CVE-2021-37677

TensorFlow is an end-to-end open source platform for machine learning. In affected versions the shape inference code for tf.raw_ops.Dequantize has a vulnerability that could trigger a denial of service via a segfault if an attacker provides invalid arguments. The shape inference implementation uses axis to select between two different values for minmax_rank which is then used to retrieve tensor dimensions. However, code assumes that axis can be either -1 or a value greater than -1, with no validation for the other values. We have patched the issue in GitHub commit da857cfa0fde8f79ad0afdbc94e88b5d4bbec764. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.

Weakness

The product receives input that is expected to specify a quantity (such as size or length), but it does not validate or incorrectly validates that the quantity has the required properties.

Affected Software

Extended Description

Specified quantities include size, length, frequency, price, rate, number of operations, time, and others. Code may rely on specified quantities to allocate resources, perform calculations, control iteration, etc. When the quantity is not properly validated, then attackers can specify malicious quantities to cause excessive resource allocation, trigger unexpected failures, enable buffer overflows, etc.

Potential Mitigations

• Assume all input is malicious. Use an “accept known good” input validation strategy, i.e., use a list of acceptable inputs that strictly conform to specifications. Reject any input that does not strictly conform to specifications, or transform it into something that does.
• When performing input validation, consider all potentially relevant properties, including length, type of input, the full range of acceptable values, missing or extra inputs, syntax, consistency across related fields, and conformance to business rules. As an example of business rule logic, “boat” may be syntactically valid because it only contains alphanumeric characters, but it is not valid if the input is only expected to contain colors such as “red” or “blue.”
• Do not rely exclusively on looking for malicious or malformed inputs. This is likely to miss at least one undesirable input, especially if the code’s environment changes. This can give attackers enough room to bypass the intended validation. However, denylists can be useful for detecting potential attacks or determining which inputs are so malformed that they should be rejected outright.

References

• https://github.com/tensorflow/tensorflow/commit/da857cfa0fde8f79ad0afdbc94e88b5d4bbec764
• https://github.com/tensorflow/tensorflow/security/advisories/GHSA-qfpc-5pjr-mh26