CVE-2022-36026: TensorFlow is an open source platform for machine learning. If `QuantizeAndDequantizeV3` is given a nonscalar `num_bits`
Summary
TensorFlow, an open source platform for machine learning, has a vulnerability in its `QuantizeAndDequantizeV3` function where passing a nonscalar `num_bits` input tensor (a multi-dimensional array instead of a single value) causes the program to crash, which can be exploited for a denial of service attack (making a service unavailable by overwhelming or crashing it). The issue affects multiple TensorFlow versions.
Solution / Mitigation
The issue has been patched in GitHub commit f3f9cb38ecfe5a8a703f2c4a8fead434ef291713. The fix will be included in TensorFlow 2.10.0 and will be backported to TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2. No workarounds are available; users should update to these patched versions.
Vulnerability Details
5.9(medium)
EPSS: 0.1%
Classification
Taxonomy References
Affected Vendors
Related Issues
CVE-2026-47482: NVIDIA Triton Inference Server for Linux contains a vulnerability where an attacker can cause missing release of memory
CVE-2022-29200: TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the implem
Original source: https://nvd.nist.gov/vuln/detail/CVE-2022-36026
First tracked: February 15, 2026 at 08:41 PM
Classified by LLM (prompt v3) · confidence: 95%