extension could bypass existing CORS checks, leading to potential disclosure of cross-origin information. This vulnerability affects Firefox ESR < 78.1, Firefox < 79, and Thunderbird < 78.1.

Go before 1.13.13 and 1.14.x before 1.14.5 has a data race in some net/http servers, as demonstrated by the httputil.ReverseProxy Handler, because it reads a request body and writes a response at the same time.

An issue was discovered in Xen through 4.13.x, allowing Intel guest OS users to gain privileges or cause a denial of service because of non-atomic modification of a live EPT PTE. When mapping guest EPT (nested paging) tables, Xen would in some circumstances use a series of non-atomic bitfield writes. Depending on the compiler version and optimisation flags, Xen might expose a dangerous partially written PTE to the hardware, which an attacker might be able to race to exploit. A guest administrato ... An issue was discovered in Xen through 4.13.x, allowing Intel guest OS users to gain privileges or cause a denial of service because of non-atomic modification of a live EPT PTE. When mapping guest EPT (nested paging) tables, Xen would in some circumstances use a series of non-atomic bitfield writes. Depending on the compiler version and optimisation flags, Xen might expose a dangerous partially written PTE to the hardware, which an attacker might be able to race to exploit. A guest administrator or perhaps even an unprivileged guest user might be able to cause denial of service, data corruption, or privilege escalation. Only systems using Intel CPUs are vulnerable. Systems using AMD CPUs, and Arm systems, are not vulnerable. Only systems using nested paging (hap, aka nested paging, aka in this case Intel EPT) are vulnerable. Only HVM and PVH guests can exploit the vulnerability. The presence and scope of the vulnerability depends on the precise optimisations performed by the compiler used to build Xen. If the compiler generates (a) a single 64-bit write, or (b) a series of read-modify-write operations in the same order as the source code, the hypervisor is not vulnerable. For example, in one test build using GCC 8.3 with normal settings, the compiler generated multiple (unlocked) read-modify-write operations in source-code order, which did not constitute a vulnerability. We have not been able to survey compilers; consequently we cannot say which compiler(s) might produce vulnerable code (with which code-generation options). The source code clearly violates the C rules, and thus should be considered vulnerable.

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An issue was discovered in Xen through 4.13.x, allowing x86 Intel HVM guest OS users to cause a host OS denial of service or possibly gain privileges because of insufficient cache write-back under VT-d. When page tables are shared between IOMMU and CPU, changes to them require flushing of both TLBs. Furthermore, IOMMUs may be non-coherent, and hence prior to flushing IOMMU TLBs, a CPU cache also needs writing back to memory after changes were made. Such writing back of cached data was missing in ... An issue was discovered in Xen through 4.13.x, allowing x86 Intel HVM guest OS users to cause a host OS denial of service or possibly gain privileges because of insufficient cache write-back under VT-d. When page tables are shared between IOMMU and CPU, changes to them require flushing of both TLBs. Furthermore, IOMMUs may be non-coherent, and hence prior to flushing IOMMU TLBs, a CPU cache also needs writing back to memory after changes were made. Such writing back of cached data was missing in particular when splitting large page mappings into smaller granularity ones. A malicious guest may be able to retain read/write DMA access to frames returned to Xen's free pool, and later reused for another purpose. Host crashes (leading to a Denial of Service) and privilege escalation cannot be ruled out. Xen versions from at least 3.2 onwards are affected. Only x86 Intel systems are affected. x86 AMD as well as Arm systems are not affected. Only x86 HVM guests using hardware assisted paging (HAP), having a passed through PCI device assigned, and having page table sharing enabled can leverage the vulnerability. Note that page table sharing will be enabled (by default) only if Xen considers IOMMU and CPU large page size support compatible.

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An issue was discovered in Xen through 4.13.x, allowing x86 HVM guest OS users to cause a hypervisor crash. An inverted conditional in x86 HVM guests' dirty video RAM tracking code allows such guests to make Xen de-reference a pointer guaranteed to point at unmapped space. A malicious or buggy HVM guest may cause the hypervisor to crash, resulting in Denial of Service (DoS) affecting the entire host. Xen versions from 4.8 onwards are affected. Xen versions 4.7 and earlier are not affected. Only ... An issue was discovered in Xen through 4.13.x, allowing x86 HVM guest OS users to cause a hypervisor crash. An inverted conditional in x86 HVM guests' dirty video RAM tracking code allows such guests to make Xen de-reference a pointer guaranteed to point at unmapped space. A malicious or buggy HVM guest may cause the hypervisor to crash, resulting in Denial of Service (DoS) affecting the entire host. Xen versions from 4.8 onwards are affected. Xen versions 4.7 and earlier are not affected. Only x86 systems are affected. Arm systems are not affected. Only x86 HVM guests using shadow paging can leverage the vulnerability. In addition, there needs to be an entity actively monitoring a guest's video frame buffer (typically for display purposes) in order for such a guest to be able to leverage the vulnerability. x86 PV guests, as well as x86 HVM guests using hardware assisted paging (HAP), cannot leverage the vulnerability.

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In Wireshark 3.2.0 to 3.2.4, the GVCP dissector could go into an infinite loop. This was addressed in epan/dissectors/packet-gvcp.c by ensuring that an offset increases in all situations.

In HylaFAX+ through 7.0.2 and HylaFAX Enterprise, the faxsetup utility calls chown on files in user-owned directories. By winning a race, a local attacker could use this to escalate his privileges to root.

In the Linux kernel 4.4 through 5.7.6, usbtest_disconnect in drivers/usb/misc/usbtest.c has a memory leak, aka CID-28ebeb8db770.

An issue was discovered in OpenEXR before v2.5.2. Invalid chunkCount attributes could cause a heap buffer overflow in getChunkOffsetTableSize() in IlmImf/ImfMisc.cpp.

An issue was discovered in OpenEXR before 2.5.2. Invalid input could cause a use-after-free in DeepScanLineInputFile::DeepScanLineInputFile() in IlmImf/ImfDeepScanLineInputFile.cpp.

An issue was discovered in OpenEXR before 2.5.2. An invalid tiled input file could cause invalid memory access in TiledInputFile::TiledInputFile() in IlmImf/ImfTiledInputFile.cpp, as demonstrated by a NULL pointer dereference.

Singularity (an open source container platform) from version 3.1.1 through 3.6.3 has a vulnerability. Due to insecure handling of path traversal and the lack of path sanitization within `unsquashfs`, it is possible to overwrite/create any files on the host filesystem during the extraction with a crafted squashfs filesystem. The extraction occurs automatically for unprivileged (either installation or with `allow setuid = no`) run of Singularity when a user attempt to run an image which is a local ... Singularity (an open source container platform) from version 3.1.1 through 3.6.3 has a vulnerability. Due to insecure handling of path traversal and the lack of path sanitization within `unsquashfs`, it is possible to overwrite/create any files on the host filesystem during the extraction with a crafted squashfs filesystem. The extraction occurs automatically for unprivileged (either installation or with `allow setuid = no`) run of Singularity when a user attempt to run an image which is a local SIF image or a single file containing a squashfs filesystem and is coming from remote sources `library://` or `shub://`. Image build is also impacted in a more serious way as it can be used by a root user, allowing an attacker to overwrite/create files leading to a system compromise, so far bootstrap methods `library`, `shub` and `localimage` are triggering the squashfs extraction. This issue is addressed in Singularity 3.6.4. All users are advised to upgrade to 3.6.4 especially if they use Singularity mainly for building image as root user. There is no solid workaround except to temporary avoid to use unprivileged mode with single file images in favor of sandbox images instead. Regarding image build, temporary avoid to build from `library` and `shub` sources and as much as possible use `--fakeroot` or a VM for that.

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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 ... 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.

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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.

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 t ... 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.

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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 bot ... 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.

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In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, to mimic Python's indexing with negative values, TFLite uses `ResolveAxis` to convert negative values to positive indices. However, the only check that the converted index is now valid is only present in debug builds. If the `DCHECK` does not trigger, then code execution moves ahead with a negative index. This, in turn, results in accessing data out of bounds which results in segfaults and/or data corruption. The issue is ... In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, to mimic Python's indexing with negative values, TFLite uses `ResolveAxis` to convert negative values to positive indices. However, the only check that the converted index is now valid is only present in debug builds. If the `DCHECK` does not trigger, then code execution moves ahead with a negative index. This, in turn, results in accessing data out of bounds which results in segfaults and/or data corruption. The issue is patched in commit 2d88f470dea2671b430884260f3626b1fe99830a, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.

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In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, changing the TensorFlow's `SavedModel` protocol buffer and altering the name of required keys results in segfaults and data corruption while loading the model. This can cause a denial of service in products using `tensorflow-serving` or other inference-as-a-service installments. Fixed were added in commits f760f88b4267d981e13f4b302c437ae800445968 and fcfef195637c6e365577829c4d67681695956e7d (both going into TensorFlow 2.2.0 and ... In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, changing the TensorFlow's `SavedModel` protocol buffer and altering the name of required keys results in segfaults and data corruption while loading the model. This can cause a denial of service in products using `tensorflow-serving` or other inference-as-a-service installments. Fixed were added in commits f760f88b4267d981e13f4b302c437ae800445968 and fcfef195637c6e365577829c4d67681695956e7d (both going into TensorFlow 2.2.0 and 2.3.0 but not yet backported to earlier versions). However, this was not enough, as #41097 reports a different failure mode. The issue is patched in commit adf095206f25471e864a8e63a0f1caef53a0e3a6, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.

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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 0462de5b544ed4731aa2fb23946ac22c01856 ... 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.

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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 ... 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.

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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.

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/w ... 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.

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In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the implementation of `SparseFillEmptyRowsGrad` uses a double indexing pattern. It is possible for `reverse_index_map(i)` to be an index outside of bounds of `grad_values`, thus resulting in a heap buffer overflow. The issue is patched in commit 390611e0d45c5793c7066110af37c8514e6a6c54, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.

In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the `SparseFillEmptyRowsGrad` implementation has incomplete validation of the shapes of its arguments. Although `reverse_index_map_t` and `grad_values_t` are accessed in a similar pattern, only `reverse_index_map_t` is validated to be of proper shape. Hence, malicious users can pass a bad `grad_values_t` to trigger an assertion failure in `vec`, causing denial of service in serving installations. The issue is patched in commit ... In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the `SparseFillEmptyRowsGrad` implementation has incomplete validation of the shapes of its arguments. Although `reverse_index_map_t` and `grad_values_t` are accessed in a similar pattern, only `reverse_index_map_t` is validated to be of proper shape. Hence, malicious users can pass a bad `grad_values_t` to trigger an assertion failure in `vec`, causing denial of service in serving installations. The issue is patched in commit 390611e0d45c5793c7066110af37c8514e6a6c54, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1."

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In Tensorflow before versions 2.2.1 and 2.3.1, the implementation of `dlpack.to_dlpack` can be made to use uninitialized memory resulting in further memory corruption. This is because the pybind11 glue code assumes that the argument is a tensor. However, there is nothing stopping users from passing in a Python object instead of a tensor. The uninitialized memory address is due to a `reinterpret_cast` Since the `PyObject` is a Python object, not a TensorFlow Tensor, the cast to `EagerTensor` fail ... In Tensorflow before versions 2.2.1 and 2.3.1, the implementation of `dlpack.to_dlpack` can be made to use uninitialized memory resulting in further memory corruption. This is because the pybind11 glue code assumes that the argument is a tensor. However, there is nothing stopping users from passing in a Python object instead of a tensor. The uninitialized memory address is due to a `reinterpret_cast` Since the `PyObject` is a Python object, not a TensorFlow Tensor, the cast to `EagerTensor` fails. The issue is patched in commit 22e07fb204386768e5bcbea563641ea11f96ceb8 and is released in TensorFlow versions 2.2.1, or 2.3.1.

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In Tensorflow before versions 2.2.1 and 2.3.1, if a user passes a list of strings to `dlpack.to_dlpack` there is a memory leak following an expected validation failure. The issue occurs because the `status` argument during validation failures is not properly checked. Since each of the above methods can return an error status, the `status` value must be checked before continuing. The issue is patched in commit 22e07fb204386768e5bcbea563641ea11f96ceb8 and is released in TensorFlow versions 2.2.1, ... In Tensorflow before versions 2.2.1 and 2.3.1, if a user passes a list of strings to `dlpack.to_dlpack` there is a memory leak following an expected validation failure. The issue occurs because the `status` argument during validation failures is not properly checked. Since each of the above methods can return an error status, the `status` value must be checked before continuing. The issue is patched in commit 22e07fb204386768e5bcbea563641ea11f96ceb8 and is released in TensorFlow versions 2.2.1, or 2.3.1.

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In Tensorflow before versions 2.2.1 and 2.3.1, if a user passes an invalid argument to `dlpack.to_dlpack` the expected validations will cause variables to bind to `nullptr` while setting a `status` variable to the error condition. However, this `status` argument is not properly checked. Hence, code following these methods will bind references to null pointers. This is undefined behavior and reported as an error if compiling with `-fsanitize=null`. The issue is patched in commit 22e07fb204386768e ... In Tensorflow before versions 2.2.1 and 2.3.1, if a user passes an invalid argument to `dlpack.to_dlpack` the expected validations will cause variables to bind to `nullptr` while setting a `status` variable to the error condition. However, this `status` argument is not properly checked. Hence, code following these methods will bind references to null pointers. This is undefined behavior and reported as an error if compiling with `-fsanitize=null`. The issue is patched in commit 22e07fb204386768e5bcbea563641ea11f96ceb8 and is released in TensorFlow versions 2.2.1, or 2.3.1.

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In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the `tf.raw_ops.Switch` operation takes as input a tensor and a boolean and outputs two tensors. Depending on the boolean value, one of the tensors is exactly the input tensor whereas the other one should be an empty tensor. However, the eager runtime traverses all tensors in the output. Since only one of the tensors is defined, the other one is `nullptr`, hence we are binding a reference to `nullptr`. This is undefined behavio ... In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the `tf.raw_ops.Switch` operation takes as input a tensor and a boolean and outputs two tensors. Depending on the boolean value, one of the tensors is exactly the input tensor whereas the other one should be an empty tensor. However, the eager runtime traverses all tensors in the output. Since only one of the tensors is defined, the other one is `nullptr`, hence we are binding a reference to `nullptr`. This is undefined behavior and reported as an error if compiling with `-fsanitize=null`. In this case, this results in a segmentation fault The issue is patched in commit da8558533d925694483d2c136a9220d6d49d843c, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.

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In FreeRDP less than or equal to 2.1.2, an integer overflow exists due to missing input sanitation in rdpegfx channel. All FreeRDP clients are affected. The input rectangles from the server are not checked against local surface coordinates and blindly accepted. A malicious server can send data that will crash the client later on (invalid length arguments to a `memcpy`) This has been fixed in 2.2.0. As a workaround, stop using command line arguments /gfx, /gfx-h264 and /network:auto

Versions of the npm CLI prior to 6.14.6 are vulnerable to an information exposure vulnerability through log files. The CLI supports URLs like "<protocol>://[<user>[:<password>]@]<hostname>[:<port>][:][/]<path>". The password value is not redacted and is printed to stdout and also to any generated log files.

ntpd in ntp 4.2.8 before 4.2.8p15 and 4.3.x before 4.3.101 allows remote attackers to cause a denial of service (memory consumption) by sending packets, because memory is not freed in situations where a CMAC key is used and associated with a CMAC algorithm in the ntp.keys file.

The server in Chocolate Doom 3.0.0 and Crispy Doom 5.8.0 doesn't validate the user-controlled num_players value, leading to a buffer overflow. A malicious user can overwrite the server's stack.

Mutt before 1.14.4 and NeoMutt before 2020-06-19 have a STARTTLS buffering issue that affects IMAP, SMTP, and POP3. When a server sends a "begin TLS" response, the client reads additional data (e.g., from a man-in-the-middle attacker) and evaluates it in a TLS context, aka "response injection."