CVE-2022-35991: TensorFlow is an open source platform for machine learning. When `TensorListScatter` and `TensorListScatterV2` receive a

Summary: TensorFlow, an open-source machine learning platform, has a vulnerability where two functions (`TensorListScatter` and `TensorListScatterV2`) crash when given certain types of input, allowing attackers to cause a denial of service attack (making the system unavailable). The issue has been fixed and will be released in upcoming versions.

Solution: The issue has been patched in GitHub commit bb03fdf4aae944ab2e4b35c7daa051068a8b7f61. The fix will be included in TensorFlow 2.10.0, and will also be backported to TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2.


CVE-2022-36026: TensorFlow is an open source platform for machine learning. If `QuantizeAndDequantizeV3` is given a nonscalar `num_bits`

Summary: TensorFlow, an open source platform for machine learning, has a vulnerability in its `QuantizeAndDequantizeV3` function where passing a nonscalar `num_bits` input tensor (a multi-dimensional array instead of a single value) causes the program to crash, which can be exploited for a denial of service attack (making a service unavailable by overwhelming or crashing it). The issue affects multiple TensorFlow versions.

Solution: The issue has been patched in GitHub commit f3f9cb38ecfe5a8a703f2c4a8fead434ef291713. The fix will be included in TensorFlow 2.10.0 and will be backported to TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2. No workarounds are available; users should update to these patched versions.


CVE-2022-36019: TensorFlow is an open source platform for machine learning. If `FakeQuantWithMinMaxVarsPerChannel` is given `min` or `ma

Summary: TensorFlow (an open source machine learning platform) has a vulnerability where a specific function called `FakeQuantWithMinMaxVarsPerChannel` crashes when given certain types of input data, allowing attackers to cause a denial of service attack (making the system stop working). The developers have fixed the bug in their code.

Solution: The fix is included in TensorFlow 2.10.0, and will also be patched in earlier versions 2.9.1, 2.8.1, and 2.7.2. Users should update to one of these versions or later.


CVE-2022-36018: TensorFlow is an open source platform for machine learning. If `RaggedTensorToVariant` is given a `rt_nested_splits` lis

Summary: TensorFlow, an open source platform for machine learning, has a vulnerability where a function called `RaggedTensorToVariant` can crash if it receives incorrectly formatted input (tensors with ranks other than one). An attacker could use this crash to launch a denial of service attack (making the system unavailable).

Solution: The issue has been patched in GitHub commit 88f93dfe691563baa4ae1e80ccde2d5c7a143821. The fix is included in TensorFlow 2.10.0 and will also be backported to (applied to earlier versions of) TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2.


CVE-2022-35990: TensorFlow is an open source platform for machine learning. When `tf.quantization.fake_quant_with_min_max_vars_per_chann

Summary: A vulnerability in TensorFlow (an open source platform for machine learning) allows attackers to crash the system by sending specially formatted inputs to a specific function called `tf.quantization.fake_quant_with_min_max_vars_per_channel_gradient`, causing a denial of service attack (where a system becomes unavailable). The issue occurs when the function receives input parameters with the wrong structure (rank other than 1).

Solution: The vulnerability was patched in GitHub commit f3cf67ac5705f4f04721d15e485e192bb319feed. The fix is included in TensorFlow 2.10.0 and will also be backported (applied to older versions still receiving updates) to TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2. There are no known workarounds for this issue.


CVE-2022-35989: TensorFlow is an open source platform for machine learning. When `MaxPool` receives a window size input array `ksize` wi

Summary: TensorFlow (an open source platform for machine learning) has a vulnerability in its MaxPool function, which crashes when given a window size array with dimensions larger than the input data, allowing attackers to cause a denial of service attack (making the system unavailable). The issue has been patched and will be fixed in upcoming versions.

Solution: The fix is included in TensorFlow 2.10.0 and will be cherrypicked into TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2. Users should update to one of these patched versions. No workarounds are available.


CVE-2022-35988: TensorFlow is an open source platform for machine learning. When `tf.linalg.matrix_rank` receives an empty input `a`, th

Summary: TensorFlow (an open source platform for machine learning) has a vulnerability in its `tf.linalg.matrix_rank` function, which crashes when given an empty input. An attacker could exploit this crash to cause a denial of service attack (making the system unavailable by overwhelming it with requests or triggering failures).

Solution: The issue has been patched in GitHub commit c55b476aa0e0bd4ee99d0f3ad18d9d706cd1260a. The fix will be included in TensorFlow 2.10.0 and will be backported to TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2.


CVE-2022-35987: TensorFlow is an open source platform for machine learning. `DenseBincount` assumes its input tensor `weights` to either

Summary: TensorFlow, an open source platform for machine learning, has a vulnerability in its `DenseBincount` function where it doesn't properly check if the `weights` input tensor (a data structure holding numbers) has the correct shape, allowing attackers to crash the program through a denial of service attack (making a system unavailable by overwhelming it).

Solution: The issue has been patched in GitHub commit bf4c14353c2328636a18bfad1e151052c81d5f43 and will be included in TensorFlow 2.10.0. The fix will also be included in earlier versions: TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2.


CVE-2022-35986: TensorFlow is an open source platform for machine learning. If `RaggedBincount` is given an empty input tensor `splits`,

Summary: TensorFlow (an open source machine learning platform) has a bug where the `RaggedBincount` function crashes when given an empty input tensor called `splits`, which can be exploited to launch a denial of service attack (making a service unavailable by overwhelming it). The vulnerability affects multiple versions of the software.

Solution: Update to TensorFlow 2.10.0, or apply the patch from GitHub commit 7a4591fd4f065f4fa903593bc39b2f79530a74b8. If you cannot update to 2.10.0 yet, cherrypicked fixes are also available in TensorFlow 2.9.1, 2.8.1, and 2.7.2. There are no known workarounds for this issue.


CVE-2022-35985: TensorFlow is an open source platform for machine learning. If `LRNGrad` is given an `output_image` input tensor that is

Summary: TensorFlow (an open source platform for machine learning) has a vulnerability in its `LRNGrad` function where passing an incorrectly formatted input tensor (one that is not 4-dimensional) causes the program to crash, allowing attackers to trigger a denial of service attack (making the system unavailable).

Solution: The issue was patched in GitHub commit bd90b3efab4ec958b228cd7cfe9125be1c0cf255. The fix is included in TensorFlow 2.10.0 and will be backported (applied to older supported versions) in TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2.


CVE-2022-35984: TensorFlow is an open source platform for machine learning. `ParameterizedTruncatedNormal` assumes `shape` is of type `i

Summary: TensorFlow (an open source machine learning platform) has a bug in the `ParameterizedTruncatedNormal` function where it only accepts one data type (`int32`) for the `shape` parameter, but crashes when given the correct type (`int64`), which could allow an attacker to cause a denial of service (making the software unavailable).

Solution: The issue was patched in GitHub commit 72180be03447a10810edca700cbc9af690dfeb51. The fix will be included in TensorFlow 2.10.0 and will also be backported (added to older versions still receiving updates) to TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2. There are no known workarounds for this issue.


CVE-2022-35983: TensorFlow is an open source platform for machine learning. If `Save` or `SaveSlices` is run over tensors of an unsuppor

Summary: TensorFlow (an open source machine learning platform) has a vulnerability where running certain save operations on data types (formats for storing numbers) that aren't supported causes the program to crash, which could be used for a denial of service attack (making a service unavailable by overwhelming it). The vulnerability affects multiple versions of TensorFlow.

Solution: The fix is included in TensorFlow 2.10.0 and will be backported (added to older versions) in TensorFlow 2.9.1, 2.8.1, and 2.7.2. Users should update to one of these patched versions.


CVE-2022-35982: TensorFlow is an open source platform for machine learning. If `SparseBincount` is given inputs for `indices`, `values`,

Summary: TensorFlow, an open source machine learning platform, has a vulnerability in the `SparseBincount` function where invalid sparse tensor (a compressed way of storing data with mostly empty values) inputs can crash the program, potentially allowing attackers to cause a denial of service attack (making the system unavailable). The issue has been patched and will be fixed in upcoming versions of TensorFlow.

Solution: The fix is included in TensorFlow 2.10.0 and has been cherrypicked (backported) to TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2. Users should update to one of these patched versions.


CVE-2022-35981: TensorFlow is an open source platform for machine learning. `FractionalMaxPoolGrad` validates its inputs with `CHECK` fa

Summary: TensorFlow, an open source machine learning platform, has a vulnerability in its `FractionalMaxPoolGrad` function (a component that processes pooling operations) where it uses CHECK failures instead of returning errors to validate inputs. If someone sends incorrectly sized inputs to this function, they can trigger a denial of service attack (making the system crash or become unresponsive).

Solution: Update TensorFlow to version 2.10.0 or apply the patch from GitHub commit 8741e57d163a079db05a7107a7609af70931def4. The fix is also being included in TensorFlow 2.9.1, 2.8.1, and 2.7.2.


CVE-2022-35979: TensorFlow is an open source platform for machine learning. If `QuantizedRelu` or `QuantizedRelu6` are given nonscalar i

Summary: TensorFlow (an open-source machine learning platform) has a vulnerability where two functions called `QuantizedRelu` and `QuantizedRelu6` crash when given certain types of incorrect inputs for their `min_features` or `max_features` parameters, which attackers could exploit to cause a denial of service attack (making the system unavailable).

Solution: The issue has been patched in GitHub commit 49b3824d83af706df0ad07e4e677d88659756d89. The fix is included in TensorFlow 2.10.0 and will be backported (applied to older versions still being supported) to TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2. No workarounds are available, so users must update to a patched version.


CVE-2022-35974: TensorFlow is an open source platform for machine learning. If `QuantizeDownAndShrinkRange` is given nonscalar inputs fo

Summary: TensorFlow (an open source machine learning platform) has a bug where a function called `QuantizeDownAndShrinkRange` crashes if it receives nonscalar inputs (arrays or objects with multiple values instead of single values) for certain parameters, allowing attackers to cause a denial of service attack (making the system unavailable).

Solution: The issue has been patched in GitHub commit 73ad1815ebcfeb7c051f9c2f7ab5024380ca8613. The fix will be included in TensorFlow 2.10.0, and will also be backported to TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2.


CVE-2022-35973: TensorFlow is an open source platform for machine learning. If `QuantizedMatMul` is given nonscalar input for: `min_a`,

Summary: TensorFlow, an open source machine learning platform, has a vulnerability in its `QuantizedMatMul` function that crashes when given certain types of improper input (nonscalar values for min/max parameters), allowing attackers to trigger a denial of service attack (making the system unavailable). The issue has been fixed and will be released in updated versions of TensorFlow.

Solution: The fix is available in GitHub commit aca766ac7693bf29ed0df55ad6bfcc78f35e7f48 and will be included in TensorFlow 2.10.0. Users of TensorFlow 2.9.1, 2.8.1, and 2.7.2 should update to the patched versions of those releases (2.9.1, 2.8.1, and 2.7.2 respectively), as the fix will be cherry-picked into these supported versions.


CVE-2022-35972: TensorFlow is an open source platform for machine learning. If `QuantizedBiasAdd` is given `min_input`, `max_input`, `mi

Summary: TensorFlow, an open source machine learning platform, has a vulnerability in its `QuantizedBiasAdd` function that crashes when given certain tensor inputs of nonzero rank (multi-dimensional arrays), allowing attackers to launch a denial of service attack (making the system unavailable). The developers have identified and patched the issue.

Solution: The fix is included in TensorFlow 2.10.0 and will also be backported to TensorFlow 2.9.1, 2.8.1, and 2.7.2. Users should update to one of these patched versions.


CVE-2022-35971: TensorFlow is an open source platform for machine learning. If `FakeQuantWithMinMaxVars` is given `min` or `max` tensors

Summary: TensorFlow, an open source machine learning platform, has a vulnerability in the `FakeQuantWithMinMaxVars` function where providing certain types of input tensors (multidimensional arrays of numbers) causes the program to crash, enabling a denial of service attack (making a system unavailable to users). The vulnerability has been identified and fixed in the codebase.

Solution: The fix is included in TensorFlow 2.10.0. Users of earlier versions should update to TensorFlow 2.9.1, TensorFlow 2.8.1, or TensorFlow 2.7.2, which will receive the patch through a cherry-pick (backporting the fix to older versions). No workarounds are available.


CVE-2022-35970: TensorFlow is an open source platform for machine learning. If `QuantizedInstanceNorm` is given `x_min` or `x_max` tenso

Summary: TensorFlow (an open source platform for machine learning) has a bug in the `QuantizedInstanceNorm` function where passing certain tensor inputs (`x_min` or `x_max` with nonzero rank, which are multi-dimensional arrays of numerical data) causes a segfault (a crash from accessing invalid memory), allowing attackers to trigger a denial of service attack (making the system unavailable). The vulnerability was fixed and will be released in TensorFlow 2.10.0, with backported patches for earlier versions.

Solution: Update to TensorFlow 2.10.0 or apply the cherrypick commits to TensorFlow 2.9.1, 2.8.1, or 2.7.2. The fix is available in GitHub commit 785d67a78a1d533759fcd2f5e8d6ef778de849e0. No workarounds exist for this issue.


CVE-2022-35969: TensorFlow is an open source platform for machine learning. The implementation of `Conv2DBackpropInput` requires `input_

Summary: TensorFlow (an open-source machine learning platform) has a bug in the `Conv2DBackpropInput` function where it crashes if the `input_sizes` parameter is not 4-dimensional, allowing attackers to cause a denial of service (making the system unavailable). The issue has been fixed and will be released in upcoming versions.

Solution: The fix is included in TensorFlow 2.10.0. For users on older versions, the patch will be available in TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2. Update to one of these versions or later.


CVE-2022-35968: TensorFlow is an open source platform for machine learning. The implementation of `AvgPoolGrad` does not fully validate

Summary: TensorFlow, an open source machine learning platform, has a bug in the `AvgPoolGrad` function where it doesn't properly check the input parameter `orig_input_shape`. This incomplete validation causes a CHECK failure (a crash that stops the program), which attackers can exploit to perform a denial of service attack (making the system unavailable to legitimate users).

Solution: The issue has been patched in GitHub commit 3a6ac52664c6c095aa2b114e742b0aa17fdce78f. The fix will be included in TensorFlow 2.10.0, and will be backported (added to older versions still being supported) in TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2.


CVE-2022-35967: TensorFlow is an open source platform for machine learning. If `QuantizedAdd` is given `min_input` or `max_input` tensor

Summary: TensorFlow, an open source machine learning platform, has a vulnerability in its `QuantizedAdd` function (a tool for adding quantized numbers, which are rounded values used to save memory). If this function receives certain tensor inputs of nonzero rank (multi-dimensional arrays), it crashes the program, which can be exploited to cause a denial of service attack (making the system unavailable to legitimate users).

Solution: The issue is patched in GitHub commit 49b3824d83af706df0ad07e4e677d88659756d89. The fix will be included in TensorFlow 2.10.0 and will be backported (applied to older supported versions) as TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2.


CVE-2022-35966: TensorFlow is an open source platform for machine learning. If `QuantizedAvgPool` is given `min_input` or `max_input` te

Summary: A bug in TensorFlow (an open source platform for machine learning) called `QuantizedAvgPool` can crash when given certain types of inputs, allowing attackers to launch a denial of service attack (making a system unavailable). The issue has been fixed and will be released in upcoming versions of the software.

Solution: The fix is available in GitHub commit 7cdf9d4d2083b739ec81cfdace546b0c99f50622. The patch will be included in TensorFlow 2.10.0 and will also be applied to TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2.


CVE-2022-35965: TensorFlow is an open source platform for machine learning. If `LowerBound` or `UpperBound` is given an empty`sorted_inp

Summary: TensorFlow (an open source platform for machine learning) has a bug where the `LowerBound` or `UpperBound` functions crash if given an empty input list, causing a nullptr dereference (trying to access memory that doesn't exist). This crash can be exploited to launch a denial of service attack (making the system unavailable to legitimate users).

Solution: The issue was patched in GitHub commit bce3717eaef4f769019fd18e990464ca4a2efeea. The fix will be included in TensorFlow 2.10.0 and will also be back-ported (applied retroactively) to TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2.


CVE-2022-35964: TensorFlow is an open source platform for machine learning. The implementation of `BlockLSTMGradV2` does not fully valid

Summary: TensorFlow (an open source platform for machine learning) has a bug in the `BlockLSTMGradV2` function that doesn't properly check its inputs, allowing attackers to crash the system with a denial of service attack (causing the program to stop working). The vulnerability affects multiple versions of TensorFlow.

Solution: The issue has been patched in GitHub commit 2a458fc4866505be27c62f81474ecb2b870498fa. The fix will be included in TensorFlow 2.10.0 and will be back-ported to TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2. There are no known workarounds.


CVE-2022-35963: TensorFlow is an open source platform for machine learning. The implementation of `FractionalAvgPoolGrad` does not fully

Summary: A bug in TensorFlow (an open source machine learning platform) within a function called `FractionalAvgPoolGrad` doesn't properly check its input data, causing an overflow (when a number becomes too large for the program to handle) that crashes the program and can be exploited to launch a denial of service attack (making a service unavailable to users).

Solution: The issue has been patched in GitHub commit 03a659d7be9a1154fdf5eeac221e5950fec07dad. The fix will be included in TensorFlow 2.10.0 and will also be applied to TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2.


CVE-2022-35960: TensorFlow is an open source platform for machine learning. In `core/kernels/list_kernels.cc's TensorListReserve`, `num_

Summary: TensorFlow (an open source machine learning platform) has a bug in its TensorListReserve function where it assumes `num_elements` is a tensor with only one value, but crashes if given multiple values. This causes the function to fail when users try to use `tf.raw_ops.TensorListReserve` with improperly sized input.

Solution: The issue has been patched in GitHub commit b5f6fbfba76576202b72119897561e3bd4f179c7. The fix is included in TensorFlow 2.10.0, and will also be released in TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2.


CVE-2022-35959: TensorFlow is an open source platform for machine learning. The implementation of `AvgPool3DGradOp` does not fully valid

Summary: TensorFlow (an open source machine learning platform) has a bug in `AvgPool3DGradOp` (a function that calculates gradients for 3D average pooling operations) where it doesn't properly check the `orig_input_shape` input value. This causes an overflow (when a number gets too large for its container) that crashes the system with a CHECK failure, allowing attackers to perform a denial of service attack (making the system unavailable).

Solution: The issue was patched in GitHub commit 9178ac9d6389bdc54638ab913ea0e419234d14eb. The fix is included in TensorFlow 2.10.0 and will be backported (adapted for older versions) to TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2.


CVE-2022-35952: TensorFlow is an open source platform for machine learning. The `UnbatchGradOp` function takes an argument `id` that is

Summary: TensorFlow, a machine learning platform, has a vulnerability in the `UnbatchGradOp` function (a component that processes gradient calculations) where it doesn't properly validate its inputs. If given a non-scalar `id` (a single value instead of what's expected) or an incorrectly sized `batch_index` (a list of indices), the function crashes the program. There are no known workarounds for this issue.

Solution: The issue was patched in GitHub commit 5f945fc6409a3c1e90d6970c9292f805f6e6ddf2. The fix will be included in TensorFlow 2.10.0 and will also be backported to TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2.


CVE-2022-35941: TensorFlow is an open source platform for machine learning. The `AvgPoolOp` function takes an argument `ksize` that must

Summary: TensorFlow's `AvgPoolOp` function has a bug where it doesn't check if the `ksize` argument (a parameter that controls pooling window size) is positive, allowing negative values to crash the program. The issue has been patched and will be included in upcoming TensorFlow releases.

Solution: Update to TensorFlow 2.10.0 or apply the patch from GitHub commit 3a6ac52664c6c095aa2b114e742b0aa17fdce78f. If you are using TensorFlow 2.9.1, 2.8.1, or 2.7.2, updates including the fix will be released for these versions as well.


CVE-2022-35940: TensorFlow is an open source platform for machine learning. The `RaggedRangOp` function takes an argument `limits` that

Summary: TensorFlow's `RaggedRangOp` function has a bug where passing a very large float value to the `limits` argument causes it to overflow when converted to an `int64` (a 64-bit integer type), crashing the entire program with an abort signal. This vulnerability affects multiple versions of TensorFlow and has no known workaround.

Solution: The issue has been patched in GitHub commit 37cefa91bee4eace55715eeef43720b958a01192. The fix will be included in TensorFlow 2.10.0, and will also be applied to TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2.


CVE-2022-35939: TensorFlow is an open source platform for machine learning. The `ScatterNd` function takes an input argument that determ

Summary: TensorFlow's `ScatterNd` function (a tool that places values into specific positions of an output array) has a bug where invalid input indices can write data to the wrong location or crash the program. The vulnerability affects multiple versions of TensorFlow.

Solution: The issue is patched in GitHub commit b4d4b4cb019bd7240a52daa4ba61e3cc814f0384. The fix will be included in TensorFlow 2.10.0 and will be backported (applied to older versions still being supported) to TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2. The source notes there are no known workarounds.


CVE-2022-35938: TensorFlow is an open source platform for machine learning. The `GatherNd` function takes arguments that determine the s

Summary: A bug in TensorFlow (an open source platform for machine learning) exists in the `GatherNd` function, which retrieves values from arrays using index arrays. When input sizes are greater than or equal to output sizes, the function tries to read memory outside its allowed bounds (out-of-bounds memory read), causing errors or system crashes. The vulnerability affects multiple recent versions of TensorFlow.

Solution: The fix has been patched in GitHub commit 4142e47e9e31db481781b955ed3ff807a781b494 and will be included in TensorFlow 2.10.0. The fix will also be backported (applied to older versions still being supported) to TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2. Users should update to these patched versions.


CVE-2022-35937: TensorFlow is an open source platform for machine learning. The `GatherNd` function takes arguments that determine the s

Summary: TensorFlow's `GatherNd` function (a tool that retrieves values from arrays based on index locations) has a vulnerability where it can read memory it shouldn't access if certain input sizes are too large. This happens because the function doesn't properly check if inputs exceed the expected output sizes, potentially exposing sensitive data or crashing the system.

Solution: The fix will be included in TensorFlow 2.10.0. Patched versions will also be available in TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2. The source notes there are no known workarounds for this issue.


CVE-2022-35935: TensorFlow is an open source platform for machine learning. The implementation of SobolSampleOp is vulnerable to a denia

Summary: TensorFlow (an open source platform for machine learning) has a bug in SobolSampleOp that crashes the program when it receives unexpected input types, because the code assumes certain inputs will be scalars (single values rather than arrays). This denial of service vulnerability has been fixed and will be released in upcoming versions.

Solution: The fix is included in TensorFlow 2.10.0. The patch will also be applied to TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, which are still supported. Users should update to one of these patched versions. No workarounds are available until an update is applied.


CVE-2022-35934: TensorFlow is an open source platform for machine learning. The implementation of tf.reshape op in TensorFlow is vulnera

Summary: TensorFlow's tf.reshape operation (a function that changes a tensor's shape without altering its data) has a vulnerability that allows attackers to crash the program by causing an integer overflow (when a number exceeds the maximum value a system can store), triggering a denial of service attack (making the service unavailable). The issue affects multiple versions of TensorFlow and has been patched.

Solution: Update to TensorFlow 2.10.0, or apply the cherrypick to versions 2.9.1, 2.8.1, or 2.7.2 (the patched versions for users on older supported releases). The fix is included in GitHub commit 61f0f9b94df8c0411f0ad0ecc2fec2d3f3c33555. There are no known workarounds for this issue.


Malicious Python Packages and Code Execution via pip download

Summary: Running pip download (a Python command that downloads packages without installing them) can execute malicious code on your computer due to a design flaw, even though many people assume only pip install poses a security risk. This vulnerability allows attackers to run arbitrary code (commands of their choice) simply by downloading a compromised package.

Solution: N/A -- no mitigation discussed in source.


Machine Learning Attack Series: Backdooring Pickle Files

Summary: Pickle files (Python's serialization format for saving objects) can be backdoored because they execute code through opcodes (instructions that control a virtual machine). Attackers can inject malicious commands into pickle files using tools like fickling, and when someone loads the file, the hidden code runs without interrupting the program's normal function. This is especially dangerous in shared environments like Google Colab, where an infected pickle file could give attackers access to a user's connected Google Drive.

Solution: The source mentions fickling, a tool by Trail of Bits that can both inject code into pickle files and check them for backdoors using two built-in safety features: '--check-safety' (which checks for malicious opcodes) and '--trace' (which shows the various opcodes). The source also recommends: "only ever open pickle files that you created or trust."

Trail of Bits
StyleGAN2-ADA
Google Colab
Husky AI


CVE-2022-35918: Streamlit is a data oriented application development framework for python. Users hosting Streamlit app(s) that use custo

Summary: Streamlit, a Python framework for building data applications, has a directory traversal vulnerability (a type of attack where an attacker uses specially crafted file paths to access files they shouldn't be able to reach) in versions before 1.11.1. An attacker could trick the Streamlit server into reading and returning sensitive files from the server's file system, such as logs or other confidential information.

Solution: Upgrade to Streamlit version 1.11.1 or later. The source explicitly states, 'This issue has been resolved in version 1.11.1. Users are advised to upgrade.' No workarounds are available.


CVE-2020-25459: An issue was discovered in function sync_tree in hetero_decision_tree_guest.py in WeBank FATE (Federated AI Technology E

Summary: CVE-2020-25459 is a vulnerability in WeBank FATE (Federated AI Technology Enabler, a system for training machine learning models across multiple parties) versions 0.1 through 1.4.2 that allows attackers to read sensitive information during the training process. The issue exists in a function called sync_tree in the hetero_decision_tree_guest.py file, which means attackers could access private data while the model is being trained.


CVE-2022-29216: TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, TensorFlow

Summary: TensorFlow's `saved_model_cli` tool (a utility for working with saved machine learning models) had a code injection vulnerability in versions before 2.9.0, 2.8.1, 2.7.2, and 2.6.4, which could allow an attacker to open a reverse shell (a backdoor connection giving remote control of a system). The vulnerability existed because the tool used `eval` (a function that executes text as code) on user input for compatibility with older test cases, but since the tool requires manual operation, the practical risk was limited.

Solution: Update TensorFlow to version 2.9.0, 2.8.1, 2.7.2, or 2.6.4 or later. The maintainers removed the `safe=False` argument, so all parsing is now done without calling `eval`.


CVE-2022-29213: TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the `tf.co

Summary: TensorFlow, an open source platform for machine learning, had a bug in two signal processing functions (`tf.compat.v1.signal.rfft2d` and `tf.compat.v1.signal.rfft3d`) where missing input validation (checking that data meets expected requirements before processing) could cause the software to crash under certain conditions. The bug was fixed in versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4.

Solution: Update TensorFlow to one of the patched versions: 2.9.0, 2.8.1, 2.7.2, or 2.6.4.


CVE-2022-29212: TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, certain TF

Summary: TensorFlow, an open source machine learning platform, had a bug in versions before 2.9.0, 2.8.1, 2.7.2, and 2.6.4 where certain converted models would crash when loaded. The problem occurred because the code assumed that quantization (a technique to compress model size by reducing numerical precision) would always use scaling factors smaller than 1, but sometimes the scale was larger, causing the program to stop unexpectedly.

Solution: Update to TensorFlow versions 2.9.0, 2.8.1, 2.7.2, or 2.6.4, which contain a patch for this issue.


CVE-2022-29211: TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the implem

Summary: TensorFlow, an open source platform for machine learning, has a vulnerability in the `tf.histogram_fixed_width` function where it crashes if the input data contains NaN (Not a Number, a special floating point value representing undefined results). The crash happens because the code tries to convert NaN to an integer without checking for it first, and this bug only affects the CPU version of TensorFlow.


CVE-2022-29210: TensorFlow is an open source platform for machine learning. In version 2.8.0, the `TensorKey` hash function used total e

Summary: TensorFlow version 2.8.0 had a bug in the `TensorKey` hash function (a function that converts data into a fixed-size code for quick lookups), where it incorrectly used `AllocatedBytes()` (an estimate of memory used by a tensor, including referenced data like strings) to access the actual tensor data bytes. This caused crashes because `AllocatedBytes()` doesn't represent the real contiguous memory buffer, and certain data types like `tstring` contain pointers rather than actual values.

Solution: This issue is patched in TensorFlow versions 2.9.0 and 2.8.1.


CVE-2022-29209: TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the macros

Summary: TensorFlow, an open source machine learning platform, had a bug in versions before 2.9.0, 2.8.1, 2.7.2, and 2.6.4 where assertion macros (special code blocks that check if conditions are true) incorrectly compared different data types, specifically `size_t` and `int` values (two different ways to store whole numbers). This type confusion could cause assertions to trigger incorrectly due to how the computer converts between these different number types.

Solution: Update TensorFlow to version 2.9.0, 2.8.1, 2.7.2, or 2.6.4 or later, as these versions contain a patch for this issue.


CVE-2022-29208: TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the implem

Summary: TensorFlow, an open source platform for machine learning, has a vulnerability in the `tf.raw_ops.EditDistance` function where incomplete validation allows users to pass negative values that cause a segmentation fault (a program crash from accessing invalid memory). An attacker could exploit this by crafting input that produces negative array indices, allowing writes before the intended array location and potentially crashing the system.


CVE-2022-29206: TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the implem

Summary: CVE-2022-29206 is a bug in TensorFlow (an open source machine learning platform) where a specific function called `tf.raw_ops.SparseTensorDenseAdd` doesn't properly check its input arguments, causing a nullptr (a reference pointing to nothing) to be accessed during execution, which leads to undefined behavior. This vulnerability affects TensorFlow versions before 2.9.0, 2.8.1, 2.7.2, and 2.6.4.

Solution: Update TensorFlow to versions 2.9.0, 2.8.1, 2.7.2, or 2.6.4 or later, which contain a patch for this issue.


CVE-2022-29205: TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, there is a

Summary: TensorFlow (an open-source machine learning platform) has a bug in older versions where calling certain compatibility functions with unsupported data types causes the program to crash. When the code tries to process a missing function, it attempts to use a null pointer (a reference to nothing in memory), which causes a segmentation fault (a type of crash where the program accesses memory it shouldn't).

Solution: Update to TensorFlow version 2.9.0, 2.8.1, 2.7.2, or 2.6.4 or later, which contain a patch for this issue.


CVE-2022-29204: TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the implem

Summary: TensorFlow, an open source platform for machine learning, has a vulnerability in one of its operations called `tf.raw_ops.UnsortedSegmentJoin` where it doesn't properly check its inputs before using them. If someone provides a negative number where a positive one is expected, it causes the program to crash with an assertion failure, which is a type of denial of service attack (making software unavailable by crashing it).


CVE-2022-29203: TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the implem

Summary: CVE-2022-29203 is a vulnerability in TensorFlow (an open source platform for machine learning) where a function called `tf.raw_ops.SpaceToBatchND` has an integer overflow bug (a situation where a calculation produces a number too large for the system to handle). This overflow causes a denial of service (making the system crash or become unavailable) when the buggy code tries to allocate memory for output data.

Solution: Update TensorFlow to version 2.9.0, 2.8.1, 2.7.2, or 2.6.4, which contain patches for this issue.


CVE-2022-29202: TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the implem

Summary: A vulnerability in TensorFlow (an open source platform for machine learning) versions prior to 2.9.0, 2.8.1, 2.7.2, and 2.6.4 allows attackers to cause a denial of service (making a system unavailable by consuming all available memory) by exploiting the `tf.ragged.constant` function, which does not properly check its input arguments. The vulnerability exists because of improper input validation (checking that data meets expected requirements before using it).

Solution: Update TensorFlow to version 2.9.0, 2.8.1, 2.7.2, or 2.6.4 or later. The source states: 'Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.'


CVE-2022-29201: TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the implem

Summary: TensorFlow, an open source machine learning platform, had a vulnerability in its `tf.raw_ops.QuantizedConv2D` function (a tool for processing images with reduced precision) before versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 where it did not properly check input arguments, causing references to point to nullptr (an invalid memory location). This flaw was fixed in the mentioned versions.


CVE-2022-29207: TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, multiple T

Summary: TensorFlow (an open source platform for machine learning) has a vulnerability in versions before 2.9.0, 2.8.1, 2.7.2, and 2.6.4 where certain operations fail when given an invalid resource handle (a reference to data or tools the program needs). In eager mode (where TensorFlow executes code immediately rather than preparing a plan first), this can cause a reference to point to a null pointer (a memory location that doesn't exist), leading to undefined behavior and potential crashes or errors. Graph mode had safeguards that prevented this issue.


CVE-2022-29200: TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the implem

Summary: TensorFlow (an open-source machine learning platform) has a bug in the `tf.raw_ops.LSTMBlockCell` function where it doesn't properly check that input arguments have the correct structure. An attacker can exploit this to cause a denial of service attack (crashing the program), because the code fails when trying to access elements inside incorrectly-shaped inputs.

Solution: Update TensorFlow to version 2.9.0, 2.8.1, 2.7.2, or 2.6.4 or later, which contain a patch for this issue.


CVE-2022-29199: TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the implem

Summary: TensorFlow (an open source machine learning platform) had a bug in the `tf.raw_ops.LoadAndRemapMatrix` function that didn't properly check its input arguments, specifically whether the `initializing_values` parameter was valid. This missing validation could cause the program to crash (denial of service, a type of attack that makes a service unavailable), even though the attacker doesn't gain control of the system.

Solution: Update TensorFlow to version 2.9.0, 2.8.1, 2.7.2, or 2.6.4 or later, which contain patches for this issue.


CVE-2022-29198: TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the implem

Summary: TensorFlow, an open source machine learning platform, has a vulnerability in a function called `tf.raw_ops.SparseTensorToCSRSparseMatrix` that doesn't properly check its inputs before processing them. This missing validation allows attackers to cause a denial of service attack (making the system crash or become unavailable) by sending specially crafted data that violates the expected format for sparse tensors (data structures that store mostly empty values efficiently).


CVE-2022-29197: TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the implem

Summary: A bug in TensorFlow (an open source machine learning platform) versions before 2.9.0, 2.8.1, 2.7.2, and 2.6.4 fails to validate input arguments to the `tf.raw_ops.UnsortedSegmentJoin` function, allowing attackers to trigger a denial of service attack (making the system crash or become unavailable). The vulnerability stems from the code assuming `num_segments` is a scalar (a single value) without checking this assumption first.


CVE-2022-29196: TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the implem

Summary: TensorFlow, an open source machine learning platform, has a vulnerability in its `tf.raw_ops.Conv3DBackpropFilterV2` function (a tool for training neural networks) that fails to properly check its input arguments before processing them. This missing validation allows attackers to crash the program with a denial of service attack (making it unavailable to legitimate users).

Solution: Update to TensorFlow versions 2.9.0, 2.8.1, 2.7.2, or 2.6.4, which contain patches that fix this input validation issue.


CVE-2022-29195: TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the implem

Summary: TensorFlow (an open source platform for machine learning) versions before 2.9.0, 2.8.1, 2.7.2, and 2.6.4 have a bug in the `tf.raw_ops.StagePeek` function that fails to check whether the `index` input is a scalar (a single number), allowing attackers to crash the system. This is a denial of service attack (making a service unavailable by overwhelming or breaking it).


CVE-2022-29193: TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the implem

Summary: TensorFlow, an open source platform for machine learning, had a vulnerability in the `tf.raw_ops.TensorSummaryV2` function that failed to properly validate (check the correctness of) input arguments before using them. This flaw could be exploited to cause a denial of service attack (making the system crash or become unavailable) by triggering a CHECK-failure (a forced program halt when an expected condition is not met).


CVE-2022-29194: TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the implem

Summary: TensorFlow, an open source machine learning platform, had a vulnerability in its `tf.raw_ops.DeleteSessionTensor` function (a specific operation within TensorFlow) that failed to properly check its input arguments before using them. This flaw could be exploited to cause a denial of service attack (making a system crash or become unavailable by sending specially crafted requests).


CVE-2022-29192: TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the implem

Summary: TensorFlow, an open source machine learning platform, had a vulnerability in the `tf.raw_ops.QuantizeAndDequantizeV4Grad` function where it did not fully validate input arguments before processing them. This bug could crash the system (a denial of service attack, where an attacker makes a service unavailable) in versions before 2.9.0, 2.8.1, 2.7.2, and 2.6.4.

Solution: Update TensorFlow to one of the patched versions: 2.9.0, 2.8.1, 2.7.2, or 2.6.4. A patch is available at https://github.com/tensorflow/tensorflow/commit/098e7762d909bac47ce1dbabe6dfd06294cb9d58.


CVE-2022-29191: TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the implem

Summary: TensorFlow, an open source machine learning platform, had a vulnerability in its `tf.raw_ops.GetSessionTensor` function (a command for retrieving tensor data from a session) where it did not properly validate input arguments, allowing attackers to crash the system through a denial of service attack (making software unavailable by overwhelming or breaking it). The vulnerability was fixed in TensorFlow versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4.


CVE-2022-21426: Vulnerability in the Oracle Java SE, Oracle GraalVM Enterprise Edition product of Oracle Java SE (component: JAXP). Supp

Summary: A vulnerability in Oracle Java SE and Oracle GraalVM Enterprise Edition (a high-performance Java runtime) in the JAXP component (Java API for XML Processing, which handles XML data) allows an unauthenticated attacker to partially disable these systems over a network. The vulnerability affects specific versions of Java and can be exploited through untrusted code in web applications or through web services that supply data to the vulnerable APIs, with a severity rating of 5.3 out of 10.

Oracle Java SE
Oracle GraalVM Enterprise Edition


GPT-3 and Phishing Attacks

Summary: GPT-3 (a large language model that generates realistic human-like text) could be misused by attackers to create convincing phishing attacks (fraudulent messages designed to trick people into revealing sensitive information). The post discusses this threat and mentions that organizations can take countermeasures to protect themselves, though specific details are not provided in the excerpt.


CVE-2022-24770: `gradio` is an open source framework for building interactive machine learning models and demos. Prior to version 2.8.11

Summary: Gradio, a framework for building interactive machine learning demos, has a vulnerability in versions before 2.8.11 where its flagging feature (which saves data to CSV files) can be tricked into storing harmful commands in the file. If someone opens this CSV file in Excel or similar programs, those commands run automatically on their computer.

Solution: Update gradio to version 2.8.11 or later, which escapes saved CSV data with single quotes to prevent command execution. As a workaround, avoid opening CSV files generated by gradio with Excel or similar spreadsheet programs.


CVE-2022-0845: Code Injection in GitHub repository pytorchlightning/pytorch-lightning prior to 1.6.0.

Summary: CVE-2022-0845 is a code injection vulnerability (a flaw where an attacker can insert and execute malicious code) in PyTorch Lightning, a machine learning framework, affecting versions before 1.6.0. The vulnerability stems from improper control over code generation, allowing attackers to run arbitrary code through the affected software.

Solution: Update PyTorch Lightning to version 1.6.0 or later. A patch is available at https://github.com/pytorchlightning/pytorch-lightning/commit/8b7a12c52e52a06408e9231647839ddb4665e8ae


CVE-2022-0736: Insecure Temporary File in GitHub repository mlflow/mlflow prior to 1.23.1.

Summary: MLflow, a machine learning platform, had an insecure temporary file vulnerability (CWE-377, a weakness where temporary files are created without proper security protections) in versions before 1.23.1. This vulnerability could potentially allow attackers to access or modify sensitive data stored in temporary files.

Solution: Update MLflow to version 1.23.1 or later. A patch is available at https://github.com/mlflow/mlflow/commit/61984e6843d2e59235d82a580c529920cd8f3711.


CVE-2022-23595: Tensorflow is an Open Source Machine Learning Framework. When building an XLA compilation cache, if default settings are

Summary: TensorFlow (an open source machine learning framework) has a vulnerability where building an XLA compilation cache (a storage system that speeds up machine learning model compilation) with default settings causes a null pointer dereference (a crash that happens when code tries to use a memory location that doesn't exist). This occurs because the default configuration allows all devices, leaving a critical variable empty.

Solution: The fix will be included in TensorFlow 2.8.0. Patches will also be released in TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3.


CVE-2022-23594: Tensorflow is an Open Source Machine Learning Framework. The TFG dialect of TensorFlow (MLIR) makes several assumptions

Summary: TensorFlow (an open-source machine learning framework) has a vulnerability in its TFG dialect, which is part of MLIR (a compiler framework for optimizing code). An attacker can modify the SavedModel format (the way trained models are saved to disk) to break assumptions the system makes, which can crash the Python interpreter or cause heap OOB (out-of-bounds memory access, where code reads or writes memory it shouldn't).

Solution: N/A -- no mitigation discussed in source.


CVE-2022-23593: Tensorflow is an Open Source Machine Learning Framework. The `simplifyBroadcast` function in the MLIR-TFRT infrastructur

Summary: TensorFlow, an open-source machine learning framework, has a vulnerability in its `simplifyBroadcast` function (a part of the MLIR-TFRT infrastructure, which is the compiler and runtime system) that causes a segfault (a crash from accessing invalid memory) when given scalar shapes (data without dimensions), resulting in a denial of service (making the system unavailable). This affects only TensorFlow version 2.7.0.

Solution: The fix will be included in TensorFlow 2.8.0.


CVE-2022-23592: Tensorflow is an Open Source Machine Learning Framework. TensorFlow's type inference can cause a heap out of bounds read

Summary: TensorFlow (an open-source machine learning framework) has a vulnerability where type inference can read data outside the bounds of allocated memory (a heap out of bounds read). The bounds checking uses a DCHECK, which is disabled in production code, allowing an attacker to manipulate a variable so it accesses memory beyond what is available.


CVE-2022-23591: Tensorflow is an Open Source Machine Learning Framework. The `GraphDef` format in TensorFlow does not allow self recursi

Summary: TensorFlow (an open-source machine learning framework) has a vulnerability where the GraphDef format (TensorFlow's way of representing computation graphs) can accept self-recursive functions even though it shouldn't, causing a stack overflow (a crash from too much memory use) when the model runs because the system gets stuck trying to resolve the same function repeatedly.

Solution: The fix will be included in TensorFlow 2.8.0. The fix will also be backported to TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3.


CVE-2022-23590: Tensorflow is an Open Source Machine Learning Framework. A `GraphDef` from a TensorFlow `SavedModel` can be maliciously

Summary: TensorFlow (an open source machine learning framework) has a vulnerability where a maliciously altered GraphDef (a representation of a machine learning model's computation graph) from a SavedModel can crash a TensorFlow process by forcing extraction of a value from a StatusOr (a data structure that holds either a valid result or an error state). The issue affects both TensorFlow 2.7 and 2.8 versions.

Solution: The issue has been patched in TensorFlow 2.8.0 and TensorFlow 2.7.1. Users should upgrade to these versions or later.


CVE-2022-23589: Tensorflow is an Open Source Machine Learning Framework. Under certain scenarios, Grappler component of TensorFlow can t

Summary: TensorFlow, a machine learning framework, has a vulnerability (CVE-2022-23589) in its Grappler component (a graph optimization tool) that can cause a null pointer dereference (crash from accessing invalid memory) when processing maliciously altered SavedModel files (serialized machine learning models). The bug occurs in two places during optimization operations and can be triggered by missing required nodes in the computation graph.

Solution: The fix will be included in TensorFlow 2.8.0. The patch will also be backported to TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3.


CVE-2022-23588: Tensorflow is an Open Source Machine Learning Framework. A malicious user can cause a denial of service by altering a `S

Summary: A malicious user can crash TensorFlow (an open source machine learning framework) by modifying a SavedModel (a pre-trained model file) in a way that tricks the Grappler optimizer (a tool that improves model performance) into building a tensor with an invalid reference dtype (data type), causing the program to fail.

Solution: The fix will be included in TensorFlow 2.8.0. The fix will also be applied to TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3.


CVE-2022-23587: Tensorflow is an Open Source Machine Learning Framework. Under certain scenarios, Grappler component of TensorFlow is vu

Summary: TensorFlow, an open-source machine learning framework, has a vulnerability in its Grappler component (a tool that optimizes computational graphs) that causes an integer overflow (when a number becomes too large to store) during cost estimation for crop and resize operations. Since attackers can control the cropping parameters, they can trigger undefined behavior (unpredictable actions that may crash the system or cause other problems).

Solution: The fix will be included in TensorFlow 2.8.0. This commit will also be applied to TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these versions are still supported.


CVE-2022-23586: Tensorflow is an Open Source Machine Learning Framework. A malicious user can cause a denial of service by altering a `S

Summary: A vulnerability in TensorFlow (an open-source machine learning framework) allows an attacker to cause a denial of service by modifying a SavedModel (a packaged version of a trained model) in a way that triggers false assertions in the code and crashes the Python interpreter. This vulnerability affects multiple versions of TensorFlow.

Solution: Update to TensorFlow 2.8.0, or apply the fix through updates to TensorFlow 2.7.1, TensorFlow 2.6.3, or TensorFlow 2.5.3. Patches are available in the following commits: 3d89911481ba6ebe8c88c1c0b595412121e6c645 and dcc21c7bc972b10b6fb95c2fb0f4ab5a59680ec2.


CVE-2022-23585: Tensorflow is an Open Source Machine Learning Framework. When decoding PNG images TensorFlow can produce a memory leak i

Summary: TensorFlow, an open-source machine learning framework, has a memory leak (unused memory that is not freed) when decoding invalid PNG image files. The problem occurs because error-handling code exits the function early without properly freeing allocated buffers (chunks of memory that were set aside for use).


CVE-2022-23584: Tensorflow is an Open Source Machine Learning Framework. A malicious user can cause a use after free behavior when decod

Summary: TensorFlow (an open-source machine learning framework) has a vulnerability where a malicious user can trigger a use after free bug (accessing memory that has already been freed) when decoding PNG images. The problem occurs because after a memory cleanup function is called, the width and height values are left in an unpredictable state.

Solution: Update to TensorFlow 2.8.0 or apply patches to the following supported versions: TensorFlow 2.7.1, TensorFlow 2.6.3, or TensorFlow 2.5.3. These versions contain the fix for this vulnerability.


CVE-2022-23583: Tensorflow is an Open Source Machine Learning Framework. A malicious user can cause a denial of service by altering a `S

Summary: A vulnerability in TensorFlow (an open-source machine learning framework) allows a malicious user to cause a denial of service (making a service unavailable) by modifying a SavedModel (a format for storing trained models) so that binary operations receive corrupted data due to type confusion (using data as if it were a different type than it actually is). This type mismatch between expected and actual data types can cause the program to crash.

Solution: The fix will be included in TensorFlow 2.8.0. The fix will also be backported (adapted for older versions) to TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3.


CVE-2022-23582: Tensorflow is an Open Source Machine Learning Framework. A malicious user can cause a denial of service by altering a `S

Summary: A vulnerability in TensorFlow (an open-source machine learning framework) allows attackers to cause a denial of service (making a service unavailable) by modifying a SavedModel (a serialized TensorFlow model) so that the TensorByteSize function crashes. The problem occurs because the TensorShape constructor crashes when it encounters partial shapes (incomplete dimension information) or very large numbers, instead of gracefully handling them like PartialTensorShape does.

Solution: The fix will be included in TensorFlow 2.8.0. Additionally, the patch will be backported (applied to earlier versions still receiving support) to TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3.


CVE-2022-23581: Tensorflow is an Open Source Machine Learning Framework. The Grappler optimizer in TensorFlow can be used to cause a den

Summary: A vulnerability in TensorFlow (an open source machine learning framework) exists in the Grappler optimizer, which can be exploited to cause a denial of service (making a system unavailable by overloading it) by modifying a SavedModel file so that a function called IsSimplifiableReshape triggers CHECK failures (unexpected error conditions that crash the program).

Solution: The fix will be included in TensorFlow 2.8.0. Patches will also be cherry-picked (backported to earlier versions) for TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, which are still in the supported range.


CVE-2022-23580: Tensorflow is an Open Source Machine Learning Framework. During shape inference, TensorFlow can allocate a large vector

Summary: TensorFlow, an open-source machine learning framework, has a vulnerability in its shape inference process where it can allocate a large vector based on user-controlled input, potentially causing uncontrolled resource consumption (using excessive memory or CPU). This happens because the system doesn't properly validate the size of data requested by users.

Solution: The fix will be included in TensorFlow 2.8.0. The vulnerability is also being patched in TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, which are still in the supported range.


CVE-2022-23579: Tensorflow is an Open Source Machine Learning Framework. The Grappler optimizer in TensorFlow can be used to cause a den

Summary: TensorFlow (an open source machine learning framework) has a vulnerability in its Grappler optimizer (a tool that improves how machine learning models run) that allows attackers to cause a denial of service (making the system stop working) by modifying a SavedModel (a saved machine learning model) in a way that triggers crashes. This vulnerability affects multiple versions of TensorFlow.

Solution: The fix will be included in TensorFlow 2.8.0. TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3 will also receive the fix through a cherrypick (applying the same fix to older supported versions).


CVE-2022-23578: Tensorflow is an Open Source Machine Learning Framework. If a graph node is invalid, TensorFlow can leak memory in the i

Summary: TensorFlow (an open-source machine learning framework) has a memory leak bug in a function called `ImmutableExecutorState::Initialize`. When a graph node (a processing unit in a machine learning model) is invalid, the software sets a pointer (a reference to a location in memory) to null without freeing the memory it previously pointed to, causing that memory to be wasted and unavailable for other tasks.

Solution: The fix will be included in TensorFlow 2.8.0. The fix will also be backported (applied to older versions still being supported) to TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3.


CVE-2022-23577: Tensorflow is an Open Source Machine Learning Framework. The implementation of `GetInitOp` is vulnerable to a crash caus

Summary: TensorFlow, an open source machine learning framework, has a vulnerability in the `GetInitOp` function that can crash the software through a null pointer dereference (accessing memory that doesn't exist). The vulnerability affects multiple versions of TensorFlow.

Solution: The fix will be included in TensorFlow 2.8.0. TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3 will also receive this fix through a cherrypick (applying the same code change to older supported versions).


CVE-2022-23576: Tensorflow is an Open Source Machine Learning Framework. The implementation of `OpLevelCostEstimator::CalculateOutputSiz

Summary: TensorFlow (an open-source machine learning framework) has a vulnerability in its `OpLevelCostEstimator::CalculateOutputSize` function where an integer overflow (when a calculation produces a number too large for the system to handle) can occur if an attacker creates an operation with tensors (multi-dimensional arrays of numbers) containing enough elements. The vulnerability can be triggered either by using many dimensions or by making individual dimensions large enough to cause the overflow.


CVE-2022-23575: Tensorflow is an Open Source Machine Learning Framework. The implementation of `OpLevelCostEstimator::CalculateTensorSiz

Summary: TensorFlow, an open-source machine learning framework, has a vulnerability in its `OpLevelCostEstimator::CalculateTensorSize` function that can be exploited through integer overflow (a type of bug where numbers become too large for the program to handle correctly). An attacker could trigger this by creating an operation with a tensor (a multi-dimensional array of data) containing an extremely large number of elements.

Solution: The fix will be included in TensorFlow 2.8.0. The vulnerability will also be patched in TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, which are still in the supported range.


CVE-2022-23574: Tensorflow is an Open Source Machine Learning Framework. There is a typo in TensorFlow's `SpecializeType` which results

Summary: TensorFlow, an open-source machine learning framework, has a typo in its `SpecializeType` code that causes a heap OOB (out-of-bounds, where the program tries to read or write memory outside the area it's allowed to access) read/write vulnerability. Due to the typo, a variable called `arg` uses the wrong loop index, which allows code to read and modify data outside the intended memory bounds.

Solution: The fix will be included in TensorFlow 2.8.0. The commit will also be cherry-picked (applied to older versions) on TensorFlow 2.7.1 and TensorFlow 2.6.3.


CVE-2022-23573: Tensorflow is an Open Source Machine Learning Framework. The implementation of `AssignOp` can result in copying uninitia

Summary: TensorFlow's `AssignOp` (a copy operation in machine learning code) has a bug where it can copy uninitialized data (memory with random or leftover values) to a new tensor, causing unpredictable behavior. The code only checks that the destination is ready, but not the source, leaving room for uninitialized data to be used.

Solution: Update to TensorFlow 2.8.0. If you cannot upgrade immediately, apply backported fixes available in TensorFlow 2.7.1, TensorFlow 2.6.3, or TensorFlow 2.5.3, which are still supported versions.


CVE-2022-23572: Tensorflow is an Open Source Machine Learning Framework. Under certain scenarios, TensorFlow can fail to specialize a ty

Summary: TensorFlow (an open source machine learning framework) has a bug where it sometimes fails to determine data types correctly during shape inference (the process of figuring out what dimensions data will have). The bug is hidden in production builds because assertion checks are disabled, causing the program to crash when it tries to use an error result as if it were valid data.

Solution: The fix will be included in TensorFlow 2.8.0. The fix will also be applied to TensorFlow 2.7.1 and TensorFlow 2.6.3, which are still in the supported range.


CVE-2022-23571: Tensorflow is an Open Source Machine Learning Framework. When decoding a tensor from protobuf, a TensorFlow process can

Summary: TensorFlow (an open source machine learning framework) has a vulnerability where attackers can crash TensorFlow processes by sending specially crafted data with invalid tensor types or shapes during decoding from protobuf (a data format used to serialize structured data). This is a denial of service attack, meaning the attacker can make the system stop working rather than gain unauthorized access.

Solution: The fix will be included in TensorFlow 2.8.0. The vulnerability will also be patched in TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3.


CVE-2022-23570: Tensorflow is an Open Source Machine Learning Framework. When decoding a tensor from protobuf, TensorFlow might do a nul

Summary: TensorFlow, an open-source machine learning framework, has a bug where it can crash or behave unpredictably when decoding certain data structures (protobuf, a format for storing structured data) if some required information is missing. The problem occurs because the code only checks for this issue in debug builds (test versions), not in production builds (versions used in real applications), so real users may experience crashes or undefined behavior.

Solution: The fix will be included in TensorFlow 2.8.0. TensorFlow 2.7.1 and TensorFlow 2.6.3 will also receive this fix through a cherrypick (backporting the fix to older supported versions).


CVE-2022-23566: Tensorflow is an Open Source Machine Learning Framework. TensorFlow is vulnerable to a heap OOB write in `Grappler`. The

Summary: TensorFlow, an open-source machine learning framework, has a vulnerability in its Grappler component where the `set_output` function can write data to an array at any index specified by an attacker, creating a heap OOB write (out-of-bounds write, where data is written to memory locations it shouldn't access). This gives a malicious user the ability to write arbitrary data to unintended memory locations.

Solution: The fix will be included in TensorFlow 2.8.0. TensorFlow 2.7.1, 2.6.3, and 2.5.3 will also receive the fix via a cherry-pick (applying specific code changes to older versions), as these versions are still supported and also affected.


CVE-2022-23565: Tensorflow is an Open Source Machine Learning Framework. An attacker can trigger denial of service via assertion failure

Summary: TensorFlow (an open-source machine learning framework) has a vulnerability where an attacker can crash the system by modifying a SavedModel file on disk to contain duplicate operation attributes, triggering an assertion failure (a built-in check that causes the program to stop if a condition is false). This is a denial of service attack (making a system unavailable to legitimate users).

Solution: Update to TensorFlow 2.8.0 or apply the patch from the commit at https://github.com/tensorflow/tensorflow/commit/c2b31ff2d3151acb230edc3f5b1832d2c713a9e0. The fix will also be included in TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3.


CVE-2022-23564: Tensorflow is an Open Source Machine Learning Framework. When decoding a resource handle tensor from protobuf, a TensorF

Summary: TensorFlow (an open source machine learning framework) has a vulnerability where attackers can crash TensorFlow processes by providing specially crafted input when the system converts protobuf (a data format) into resource handle tensors, because a validation check can be bypassed through user-controlled arguments.

Solution: Update to TensorFlow 2.8.0, or apply cherrypicked fixes available in TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3.


CVE-2022-23563: Tensorflow is an Open Source Machine Learning Framework. In multiple places, TensorFlow uses `tempfile.mktemp` to create

Summary: TensorFlow, an open-source machine learning framework, uses an unsafe function called `tempfile.mktemp` to create temporary files in multiple places. This creates a race condition vulnerability (TOC/TOU, a timing gap where another process can interfere between when the system checks if a filename exists and when it actually creates the file), which is especially dangerous in utility and library code rather than just testing code.

Solution: The source states: "We have patched the issue in several commits, replacing `mktemp` with the safer `mkstemp`/`mkdtemp` functions, according to the usage pattern. Users are advised to upgrade as soon as possible."


CVE-2022-23562: Tensorflow is an Open Source Machine Learning Framework. The implementation of `Range` suffers from integer overflows. T

Summary: TensorFlow (an open-source framework for building machine learning models) has a vulnerability in its Range function where integer overflows (when numbers get too large and wrap around to incorrect values) can cause undefined behavior or extremely large memory allocations. This bug affects multiple versions of the software.

Solution: The fix will be included in TensorFlow 2.8.0. The vulnerability will also be patched in TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, which are still supported versions.


CVE-2022-23561: Tensorflow is an Open Source Machine Learning Framework. An attacker can craft a TFLite model that would cause a write o

Summary: An attacker can create a malicious TFLite model (a compressed machine learning format for mobile devices) that writes data outside the boundaries of an array in TensorFlow, potentially overwriting the memory allocator's linked list (a data structure that tracks available memory) to achieve arbitrary write access to system memory. This vulnerability affects multiple versions of TensorFlow, an open-source framework for building AI systems.

Solution: The fix will be included in TensorFlow 2.8.0. The same fix will also be cherry-picked (backported) to TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3.


CVE-2022-23560: Tensorflow is an Open Source Machine Learning Framework. An attacker can craft a TFLite model that would allow limited r

Summary: TensorFlow, an open-source machine learning framework, has a vulnerability in TFLite (TensorFlow Lite, a lightweight version for mobile devices) where an attacker can create a specially crafted model that allows limited reads and writes outside of arrays by exploiting missing validation during conversion from sparse tensors (data structures with mostly empty values) to dense tensors (fully populated data structures). This vulnerability affects multiple versions of TensorFlow.

Solution: Upgrade to TensorFlow 2.8.0. For users on earlier supported versions, patches are also available in TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3. Users are advised to upgrade as soon as possible.


CVE-2022-23559: Tensorflow is an Open Source Machine Learning Framework. An attacker can craft a TFLite model that would cause an intege

Summary: TensorFlow (an open-source machine learning framework) has a vulnerability where an attacker can create a malicious TFLite model (a lightweight version of TensorFlow for mobile devices) that causes an integer overflow (when a number calculation exceeds the maximum value a computer can store) in embedding lookup operations. This overflow can sometimes lead to heap OOB read/write (accessing memory outside the intended boundaries), potentially allowing attackers to read or corrupt data.

Solution: Users are advised to upgrade to a patched version. Patches are available at: https://github.com/tensorflow/tensorflow/commit/1de49725a5fc4e48f1a3b902ec3599ee99283043, https://github.com/tensorflow/tensorflow/commit/a4e401da71458d253b05e41f28637b65baf64be4, and https://github.com/tensorflow/tensorflow/commit/f19be71717c497723ba0cea0379e84f061a75e01


CVE-2022-23558: Tensorflow is an Open Source Machine Learning Framework. An attacker can craft a TFLite model that would cause an intege

Summary: An attacker can create a malicious TFLite model (a lightweight version of TensorFlow used on mobile devices) that causes an integer overflow (where a number gets too large to fit in its storage space, wrapping around to a negative or small value) in TensorFlow's `TfLiteIntArrayCreate` function. The vulnerability happens because the code returns an `int` instead of a larger `size_t` datatype, allowing attackers to manipulate model inputs so the calculated size exceeds what an `int` can hold.

Solution: The fix will be included in TensorFlow 2.8.0. It will also be backported (applied to older versions still receiving updates) to TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3.


CVE-2022-23557: Tensorflow is an Open Source Machine Learning Framework. An attacker can craft a TFLite model that would trigger a divis

Summary: TensorFlow, an open-source machine learning framework, has a vulnerability in its TFLite (TensorFlow Lite, a version optimized for mobile devices) model processor where an attacker can create a specially crafted model that causes a division by zero error (attempting to divide a number by zero, which crashes programs) in the `BiasAndClamp` function because the code doesn't check if `bias_size` is zero before using it.

Solution: The fix will be included in TensorFlow 2.8.0. The patch will also be applied to TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3.


CVE-2022-21741: Tensorflow is an Open Source Machine Learning Framework. ### Impact An attacker can craft a TFLite model that would trig

Summary: A vulnerability in TensorFlow (an open-source machine learning framework) allows an attacker to create a malicious TFLite model (TensorFlow Lite, a lightweight version of TensorFlow) that causes a division by zero error in depthwise convolutions (a type of neural network operation). The bug occurs because the code divides by a user-controlled parameter without first checking that it is positive.

Solution: The fix will be included in TensorFlow 2.8.0. It will also be cherry-picked (applied as a patch) to TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3.


CVE-2022-21740: Tensorflow is an Open Source Machine Learning Framework. The implementation of `SparseCountSparseOutput` is vulnerable t

Summary: TensorFlow, an open-source machine learning framework, has a vulnerability in its `SparseCountSparseOutput` function that allows a heap overflow (a type of memory corruption where a program writes data beyond allocated memory boundaries). The vulnerability affects multiple versions of TensorFlow.

Solution: The fix will be included in TensorFlow 2.8.0. Patches will also be cherry-picked (applied) to TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, which are still in the supported range.


CVE-2022-21739: Tensorflow is an Open Source Machine Learning Framework. The implementation of `QuantizedMaxPool` has an undefined behav

Summary: TensorFlow (an open source machine learning framework) has a bug in its `QuantizedMaxPool` function where user-controlled inputs can trigger a null pointer dereference (a crash caused by the program trying to access memory that doesn't exist). The vulnerability allows attackers to potentially cause the program to crash or behave unpredictably.

Solution: The fix will be included in TensorFlow 2.8.0. The patch will also be backported to TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3. Users should update to one of these versions or later.


CVE-2022-21738: Tensorflow is an Open Source Machine Learning Framework. The implementation of `SparseCountSparseOutput` can be made to

Summary: TensorFlow, an open source machine learning framework, has a vulnerability in its `SparseCountSparseOutput` function where an integer overflow (a number becoming too large for its storage space) can crash the TensorFlow process during memory allocation. This vulnerability affects multiple versions of TensorFlow.

Solution: The fix will be included in TensorFlow 2.8.0. TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3 will also receive this fix through a cherry-pick (applying the same fix to older supported versions).


CVE-2022-21737: Tensorflow is an Open Source Machine Learning Framework. The implementation of `*Bincount` operations allows malicious u

Summary: TensorFlow (an open-source machine learning framework) has a vulnerability in its Bincount operations that allows attackers to crash the system (denial of service) by sending specially crafted arguments that trigger internal safety checks to fail. The problem occurs because some invalid input conditions aren't caught early enough during the system's processing stages, leading to crashes when the system tries to allocate memory for output data.

Solution: The fix will be included in TensorFlow 2.8.0. The fix will also be backported (applied to older versions) in TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3.


CVE-2022-23569: Tensorflow is an Open Source Machine Learning Framework. Multiple operations in TensorFlow can be used to trigger a deni

Summary: TensorFlow (an open-source machine learning framework) has a vulnerability where certain operations can crash the program through denial of service attacks (making it unavailable by triggering assertion failures, which are safety checks in code that stop execution if something goes wrong). The developers have fixed the issue and plan to release patches across multiple supported versions.

Solution: The fix will be included in TensorFlow 2.8.0. Patches will also be cherry-picked (applied retroactively) to TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3.


CVE-2022-21735: Tensorflow is an Open Source Machine Learning Framework. The implementation of `FractionalMaxPool` can be made to crash

Summary: TensorFlow, an open-source machine learning framework, has a vulnerability in its `FractionalMaxPool` function (a pooling operation used in neural networks) that can crash the program through a division by zero error (attempting to divide a number by zero, which is mathematically undefined). The vulnerability affects multiple versions of TensorFlow.

Solution: The fix will be included in TensorFlow 2.8.0. TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3 will also receive this fix through a cherrypick commit, as these versions are still supported.


CVE-2022-21734: Tensorflow is an Open Source Machine Learning Framework. The implementation of `MapStage` is vulnerable a `CHECK`-fail i

Summary: TensorFlow, an open-source machine learning framework, has a vulnerability in its `MapStage` component where a CHECK-fail (a type of crash caused by a failed validation check) occurs if the key tensor (a multi-dimensional array of data) is not a scalar (a single value). This bug can cause the program to crash unexpectedly.

Solution: The fix will be included in TensorFlow 2.8.0. The vulnerability will also be patched in TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, which are still in the supported range.


CVE-2022-21729: Tensorflow is an Open Source Machine Learning Framework. The implementation of `UnravelIndex` is vulnerable to a divisio

Summary: TensorFlow, an open-source machine learning framework, has a vulnerability in its `UnravelIndex` function caused by an integer overflow bug (a situation where a number becomes too large for the system to handle correctly) that leads to division by zero. This flaw affects multiple versions of TensorFlow and could allow attackers to crash or disrupt the software.

Solution: The fix will be included in TensorFlow 2.8.0. TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3 will also receive this fix through a cherrypick (applying a specific code change to older versions).


CVE-2022-21725: Tensorflow is an Open Source Machine Learning Framework. The estimator for the cost of some convolution operations can b

Summary: TensorFlow (an open-source machine learning framework) has a bug where a cost estimator for convolution operations can be forced to divide by zero because it doesn't check that the stride argument (a parameter controlling step size in operations) is positive. The fix adds validation to ensure the stride is valid before the operation runs.

Solution: The fix will be included in TensorFlow 2.8.0. The fix will also be back-ported to TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, which are still in the supported range.


CVE-2022-23568: Tensorflow is an Open Source Machine Learning Framework. The implementation of `AddManySparseToTensorsMap` is vulnerable

Summary: TensorFlow (an open-source machine learning framework) has a vulnerability in the `AddManySparseToTensorsMap` function where an integer overflow (when a number gets too large for its storage space) causes the program to crash when creating new TensorShape objects. The problem exists because the code doesn't properly validate input tensor shapes before using them.

Solution: The fix will be included in TensorFlow 2.8.0. The fix will also be applied to TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3 through a cherrypick (applying specific code changes to older versions).


CVE-2022-23567: Tensorflow is an Open Source Machine Learning Framework. The implementations of `Sparse*Cwise*` ops are vulnerable to in

Summary: TensorFlow, an open-source machine learning framework, has a vulnerability in its `Sparse*Cwise*` operations (specialized math functions for sparse tensors, a type of data structure with mostly empty values) that can be exploited through integer overflows (when calculations produce numbers too large for the system to handle). An attacker could cause the system to run out of memory or crash by providing specially crafted input dimensions.


CVE-2022-21736: Tensorflow is an Open Source Machine Learning Framework. The implementation of `SparseTensorSliceDataset` has an undefin

Summary: TensorFlow, an open-source machine learning framework, has a bug in the `SparseTensorSliceDataset` component where it can crash by dereferencing a null pointer (accessing memory that doesn't exist) when given certain inputs. The code doesn't properly check that its three input arguments meet required conditions before using them.

Solution: The fix will be included in TensorFlow 2.8.0. The fix will also be applied to TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, which are still in the supported range.


CVE-2022-21733: Tensorflow is an Open Source Machine Learning Framework. The implementation of `StringNGrams` can be used to trigger a d

Summary: A bug in TensorFlow's `StringNGrams` function (a tool that breaks text into small overlapping pieces) allows attackers to crash the system by causing it to run out of memory through an integer overflow (when a number gets too large and wraps around to an incorrect value). The problem stems from missing validation on the `pad_width` parameter, which can result in a negative `ngram_width` value that causes excessive memory allocation.

Solution: The fix will be included in TensorFlow 2.8.0. TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3 will also receive this fix through cherrypicked commits (backports of the fix to older versions still being supported).


CVE-2022-21732: Tensorflow is an Open Source Machine Learning Framework. The implementation of `ThreadPoolHandle` can be used to trigger

Summary: TensorFlow (an open source machine learning framework) has a vulnerability in its `ThreadPoolHandle` component that allows attackers to cause a denial of service attack (making a service unavailable by overwhelming it) by allocating excessive memory. The problem exists because the code only checks that the `num_threads` argument is not negative, but does not limit how large the value can be.

Solution: The fix will be included in TensorFlow 2.8.0 and will also be backported to TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3 (which are still supported versions).


CVE-2022-21731: Tensorflow is an Open Source Machine Learning Framework. The implementation of shape inference for `ConcatV2` can be use

Summary: TensorFlow, an open-source machine learning framework, has a bug in its shape inference (the process of figuring out data dimensions) for the `ConcatV2` operation that can be exploited to crash a program through a segfault (a memory access error). The vulnerability occurs because a type confusion (mixing up different data types) allows a negative value to bypass a safety check, potentially letting attackers cause a denial of service attack (making the system unavailable).

Solution: The fix will be included in TensorFlow 2.8.0. The fix will also be applied to TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3 through backports (applying the same fix to older supported versions).


CVE-2022-21730: Tensorflow is an Open Source Machine Learning Framework. The implementation of `FractionalAvgPoolGrad` does not consider

Summary: TensorFlow, an open-source machine learning framework, has a vulnerability in its `FractionalAvgPoolGrad` function that fails to validate input data properly, allowing an attacker to read memory from outside the intended bounds of the heap (out-of-bounds read, where a program accesses data it shouldn't). This is a memory safety issue that could let attackers access sensitive information.

Solution: The fix will be included in TensorFlow 2.8.0. Security patches will also be backported to TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, which are still in the supported range.


CVE-2022-21728: Tensorflow is an Open Source Machine Learning Framework. The implementation of shape inference for `ReverseSequence` doe

Summary: TensorFlow, an open source machine learning framework, has a bug in its shape inference for the `ReverseSequence` operation where it doesn't properly check if the `batch_dim` parameter is a negative number, allowing it to read memory outside the intended array bounds (a heap OOB read, or out-of-bounds read that accesses invalid memory). While the code checks that `batch_dim` isn't larger than the input rank, it fails to reject negative values that are too extreme, which can cause the program to access memory before the start of the array.

Solution: The fix will be included in TensorFlow 2.8.0 and will also be applied to TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3 through cherrypicking (applying the same commit to older versions).


CVE-2022-21727: Tensorflow is an Open Source Machine Learning Framework. The implementation of shape inference for `Dequantize` is vulne

Summary: TensorFlow, an open source machine learning framework, has a vulnerability in its shape inference for the `Dequantize` operation where the `axis` argument is not properly validated. An attacker can provide an unexpectedly large `axis` value that causes an integer overflow (when a number becomes too large and wraps around to a negative or incorrect value) when the code adds 1 to it.

Solution: The fix will be included in TensorFlow 2.8.0. It will also be backported (applied to earlier versions) to TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3.


CVE-2022-21726: Tensorflow is an Open Source Machine Learning Framework. The implementation of `Dequantize` does not fully validate the

Summary: TensorFlow, an open-source machine learning framework, has a bug in its `Dequantize` function where the `axis` parameter (which specifies which dimension to operate on) isn't properly validated. This allows attackers to read past the end of an array in memory, potentially causing crashes or exposing sensitive data through a heap OOB (out-of-bounds) access, which means reading memory locations outside the intended storage area.

Solution: The fix will be included in TensorFlow 2.8.0. The vulnerability will also be patched in TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3 through backported commits (cherrypicks).


CVE-2022-21296: Vulnerability in the Oracle Java SE, Oracle GraalVM Enterprise Edition product of Oracle Java SE (component: JAXP). Supp

Summary: A vulnerability in Oracle Java SE and Oracle GraalVM Enterprise Edition's JAXP component (a Java library for processing XML data) allows an attacker on the network to read some data they shouldn't have access to without needing to log in. The vulnerability affects several older versions of Java and can be exploited through web services or untrusted code running in a Java sandbox (a restricted environment meant to safely run untrusted programs).

Solution: N/A -- no mitigation discussed in source.


Log4Shell and Request Forgery Attacks

Summary: Log4Shell is a critical vulnerability in Apache's log4j library (a widely-used Java logging tool) that allows remote code execution (running commands on a system from afar) through its Java Naming and Directory Interface support. The vulnerability is particularly dangerous because log4j is used in many Java applications and is easy to exploit. The source mentions that patches were released to fix the issue, though it also notes that bypasses to those patches were discovered, leading to additional patches.

Solution: Patches were released to address the vulnerability. The source notes that when bypasses to initial patches were discovered, additional patches were subsequently released.


CVE-2021-4118: pytorch-lightning is vulnerable to Deserialization of Untrusted Data

Summary: pytorch-lightning (a popular machine learning library) contains a vulnerability related to deserialization of untrusted data (CWE-502, where a program unsafely processes data from an untrusted source, potentially allowing an attacker to run malicious code). The vulnerability was identified and reported through the huntr.dev bug bounty program.

Solution: A patch is available in the pytorch-lightning repository at commit 62f1e82e032eb16565e676d39e0db0cac7e34ace. Users should update to this patched version to fix the deserialization vulnerability.


CVE-2021-43831: Gradio is an open source framework for building interactive machine learning models and demos. In versions prior to 2.5.

Summary: Gradio, a framework for building interactive machine learning demos, had a vulnerability in versions before 2.5.0 where users could read any file on the host computer if they knew the file path, since file access wasn't restricted (though files could only be opened in read-only mode). This meant anyone with a link to a Gradio interface could potentially access sensitive files on the server.

Solution: Update to Gradio version 2.5.0 or later, where the vulnerability has been patched.


CVE-2021-43811: Sockeye is an open-source sequence-to-sequence framework for Neural Machine Translation built on PyTorch. Sockeye uses Y

Summary: Sockeye, an open-source tool for Neural Machine Translation (a type of AI that translates text between languages), had a security flaw in versions before 2.3.24 where it used unsafe YAML loading (a method to read configuration files without proper safety checks). An attacker could hide malicious code in a model's configuration file, and if a user downloaded and ran that model, the hidden code would execute on their computer.

Solution: The issue is fixed in version 2.3.24. Users should update to this version or later.


CVE-2021-43775: Aim is an open-source, self-hosted machine learning experiment tracking tool. Versions of Aim prior to 3.1.0 are vulnera

Summary: Aim is an open-source tool for tracking machine learning experiments. Versions before 3.1.0 have a path traversal vulnerability (a type of attack where special sequences like '../' are used to access files outside the intended directory), which could allow attackers to read sensitive files like source code, configuration files, or system files on the server.

Solution: Upgrade to Aim v3.1.0, where the vulnerability is resolved.


CVE-2021-41228: TensorFlow is an open source platform for machine learning. In affected versions TensorFlow's `saved_model_cli` tool is

Summary: TensorFlow's `saved_model_cli` tool (a command-line utility for working with machine learning models) has a code injection vulnerability because it runs `eval` on user-supplied strings, which could allow attackers to execute arbitrary code on the system. The risk is limited since the tool is only run manually by users, not automatically.

Solution: The developers patched this by adding a `safe` flag that defaults to `True` and an explicit warning for users. The fix is included in TensorFlow 2.7.0, and will also be backported (applied to older versions still being supported) to TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4.


CVE-2021-41227: TensorFlow is an open source platform for machine learning. In affected versions the `ImmutableConst` operation in Tenso

Summary: TensorFlow (an open source machine learning platform) has a vulnerability in the `ImmutableConst` operation that allows attackers to read arbitrary memory contents. The issue occurs because the operation doesn't properly handle a special type of string called `tstring` that can reference memory-mapped data.

Solution: The fix will be included in TensorFlow 2.7.0. The patch will also be backported (applied to older supported versions) in TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4.


CVE-2021-41225: TensorFlow is an open source platform for machine learning. In affected versions TensorFlow's Grappler optimizer has a u

Summary: TensorFlow's Grappler optimizer (the part of TensorFlow that improves how machine learning models run) has a bug where a variable called `dequeue_node` is never initialized if a saved model doesn't contain a specific type of operation called a `Dequeue` node. This uninitialized variable could cause the optimizer to behave unpredictably or crash.

Solution: Update to TensorFlow 2.7.0 or later. If you need to stay on earlier versions, update to TensorFlow 2.6.1, 2.5.2, or 2.4.4, which will include the fix through a cherrypick (backport of the specific fix to older versions).


CVE-2021-41222: TensorFlow is an open source platform for machine learning. In affected versions the implementation of `SplitV` can trig

Summary: TensorFlow, an open source platform for machine learning, has a vulnerability in the `SplitV` function where supplying negative arguments can cause a segfault (a crash from accessing invalid memory). The crash happens when the `size_splits` parameter contains multiple values with at least one being negative.

Solution: The fix will be included in TensorFlow 2.7.0. The patch will also be backported to TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, which are still in the supported range. Users can reference the specific commit at https://github.com/tensorflow/tensorflow/commit/25d622ffc432acc736b14ca3904177579e733cc6.


CVE-2021-41221: TensorFlow is an open source platform for machine learning. In affected versions the shape inference code for the `Cudnn

Summary: TensorFlow (an open source machine learning platform) has a vulnerability where shape inference code for certain operations can be tricked into accessing invalid memory through a heap buffer overflow (where a program writes data beyond the allocated memory space). This happens because the code doesn't verify that certain input parameters have the correct structure before using them.

Solution: The fix will be included in TensorFlow 2.7.0. The patch will also be backported (adapted and released) for TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4.


CVE-2021-41220: TensorFlow is an open source platform for machine learning. In affected versions the async implementation of `Collective

Summary: TensorFlow, an open source platform for machine learning, had a memory leak and use-after-free bug (a mistake where the program tries to access data after it has already been deleted) in its `CollectiveReduceV2` function due to improper handling of asynchronous operations. The vulnerability was caused by objects being moved from memory while still being accessed elsewhere in the code.

Solution: The fix is included in TensorFlow 2.7.0, and the patch was also backported to TensorFlow 2.6.1, which was the only other affected version.


CVE-2021-41216: TensorFlow is an open source platform for machine learning. In affected versions the shape inference function for `Trans

Summary: TensorFlow (an open source platform for machine learning) contains a vulnerability in its shape inference function for the `Transpose` operation where negative values in the `perm` parameter can cause a heap buffer overflow (writing data outside the intended memory boundaries). The issue stems from insufficient validation of the indices in `perm` before they are processed.

Solution: The fix will be included in TensorFlow 2.7.0. Users of affected versions should upgrade to TensorFlow 2.7.0 or the patched versions: TensorFlow 2.6.1, TensorFlow 2.5.2, or TensorFlow 2.4.4.


CVE-2021-41213: TensorFlow is an open source platform for machine learning. In affected versions the code behind `tf.function` API can b

Summary: TensorFlow, an open source machine learning platform, has a vulnerability in its `tf.function` API (a feature that converts Python functions into optimized operations) where mutually recursive functions (functions that call each other back and forth) can cause a deadlock using a non-reentrant Lock (a mechanism that prevents simultaneous access but doesn't allow the same thread to re-enter it). An attacker could cause a denial of service by tricking users into loading vulnerable models, though this scenario is uncommon.

Solution: The fix will be included in TensorFlow 2.7.0. The fix will also be backported (applied to older supported versions) to TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4.