CVE-2021-29591: Stack overflow due to looping TFLite subgraph
Impact TFlite graphs must not have loops between nodes. However, this condition was not checked and an attacker could craft models that would result in infinite loop during evaluation. In certain cases, the infinite loop would be replaced by stack overflow due to too many recursive calls.
For example, the While implementation could be tricked into a scneario where both the body and the loop subgraphs are the same. Evaluating one of the subgraphs means calling the Eval function for the other and this quickly exhaust all stack space. Patches We have patched the issue in GitHub commit 9c1dc920d8ffb4893d6c9d27d1f039607b326743 (for the While operator) and in GitHub commit c6173f5fe66cdbab74f4f869311fe6aae2ba35f4 (in general). 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.
For more information Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.
Attribution This vulnerability has been reported by members of the Aivul Team from Qihoo 360.
Other sources
TensorFlow is an end-to-end open source platform for machine learning. TFlite graphs must not have loops between nodes. However, this condition was not checked and an attacker could craft models that would result in infinite loop during evaluation. In certain cases, the infinite loop would be replaced by stack overflow due to too many recursive calls. For example, the While implementation(https://github.com/tensorflow/tensorflow/blob/106d8f4fb89335a2c52d7c895b7a7485465ca8d9/tensorflow/lite/kernels/while.cc) could be tricked into a scneario where both the body and the loop subgraphs are the same. Evaluating one of the subgraphs means calling the Eval function for the other and this quickly exhaust all stack space. 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. Please consult our security guide(https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
Affected Software
Remediation
Event History
Frequently Asked Questions
What is the severity of CVE-2021-29591?
The severity of CVE-2021-29591 is not specified in the initial reports, but it poses a significant risk due to potential infinite loops or stack overflow during evaluation.
How do I fix CVE-2021-29591?
To fix CVE-2021-29591, users should upgrade to TensorFlow version 2.4.2, 2.3.3, 2.2.3, or 2.1.4 depending on the current version used.
Which versions of TensorFlow are affected by CVE-2021-29591?
CVE-2021-29591 affects TensorFlow versions below 2.4.2, specifically versions 2.1.4 and between 2.2.0 to 2.2.3, 2.3.0 to 2.3.3.
What kind of impact does CVE-2021-29591 have on TensorFlow users?
CVE-2021-29591 can allow crafted models to cause infinite loops during evaluation, leading to performance degradation or application crashes.
Is there a way to detect if I'm vulnerable to CVE-2021-29591?
Users can check their TensorFlow version against the fixed versions to determine if they are vulnerable to CVE-2021-29591.