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.


CVE-2021-41218: TensorFlow is an open source platform for machine learning. In affected versions the shape inference code for `AllToAll`

Summary: TensorFlow, an open source machine learning platform, has a bug in its shape inference code for the `AllToAll` function that causes a division by zero error (when a value is divided by 0, causing the program to crash) whenever the `split_count` argument is set to 0. This vulnerability could allow an attacker to crash or disrupt a TensorFlow application.

Solution: The fix is included in TensorFlow 2.7.0. For users on earlier versions still receiving support, the patch will also be applied to TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4. Users should update to one of these patched versions.


CVE-2021-41209: TensorFlow is an open source platform for machine learning. In affected versions the implementations for convolution ope

Summary: TensorFlow (an open source platform for machine learning) has a bug where its convolution operators (mathematical functions that process data in neural networks) crash with a division by zero error when given empty filter tensors (arrays of parameters). This vulnerability affects multiple versions of TensorFlow.

Solution: The fix is included in TensorFlow 2.7.0 and has also been backported to TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4.


CVE-2021-41208: TensorFlow is an open source platform for machine learning. In affected versions the code for boosted trees in TensorFlo

Summary: TensorFlow's boosted trees code (a machine learning feature for building multiple decision trees together) lacks proper input validation, allowing attackers to crash the system (denial of service, where a service becomes unavailable), read sensitive data from memory, or write malicious data to memory buffers. The TensorFlow developers recommend stopping use of these APIs since the boosted trees code is no longer actively maintained.

Solution: The fix will be included in TensorFlow 2.7.0. Security patches will also be backported to TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4.


CVE-2021-41207: TensorFlow is an open source platform for machine learning. In affected versions the implementation of `ParallelConcat`

Summary: TensorFlow, an open source platform for machine learning, has a vulnerability in its `ParallelConcat` function that lacks proper input validation and can cause a division by zero error (a crash caused by dividing a number by zero). The affected versions have known fixes available through updates to TensorFlow 2.7.0 and earlier supported versions.

Solution: Update to TensorFlow 2.7.0. For users on earlier versions still in the supported range, apply patches for TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4. The fix is available in the commit: https://github.com/tensorflow/tensorflow/commit/f2c3931113eaafe9ef558faaddd48e00a6606235


CVE-2021-41206: TensorFlow is an open source platform for machine learning. In affected versions several TensorFlow operations are missi

Summary: TensorFlow, a machine learning platform, has a vulnerability (CVE-2021-41206) where certain operations don't properly check the size and dimensions of tensor arguments (the numerical arrays that machine learning models process). This missing validation can cause crashes, memory corruption (reads and writes to unintended memory locations), or other undefined behavior depending on which operation is affected.

Solution: The fixes will be included in TensorFlow 2.7.0. Patches will also be backported to TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4.


CVE-2021-41202: TensorFlow is an open source platform for machine learning. In affected versions while calculating the size of the outpu

Summary: TensorFlow, an open source platform for machine learning, has a bug in its `tf.range` function where a conditional statement mixes two different number types (int64, a large integer type, and double, a decimal number type). Due to how C++ automatically converts between these types, the calculation overflows (produces incorrect results that are too large to store). This causes the output size calculation to fail.

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


CVE-2021-41226: TensorFlow is an open source platform for machine learning. In affected versions the implementation of `SparseBinCount`

Summary: TensorFlow, an open source platform for machine learning, has a vulnerability in its `SparseBinCount` function that allows heap OOB access (out-of-bounds memory access, where a program reads data outside the memory it's allowed to use) because it doesn't validate that the `values` argument matches the shape of the sparse output. This bug could let attackers crash the system or potentially read sensitive data from memory.

Solution: The fix is included in TensorFlow 2.7.0 and has been backported to TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4. Users should update to one of these patched versions.


CVE-2021-41224: TensorFlow is an open source platform for machine learning. In affected versions the implementation of `SparseFillEmptyR

Summary: TensorFlow, an open source machine learning platform, has a vulnerability in the `SparseFillEmptyRows` function that can cause a heap OOB access (out-of-bounds read, where a program tries to read memory it shouldn't access) when the size of `indices` does not match the size of `values`. This is a memory safety bug that could potentially crash the program or expose sensitive data.

Solution: The fix will be included in TensorFlow 2.7.0. The vulnerability is also addressed in TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4 through a cherry-picked commit (a targeted code fix applied to older versions). Users should update to one of these patched versions.


CVE-2021-41223: TensorFlow is an open source platform for machine learning. In affected versions the implementation of `FusedBatchNorm`

Summary: TensorFlow, an open source machine learning platform, has a vulnerability in its `FusedBatchNorm` kernels that allows heap OOB access (out-of-bounds memory reading, where a program tries to read data outside the memory space it's allowed to use). This bug affects multiple older versions of TensorFlow that are still supported.

Solution: The fix will be included in TensorFlow 2.7.0. The commit will also be cherry-picked (applied retroactively) to TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4.


CVE-2021-41219: TensorFlow is an open source platform for machine learning. In affected versions the code for sparse matrix multiplicati

Summary: TensorFlow, an open source platform for machine learning, has a vulnerability in its sparse matrix multiplication code where it can crash or behave unpredictably (undefined behavior) if matrix dimensions are 0 or less, because the code tries to write to an empty memory location (nullptr, a reference to nothing). When dimensions are invalid, the code should create an empty output but not write to it, otherwise it causes a heap OOB access (writing data outside the boundaries of allocated memory).

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


CVE-2021-41217: TensorFlow is an open source platform for machine learning. In affected versions the process of building the control flo

Summary: TensorFlow, an open source machine learning platform, has a vulnerability where the code that builds a control flow graph (the structure representing how data moves through a model) crashes when it assumes paired nodes exist but they don't. When the first node in a pair is missing, the code tries to use a null pointer (a reference to nothing), causing the program to crash.

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


CVE-2021-41215: TensorFlow is an open source platform for machine learning. In affected versions the shape inference code for `Deseriali

Summary: TensorFlow, an open source machine learning platform, has a vulnerability where the shape inference code for `DeserializeSparse` (a function that converts serialized data back into sparse tensors, which are data structures that efficiently store mostly-empty matrices) can crash due to a null pointer dereference (trying to access memory that hasn't been allocated). This happens because the code incorrectly assumes the input tensor has a specific structure.

Solution: The fix will be included in TensorFlow 2.7.0. The patch will also be applied to TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4.


CVE-2021-41214: TensorFlow is an open source platform for machine learning. In affected versions the shape inference code for `tf.ragged

Summary: TensorFlow, an open source machine learning platform, has a bug in its shape inference code for the `tf.ragged.cross` function where it tries to use a null pointer (a reference to nothing), causing undefined behavior. The vulnerability is caused by accessing an uninitialized pointer (a memory location that hasn't been set up yet).

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


CVE-2021-41212: TensorFlow is an open source platform for machine learning. In affected versions the shape inference code for `tf.ragged

Summary: TensorFlow, an open source machine learning platform, has a vulnerability in its shape inference code for the `tf.ragged.cross` function that allows reading data outside the bounds of allocated memory (an out-of-bounds read, which can cause crashes or expose sensitive data). The vulnerability affects multiple versions of TensorFlow and has been patched in newer releases.

Solution: The fix is included in TensorFlow 2.7.0. For users on earlier versions, patches were also released for TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, which are still in the supported range.


CVE-2021-41211: TensorFlow is an open source platform for machine learning. In affected versions the shape inference code for `QuantizeV

Summary: TensorFlow, an open-source machine learning platform, has a vulnerability in its shape inference code for the `QuantizeV2` function that allows reading memory outside of the intended boundaries (heap OOB read, or out-of-bounds read) when the `axis` parameter is given a negative value less than -1. This happens because the code doesn't properly validate that negative axis values stay within acceptable bounds before accessing memory.

Solution: The fix will be included in TensorFlow 2.7.0. The fix will also be applied to TensorFlow 2.6.1, as this is the only other version affected.


CVE-2021-41205: TensorFlow is an open source platform for machine learning. In affected versions the shape inference functions for the `

Summary: TensorFlow, an open source platform for machine learning, has a vulnerability in its shape inference functions for `QuantizeAndDequantizeV*` operations that can cause the program to read data outside the bounds of allocated memory (an out-of-bounds read, which is a memory safety error). This affects multiple versions of TensorFlow.

Solution: The fix will be included in TensorFlow 2.7.0. The patch will also be applied to TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these versions are affected and still supported.


CVE-2021-41204: TensorFlow is an open source platform for machine learning. In affected versions during TensorFlow's Grappler optimizer

Summary: TensorFlow, an open source machine learning platform, has a bug in its Grappler optimizer (the part that optimizes computational graphs) where constant folding (simplifying calculations before running them) incorrectly tries to copy resource tensors (special data structures that shouldn't be modified), causing the program to crash. The issue affects multiple versions of TensorFlow.

Solution: The fix will be included in TensorFlow 2.7.0. Updates will also be available in TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4.


CVE-2021-41203: TensorFlow is an open source platform for machine learning. In affected versions an attacker can trigger undefined behav

Summary: TensorFlow, an open-source machine learning platform, has a vulnerability where attackers can cause crashes or undefined behavior (unpredictable program execution) by modifying saved checkpoints (saved states of a trained model) from outside the system, because the checkpoint loading code doesn't properly validate file formats. This affects multiple versions of TensorFlow that are still being supported.

Solution: The fixes will be included in TensorFlow 2.7.0. Additionally, patches will be cherry-picked (applied) to TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, which are also affected and still in the supported range.


CVE-2021-41210: TensorFlow is an open source platform for machine learning. In affected versions the shape inference functions for `Spar

Summary: TensorFlow, an open source machine learning platform, had a vulnerability in its shape inference functions for `SparseCountSparseOutput` that could cause an out-of-bounds read (accessing memory outside the intended area of a heap-allocated array, which can crash the program or leak data). This vulnerability affected multiple versions of TensorFlow.

Solution: The fix is included in TensorFlow 2.7.0. The patch was also cherry-picked (applied to earlier versions) for TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, which were still in the supported range at the time.


CVE-2021-41201: TensorFlow is an open source platform for machine learning. In affeced versions during execution, `EinsumHelper::ParseEq

Summary: TensorFlow, an open source machine learning platform, has a bug in the `EinsumHelper::ParseEquation()` function where it fails to properly initialize certain flags (variables that track whether ellipsis notation is used in inputs and outputs). The function only sets these flags to true but never to false, which can cause the program to read uninitialized memory (garbage values) if code calling this function assumes the flags are always set correctly.

Solution: The fix will be included in TensorFlow 2.7.0. The fix will also be backported (cherry-picked) to TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4.


CVE-2021-41200: TensorFlow is an open source platform for machine learning. In affected versions if `tf.summary.create_file_writer` is c

Summary: TensorFlow (an open source platform for machine learning) has a bug where calling a specific function called `tf.summary.create_file_writer` with non-scalar arguments (values that aren't single numbers) causes the program to crash due to a failed assertion check. This vulnerability affects several versions of TensorFlow.

Solution: The fix will be included in TensorFlow 2.7.0. The developers will also apply this fix 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 patch commit at https://github.com/tensorflow/tensorflow/commit/874bda09e6702cd50bac90b453b50bcc65b2769e.


CVE-2021-41199: TensorFlow is an open source platform for machine learning. In affected versions if `tf.image.resize` is called with a l

Summary: TensorFlow (an open source machine learning platform) has a bug in its `tf.image.resize` function where using very large input values causes the program to crash due to an integer overflow (when a number becomes too large for its storage type). The overflow is caught by a safety check that stops the entire process.

Solution: The fix will be included in TensorFlow 2.7.0. The fix will also be backported to TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4.


CVE-2021-41198: TensorFlow is an open source platform for machine learning. In affected versions if `tf.tile` is called with a large inp

Summary: TensorFlow (an open source machine learning platform) crashes when the `tf.tile` function (which repeats tensor data) is called with very large inputs, because the number of output elements exceeds what an `int64_t` integer type can hold, causing an overflow that triggers a safety check and terminates the process.

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


CVE-2021-41197: TensorFlow is an open source platform for machine learning. In affected versions TensorFlow allows tensor to have a larg

Summary: TensorFlow (an open source machine learning platform) has a vulnerability where tensors (multi-dimensional arrays of numbers) with very large dimensions can cause an integer overflow (when a calculation produces a number too big to store), resulting in a crash or inconsistent behavior. The vulnerability occurs because the code checks for overflow incorrectly in some parts of the codebase.

Solution: The fix will be included in TensorFlow 2.7.0. Users of affected versions should update to TensorFlow 2.7.0, or apply cherrypicked patches available for TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4.


CVE-2021-41196: TensorFlow is an open source platform for machine learning. In affected versions the Keras pooling layers can trigger a

Summary: TensorFlow (an open source machine learning platform) has a bug in its Keras pooling layers (functions that reduce data size by sampling from groups of values) that can cause a segfault (crash where the program tries to access invalid memory) if the pool size is 0 or if a dimension is negative, because the code doesn't check that these values are positive.

Solution: Update to TensorFlow 2.7.0, or apply the fix via cherrypicked commits in TensorFlow 2.6.1, TensorFlow 2.5.2, or TensorFlow 2.4.4.


CVE-2021-41195: TensorFlow is an open source platform for machine learning. In affected versions the implementation of `tf.math.segment_

Summary: TensorFlow's `tf.math.segment_*` operations (functions that process data divided into segments) crash with a denial of service error when a segment ID is very large, because the code doesn't properly handle cases where the output size exceeds what an int64_t (a 64-bit integer type) can store. The crash happens in both CPU and GPU implementations when computing output shape.

Solution: The fix will be included in TensorFlow 2.7.0. TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4 will also receive this patch as these versions are still supported.


CVE-2021-42694: An issue was discovered in the character definitions of the Unicode Specification through 14.0. The specification allows

Summary: CVE-2021-42694 is a vulnerability in the Unicode Specification (up to version 14.0) that allows attackers to create source code identifiers (like function names) using homoglyphs (characters that look identical but are technically different) to sneak malicious code into software. An attacker could use these visually identical but distinct characters in upstream dependencies (external code libraries), and developers reviewing the code might not catch the deception, allowing the malicious code to be used downstream (in other software that depends on it).

Solution: The Unicode Consortium provides guidance on mitigations for this class of issues in Unicode Technical Standard #39, Unicode Security Mechanisms, and has documented this security vulnerability in Unicode Technical Report #36, Unicode Security Considerations.


CVE-2021-41127: Rasa is an open source machine learning framework to automate text-and voice-based conversations. In affected versions a

Summary: Rasa is a framework for building conversational AI systems, and versions before 2.8.10 have a vulnerability where a malicious model file (a compressed archive containing trained AI weights) can overwrite or replace important bot files. This happens because the software doesn't properly validate what's inside the model file before extracting it.

Solution: The vulnerability is fixed in Rasa 2.8.10. For users unable to update, ensure that users do not upload untrusted model files, and restrict CLI (command-line interface, a text-based way to control software) or API endpoint access (network connections that allow external programs to interact with Rasa) where a malicious actor could target a deployed Rasa instance.


Video: Understanding Image Scaling Attacks

Summary: Adversaries can hide a smaller image within a larger one so that it becomes visible when a computer resizes the image using insecure interpolation (a method of calculating pixel values between known points). The video demonstrates this attack technique and explains how to prevent it from happening.

Solution: The source mentions that mitigation is discussed in the video but does not explicitly state the mitigation steps in the text provided. N/A -- no specific mitigation described in source.


CVE-2021-39207: parlai is a framework for training and evaluating AI models on a variety of openly available dialogue datasets. In affec

Summary: ParlAI, a framework for training AI models on dialogue datasets, has a vulnerability where it unsafely loads YAML files (a data format), allowing attackers to execute arbitrary code on affected systems. The vulnerability occurs because the framework uses an unsafe YAML loader that can be tricked into running malicious code hidden in data files.

Solution: Update ParlAI to version v1.1.0 or above. If upgrading is not possible, change the Loader to SafeLoader as a workaround. See commit 507d066ef432ea27d3e201da08009872a2f37725 for details.


Using Microsoft Counterfit to create adversarial examples for Husky AI

Summary: This post describes Microsoft Counterfit, a tool for testing machine learning models against adversarial attacks (subtle modifications to input data designed to fool AI systems). The author demonstrates how to set up Counterfit, create a custom target for a husky image classifier, and use the tool's built-in attack modules to test the model's robustness.

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

Microsoft Counterfit
HuggingFace
IBM Adversarial Robustness Toolbox
TextAttack
Keras
TensorFlow


CVE-2021-37690: TensorFlow is an end-to-end open source platform for machine learning. In affected versions when running shape functions

Summary: TensorFlow, an open-source machine learning platform, had a bug where certain shape functions created temporary data structures (ShapeAndType structs) that were deleted too quickly, causing crashes (segfaults, or sudden program failures) if other code tried to access them. The issue was that while normal output shapes were being protected by copying them to safer ownership, the code wasn't doing the same protection for shapes and types together.

Solution: The issue was patched in GitHub commit ee119d4a498979525046fba1c3dd3f13a039fbb1 and fixed by applying the same cloning logic to output shapes and types. The fix is included in TensorFlow 2.6.0, and was also backported (added to earlier versions) in TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4.


CVE-2021-37692: TensorFlow is an end-to-end open source platform for machine learning. In affected versions under certain conditions, Go

Summary: TensorFlow (an open source machine learning platform) had a bug where Go code could crash the program during memory cleanup of string tensors if encoding failed. The problem occurred because the cleanup process assumed encoding always succeeded, but didn't check whether it actually did.

Solution: The fix defers calling the finalizer function (the cleanup code) until after the tensor is fully created, and changes how memory is deallocated for string tensors to be based on bytes actually written rather than assuming encoding succeeded. This was patched in GitHub commit 8721ba96e5760c229217b594f6d2ba332beedf22 and will be included in TensorFlow 2.6.0 and will be backported to TensorFlow 2.5.1.


CVE-2021-37691: TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can craft a TFLi

Summary: TensorFlow, an open-source machine learning platform, has a vulnerability where an attacker can create a specially crafted TFLite model (a lightweight version of TensorFlow for mobile and embedded devices) that causes a division by zero error (a crash that happens when code tries to divide a number by zero) in its LSH projection feature. This flaw affects multiple versions of TensorFlow.

Solution: The issue has been patched in GitHub commit 0575b640091680cfb70f4dd93e70658de43b94f9. The fix will be included in TensorFlow 2.6.0 and will also be backported (applied to older versions) to TensorFlow 2.5.1, 2.4.3, and 2.3.4.


CVE-2021-37687: TensorFlow is an end-to-end open source platform for machine learning. In affected versions TFLite's [`GatherNd` impleme

Summary: TensorFlow Lite (TFLite, a lightweight version of TensorFlow for mobile and embedded devices) has a vulnerability in its `GatherNd` and `Gather` operations that fail to check for negative indices. An attacker can exploit this by creating a specially designed model with negative values to read sensitive data from the heap (temporary memory storage), potentially exposing private information.

Solution: The issue was patched in GitHub commits bb6a0383ed553c286f87ca88c207f6774d5c4a8f and eb921122119a6b6e470ee98b89e65d721663179d. The fix is included in TensorFlow 2.6.0 and will be backported to TensorFlow 2.5.1, 2.4.3, and 2.3.4.


CVE-2021-37685: TensorFlow is an end-to-end open source platform for machine learning. In affected versions TFLite's [`expand_dims.cc`](

Summary: TensorFlow, an open source machine learning platform, has a vulnerability in TFLite (TensorFlow Lite, a lightweight version for mobile devices) where a negative `axis` parameter value can cause the software to read data outside the intended memory area. This could potentially expose sensitive information or crash the program.

Solution: The issue was patched in GitHub commit d94ffe08a65400f898241c0374e9edc6fa8ed257. The fix is included in TensorFlow 2.6.0 and was also applied to TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4.


CVE-2021-37684: TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementations of pooli

Summary: TensorFlow (an open source platform for machine learning) has a vulnerability in its pooling operations where the code doesn't check if divisors are zero before dividing, which can cause crashes. The issue has been patched and will be included in upcoming versions of TensorFlow.

Solution: Update to TensorFlow 2.6.0, or apply the patch from GitHub commit dfa22b348b70bb89d6d6ec0ff53973bacb4f4695. If you cannot upgrade to 2.6.0, use patched versions 2.5.1, 2.4.3, or 2.3.4 (these versions will receive the fix via cherrypick).


CVE-2021-37683: TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation of divisi

Summary: TensorFlow, a popular machine learning platform, has a vulnerability in its division operation in TFLite (a lightweight version for mobile devices) where it doesn't check if the divisor (the number you're dividing by) is zero, which can cause crashes. The issue has been fixed and will be available in several updated versions of the software.

Solution: The fix is included in TensorFlow 2.6.0. It will also be backported (applied to older versions still receiving support) in TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4. Users should update to one of these patched versions.


CVE-2021-37682: TensorFlow is an end-to-end open source platform for machine learning. In affected versions all TFLite operations that u

Summary: TensorFlow, an open-source machine learning platform, has a vulnerability in TFLite (TensorFlow Lite, a lightweight version for mobile devices) where operations using quantization (a technique that reduces model size by using lower-precision numbers) can accidentally use uninitialized values because the code doesn't properly check whether quantization settings are valid before using them. This could cause unpredictable behavior in machine learning models running on mobile or embedded devices.

Solution: The issue has been patched in GitHub commits 537bc7c723439b9194a358f64d871dd326c18887, 4a91f2069f7145aab6ba2d8cfe41be8a110c18a5, and 8933b8a21280696ab119b63263babdb54c298538. The fix is included in TensorFlow 2.6.0 and has been backported to TensorFlow 2.5.1, 2.4.3, and 2.3.4.


CVE-2021-37679: TensorFlow is an end-to-end open source platform for machine learning. In affected versions it is possible to nest a `tf

Summary: TensorFlow has a vulnerability where nesting `tf.map_fn` (a function that applies operations to tensor elements) calls with RaggedTensor inputs (tensors with variable row lengths) and no function signature can leak uninitialized memory from the heap and potentially cause data loss. The bug occurs because the code doesn't verify that inner tensor shapes match when converting from a Variant tensor to a RaggedTensor.

Solution: The issue was patched in GitHub commit 4e2565483d0ffcadc719bd44893fb7f609bb5f12. The fix is included in TensorFlow 2.6.0 and was also backported (applied to earlier versions) in TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4.


CVE-2021-37678: TensorFlow is an end-to-end open source platform for machine learning. In affected versions TensorFlow and Keras can be

Summary: TensorFlow and Keras had a security flaw where loading machine learning models from YAML files (a text format for storing data) could let attackers run arbitrary code (any commands they want) on a system. The problem was caused by using an unsafe YAML parser that doesn't validate what code it runs.

Solution: The TensorFlow team removed YAML format support entirely and patched the issue in GitHub commit 23d6383eb6c14084a8fc3bdf164043b974818012. The fix is included in TensorFlow 2.6.0, and will also be backported (applied to older versions) in TensorFlow 2.5.1, 2.4.3, and 2.3.4.


CVE-2021-37677: TensorFlow is an end-to-end open source platform for machine learning. In affected versions the shape inference code for

Summary: TensorFlow, an open-source machine learning platform, has a vulnerability in its shape inference code for the `tf.raw_ops.Dequantize` function that could crash a system (denial of service via segfault, which is when a program crashes due to accessing invalid memory) if an attacker provides invalid arguments. The bug exists because the code doesn't properly validate the `axis` parameter before using it to access tensor dimensions (the size measurements of data structures in machine learning).

Solution: The issue has been patched in GitHub commit da857cfa0fde8f79ad0afdbc94e88b5d4bbec764. The fix is included in TensorFlow 2.6.0 and will be backported to TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4.


CVE-2021-37674: TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can trigger a de

Summary: TensorFlow, an open-source machine learning platform, has a vulnerability where attackers can cause a denial of service (making a system unavailable by crashing it) through a segmentation fault (a memory error that crashes a program) in the MaxPoolGrad operation due to missing input validation on certain data structures called tensors. The vulnerability exists because an earlier fix for a related issue was incomplete.

Solution: The issue has been patched in GitHub commit 136b51f10903e044308cf77117c0ed9871350475. The fix will be included in TensorFlow 2.6.0 and will be backported to TensorFlow 2.5.1, 2.4.3, and 2.3.4.


CVE-2021-37673: TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can trigger a de

Summary: TensorFlow, an open-source machine learning platform, has a vulnerability where attackers can crash the system (denial of service, a type of attack that makes a service unavailable) through a function called `tf.raw_ops.MapStage` because it doesn't validate that the `key` input is a proper non-empty tensor (a multi-dimensional array of numbers). This bug affects multiple versions of TensorFlow.

Solution: The issue has been patched in GitHub commit d7de67733925de196ec8863a33445b73f9562d1d. The fix will be included in TensorFlow 2.6.0, and will also be backported to TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4.


CVE-2021-37672: TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can read from ou

Summary: TensorFlow, an open source machine learning platform, has a vulnerability where an attacker can read data outside the intended memory bounds (a heap overflow, which is when a program accesses memory it shouldn't) by sending specially crafted invalid arguments to a function called tf.raw_ops.SdcaOptimizerV2. The vulnerability exists because the code doesn't verify that the length of input labels matches the number of examples being processed.

Solution: The issue has been patched in GitHub commit a4e138660270e7599793fa438cd7b2fc2ce215a6. The fix will be included in TensorFlow 2.6.0, and will also be backported (applied to older supported versions) to TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4.


CVE-2021-37670: TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can read from ou

Summary: TensorFlow, an open source machine learning platform, has a vulnerability where attackers can read data outside the intended memory bounds by sending specially crafted arguments to certain functions like `tf.raw_ops.UpperBound` and `tf.raw_ops.LowerBound`. The vulnerability exists because the code doesn't properly validate the rank (the number of dimensions) of the input data it receives. This could allow attackers to access sensitive information stored in memory.

Solution: The issue was patched in GitHub commit 42459e4273c2e47a3232cc16c4f4fff3b3a35c38. The fix will be included in TensorFlow 2.6.0 and will also be backported to TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4.


CVE-2021-37669: TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can cause denial

Summary: TensorFlow, an open-source machine learning platform, has a vulnerability in its `tf.raw_ops.NonMaxSuppressionV5` function that allows attackers to crash applications by supplying a negative number, which causes a division by zero error due to improper type conversion (converting a signed integer to an unsigned integer).

Solution: Update to TensorFlow 2.6.0 or apply the patches in GitHub commits 3a7362750d5c372420aa8f0caf7bf5b5c3d0f52d and b5cdbf12ffcaaffecf98f22a6be5a64bb96e4f58. Patches are also being cherry-picked (backported) into TensorFlow 2.5.1, 2.4.3, and 2.3.4.


CVE-2021-37668: TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can cause denial

Summary: TensorFlow, an open source platform for machine learning, has a vulnerability (CVE-2021-37668) where attackers can crash applications by exploiting the `tf.raw_ops.UnravelIndex` function through division by zero (a math error where a program tries to divide by 0). The bug occurs because the code doesn't check if the `dims` tensor (a multi-dimensional array) is empty before performing calculations.

Solution: The issue was patched in GitHub commit a776040a5e7ebf76eeb7eb923bf1ae417dd4d233. The fix is included in TensorFlow 2.6.0 and will be backported (adapted for earlier versions) to TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4.


CVE-2021-37665: TensorFlow is an end-to-end open source platform for machine learning. In affected versions due to incomplete validation

Summary: TensorFlow, an open source machine learning platform, has a vulnerability in its MKL implementation where incomplete validation of input tensor dimensions allows attackers to trigger undefined behavior (accessing invalid memory locations or reading data outside allocated memory bounds). Two operations, requantization and MklRequantizePerChannelOp, are affected by this flaw.

Solution: The issue was patched in GitHub commits 9e62869465573cb2d9b5053f1fa02a81fce21d69 and 203214568f5bc237603dbab6e1fd389f1572f5c9. The fix is included in TensorFlow 2.6.0 and was backported to versions 2.5.1, 2.4.3, and 2.3.4.


CVE-2021-37663: TensorFlow is an end-to-end open source platform for machine learning. In affected versions due to incomplete validation

Summary: TensorFlow, a machine learning platform, has a vulnerability in its `tf.raw_ops.QuantizeV2` function where incomplete validation (checking that inputs meet requirements) allows attackers to cause crashes or read data from invalid memory locations. The vulnerability occurs because the code doesn't properly verify that input parameters have matching sizes and are within valid ranges.

Solution: The issue has been patched in GitHub commit 6da6620efad397c85493b8f8667b821403516708. The fix will be included in TensorFlow 2.6.0 and has also been backported (adapted for older versions) to TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4.


CVE-2021-37689: TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can craft a TFLi

Summary: TensorFlow, an open-source machine learning platform, has a vulnerability where an attacker can create a malicious model file that crashes the system by triggering a null pointer dereference (accessing memory at an invalid location without checking if it's safe). The problem occurs in the MLIR optimization (a compiler technique that improves code performance) of the L2NormalizeReduceAxis operator, which tries to access data in a vector without first verifying the vector contains any elements.

Solution: The issue has been patched in GitHub commit d6b57f461b39fd1aa8c1b870f1b974aac3554955. The fix is included in TensorFlow 2.6.0 and has been backported to TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4.


CVE-2021-37688: TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can craft a TFLi

Summary: TensorFlow, an open source machine learning platform, has a vulnerability where an attacker can create a specially crafted TFLite model (a lightweight version of TensorFlow for mobile devices) that causes a null pointer dereference (attempting to access memory that doesn't exist), crashing the system and preventing it from working. The flaw occurs because the code tries to access a pointer without checking if it's valid first.

Solution: The issue was patched in GitHub commit 15691e456c7dc9bd6be203b09765b063bf4a380c. The fix will be included in TensorFlow 2.6.0 and will also be backported to TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4.


CVE-2021-37686: TensorFlow is an end-to-end open source platform for machine learning. In affected versions the strided slice implementa

Summary: TensorFlow 2.6.0 has a bug in its strided slice implementation (a feature that extracts portions of arrays), which attackers can exploit to create models that cause infinite loops (the program gets stuck repeating the same instructions endlessly). The bug appears in TFLite (TensorFlow Lite, a lightweight version for mobile devices) when handling ellipsis (a shorthand notation using '...' in array indexing).

Solution: The issue has been patched in GitHub commit dfa22b348b70bb89d6d6ec0ff53973bacb4f4695. Update TensorFlow to a version after 2.6.0.


CVE-2021-37681: TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation of SVDF i

Summary: TensorFlow (an open source machine learning platform) has a vulnerability in its SVDF implementation (a neural network component) in TFLite (a lightweight version for mobile devices) where a null pointer error (attempting to use data that doesn't exist in memory) can occur. The bug happens because the `GetVariableInput` function can return a null pointer, but the code doesn't check for this before trying to use it as valid data.

Solution: The issue has been patched in GitHub commit 5b048e87e4e55990dae6b547add4dae59f4e1c76. The fix will be included in TensorFlow 2.6.0, and will also be backported (adapted for older versions) to TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4.


CVE-2021-37680: TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation of fully

Summary: TensorFlow, an open source machine learning platform, has a vulnerability in its fully connected layers (neural network components that connect all inputs to all outputs) in TFLite (a lightweight version for mobile devices) that causes a division by zero error (attempting to divide by zero, which crashes the program). The issue has been patched and will be included in upcoming updates.

Solution: The fix will be included in TensorFlow 2.6.0. It will also be backported (applied to older versions still being supported) to TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4.


CVE-2021-37676: TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can cause undefi

Summary: TensorFlow (an open-source platform for machine learning) has a vulnerability where an attacker can trigger undefined behavior (unpredictable program crashes or malfunctions) by exploiting the `tf.raw_ops.SparseFillEmptyRows` function, which fails to check whether input arguments are empty tensors (multi-dimensional arrays). This flaw exists in the shape inference code, which is responsible for determining the size and structure of data.

Solution: The issue has been patched in GitHub commit 578e634b4f1c1c684d4b4294f9e5281b2133b3ed. The fix will be included in TensorFlow 2.6.0 and will also be back-ported to TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4.


CVE-2021-37675: TensorFlow is an end-to-end open source platform for machine learning. In affected versions most implementations of conv

Summary: TensorFlow, a machine learning platform, has a vulnerability where attackers can crash the software by exploiting division by zero errors in convolution operators (mathematical operations that process data in machine learning models). This happens because the code that checks input shapes is missing validation steps before performing divisions, allowing someone to trigger a denial of service (making the system unavailable).

Solution: The issue has been patched in GitHub commit 8a793b5d7f59e37ac7f3cd0954a750a2fe76bad4. The fix will be included in TensorFlow 2.6.0 and will be backported to TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4.


CVE-2021-37671: TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can cause undefi

Summary: TensorFlow, an open source machine learning platform, has a vulnerability in its Map and OrderedMap operations where an attacker can cause undefined behavior (unpredictable or dangerous program actions) by exploiting a missing check for empty data indices. The code checks if indices are in order but doesn't verify they exist, leaving a gap that can lead to null pointer reference binding (attempting to use memory that hasn't been allocated).

Solution: The fix is included in TensorFlow 2.6.0 and was cherrypicked into TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4. Users of affected versions should update to one of these patched releases.


CVE-2021-37667: TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can cause undefi

Summary: TensorFlow, an open source machine learning platform, has a vulnerability where an attacker can cause undefined behavior (unpredictable program crashes or malfunctions) by exploiting a flaw in the `tf.raw_ops.UnicodeEncode` function. The problem occurs because the code reads data from a tensor without first checking if that tensor is empty, which can lead to a null pointer dereference (trying to access memory that doesn't exist).

Solution: The issue is patched in GitHub commit 2e0ee46f1a47675152d3d865797a18358881d7a6. The fix will be included in TensorFlow 2.6.0 and will also be backported (applied to earlier versions still receiving updates) in TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4.


CVE-2021-37666: TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can cause undefi

Summary: TensorFlow, an open source machine learning platform, has a vulnerability (CVE-2021-37666) where attackers can cause undefined behavior (unpredictable program crashes or errors) by exploiting incomplete validation in the RaggedTensorToVariant function. The flaw occurs when the function receives empty input values that it doesn't properly check for.

Solution: The issue has been patched in GitHub commit be7a4de6adfbd303ce08be4332554dff70362612. The fix will be included in TensorFlow 2.6.0, and will also be back-ported to TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4.


CVE-2021-37652: TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation for `tf.r

Summary: TensorFlow, a machine learning platform, has a use-after-free vulnerability (a bug where freed memory is accessed again) in the `tf.raw_ops.BoostedTreesCreateEnsemble` function that attackers can trigger with specially crafted input. The issue stems from refactoring that changed a resource from a naked pointer (basic memory reference) to a smart pointer (automatic memory management), causing the resource to be freed twice and its members to be accessed during cleanup after it's already been deallocated.

Solution: The issue was patched in GitHub commit 5ecec9c6fbdbc6be03295685190a45e7eee726ab. The fix is included in TensorFlow 2.6.0 and was also backported to TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4.