The implementation of tf.math.segment_*
operations results in a CHECK
-fail related abort (and denial of service) if a segment id in segment_ids
is large.
import tensorflow as tf
tf.math.segment_max(data=np.ones((1,10,1)), segment_ids=[1676240524292489355])
tf.math.segment_min(data=np.ones((1,10,1)), segment_ids=[1676240524292489355])
tf.math.segment_mean(data=np.ones((1,10,1)), segment_ids=[1676240524292489355])
tf.math.segment_sum(data=np.ones((1,10,1)), segment_ids=[1676240524292489355])
tf.math.segment_prod(data=np.ones((1,10,1)), segment_ids=[1676240524292489355])
This is similar to CVE-2021-29584 (and similar other reported vulnerabilities in TensorFlow, localized to specific APIs): the implementation (both on CPU and GPU) computes the output shape using AddDim
. However, if the number of elements in the tensor overflows an int64_t
value, AddDim
results in a CHECK
failure which provokes a std::abort
. Instead, code should use AddDimWithStatus
.
We have patched the issue in GitHub commit e9c81c1e1a9cd8dd31f4e83676cab61b60658429 (merging #51733).
The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.
Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.
This vulnerability has been reported externally via a GitHub issue.
{ "nvd_published_at": "2021-11-05T20:15:00Z", "cwe_ids": [ "CWE-190" ], "severity": "MODERATE", "github_reviewed": true, "github_reviewed_at": "2021-11-08T22:57:24Z" }