CVE-2021-29523

TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a denial of service via a CHECK-fail in tf.raw_ops.AddManySparseToTensorsMap. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/6f9896890c4c703ae0a0845394086e2e1e523299/tensorflow/core/kernels/sparse_tensors_map_ops.cc#L257) takes the values specified in sparse_shape as dimensions for the output shape. The TensorShape constructor(https://github.com/tensorflow/tensorflow/blob/6f9896890c4c703ae0a0845394086e2e1e523299/tensorflow/core/framework/tensor_shape.cc#L183-L188) uses a CHECK operation which triggers when InitDims(https://github.com/tensorflow/tensorflow/blob/6f9896890c4c703ae0a0845394086e2e1e523299/tensorflow/core/framework/tensor_shape.cc#L212-L296) returns a non-OK status. This is a legacy implementation of the constructor and operations should use BuildTensorShapeBase or AddDimWithStatus to prevent CHECK-failures in the presence of overflows. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.

Weakness

The product performs a calculation that can produce an integer overflow or wraparound, when the logic assumes that the resulting value will always be larger than the original value. This can introduce other weaknesses when the calculation is used for resource management or execution control.

Affected Software

Potential Mitigations

• Use a language that does not allow this weakness to occur or provides constructs that make this weakness easier to avoid.
• If possible, choose a language or compiler that performs automatic bounds checking.
• Use a vetted library or framework that does not allow this weakness to occur or provides constructs that make this weakness easier to avoid.
• Use libraries or frameworks that make it easier to handle numbers without unexpected consequences.
• Examples include safe integer handling packages such as SafeInt (C++) or IntegerLib (C or C++). [REF-106]
• Perform input validation on any numeric input by ensuring that it is within the expected range. Enforce that the input meets both the minimum and maximum requirements for the expected range.
• Use unsigned integers where possible. This makes it easier to perform validation for integer overflows. When signed integers are required, ensure that the range check includes minimum values as well as maximum values.
• Understand the programming language’s underlying representation and how it interacts with numeric calculation (CWE-681). Pay close attention to byte size discrepancies, precision, signed/unsigned distinctions, truncation, conversion and casting between types, “not-a-number” calculations, and how the language handles numbers that are too large or too small for its underlying representation. [REF-7]
• Also be careful to account for 32-bit, 64-bit, and other potential differences that may affect the numeric representation.

Related Attack Patterns

• https://cwe.mitre.org/data/definitions/92.html

References

• https://github.com/tensorflow/tensorflow/commit/69c68ecbb24dff3fa0e46da0d16c821a2dd22d7c
• https://github.com/tensorflow/tensorflow/security/advisories/GHSA-2cpx-427x-q2c6