TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation for tf.raw_ops.FractionalAvgPoolGrad can be tricked into accessing data outside of bounds of heap allocated buffers. The implementation does not validate that the input tensor is non-empty. Thus, code constructs an empty EigenDoubleMatrixMap and then accesses this buffer with indices that are outside of the empty area. We have patched the issue in GitHub commit 0f931751fb20f565c4e94aa6df58d54a003cdb30. 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 reads data past the end, or before the beginning, of the intended buffer.
Affected Software
| Name |
Vendor |
Start Version |
End Version |
| Tensorflow |
Google |
2.3.0 (including) |
2.3.4 (excluding) |
| Tensorflow |
Google |
2.4.0 (including) |
2.4.3 (excluding) |
| Tensorflow |
Google |
2.5.0 (including) |
2.5.0 (including) |
| Tensorflow |
Google |
2.6.0-rc0 (including) |
2.6.0-rc0 (including) |
| Tensorflow |
Google |
2.6.0-rc1 (including) |
2.6.0-rc1 (including) |
| Tensorflow |
Google |
2.6.0-rc2 (including) |
2.6.0-rc2 (including) |
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.
- To reduce the likelihood of introducing an out-of-bounds read, ensure that you validate and ensure correct calculations for any length argument, buffer size calculation, or offset. Be especially careful of relying on a sentinel (i.e. special character such as NUL) in untrusted inputs.
References