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CVE-2021-37679
CVE ID
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CVE-2021-37679
MITRE ATT&CK
Collection
Command and Control
Credential Access
Defense Evasion
Discovery
Execution
Exfiltration
Impact
Initial Access
Lateral Movement
Persistence
Privilege Escalation
Topic Tags
Description
TensorFlow is an end-to-end open source platform for machine learning. In affected versions it is possible to nest a tf.map_fn within another tf.map_fn call. However, if the input tensor is a RaggedTensor and there is no function signature provided, code assumes the output is a fully specified tensor and fills output buffer with uninitialized contents from the heap. The t and z outputs should be identical, however this is not the case. The last row of t contains data from the heap which can be used to leak other memory information. The bug lies in the conversion from a Variant tensor to a RaggedTensor. The implementation does not check that all inner shapes match and this results in the additional dimensions. The same implementation can result in data loss, if input tensor is tweaked. We have patched the issue in GitHub commit 4e2565483d0ffcadc719bd44893fb7f609bb5f12. 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.
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CVSS V3 Severity and Metrics
General Information
Vendors
Products
- tensorflow,
- tensorflow 2.5.0,
- tensorflow 2.6.0
References
Additional Info
Technical Analysis
Report as Exploited in the Wild
CVE ID
AttackerKB requires a CVE ID in order to pull vulnerability data and references from the CVE list and the National Vulnerability Database. If available, please supply below:
