CWE-617

TensorFlow is an open source platform for machine learning. If `tf.raw_ops.TensorListResize` is given a nonscalar value for input `size`, it results `CHECK` fail which can be used to trigger a denial of service attack. We have patched the issue in GitHub commit 888e34b49009a4e734c27ab0c43b0b5102682c56. The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, as these are also affected and still in supported range.

JasPer 3.0.6 allows denial of service via a reachable assertion in the function inttobits in libjasper/base/jas_image.c.

This issue can affect BIND 9 resolvers with `stale-answer-enable yes;` that also make use of the option `stale-answer-client-timeout`, configured with a value greater than zero. If the resolver receives many queries that require recursion, there will be a corresponding increase in the number of clients that are waiting for recursion to complete. If there are sufficient clients already waiting when a new client query is received so that it is necessary to SERVFAIL the longest waiting client (see BIND 9 ARM `recursive-clients` limit and soft quota), then it is possible for a race to occur between providing a stale answer to this older client and sending an early timeout SERVFAIL, which may cause an assertion failure. This issue affects BIND 9 versions 9.16.12 through 9.16.36, 9.18.0 through 9.18.10, 9.19.0 through 9.19.8, and 9.16.12-S1 through 9.16.36-S1.

LIEF commit 365a16a was discovered to contain a reachable assertion abort via the component BinaryStream.hpp.

TensorFlow is an open source platform for machine learning. The implementation of `AvgPoolGrad` does not fully validate the input `orig_input_shape`. This results in a `CHECK` failure which can be used to trigger a denial of service attack. We have patched the issue in GitHub commit 3a6ac52664c6c095aa2b114e742b0aa17fdce78f. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue.