PT-2026-59856

Impact Under certain scenarios, TensorFlow can fail to specialize a type during shape inference: cc void InferenceContext::PreInputInit const OpDef& op def, const std::vector& input tensors, const std::vector& input tensors as shapes const auto ret = full type::SpecializeTypeattrs , op def;...

PT-2026-59756

Impact When decoding a resource handle tensor from protobuf, a TensorFlow process can encounter cases where a CHECK assertion is invalidated based on user controlled arguments. This allows attackers to cause denial of services in TensorFlow processes. Patches We have patched the issue in GitHub...

PT-2026-59851

Impact The implementation of tf.raw ops.SparseTensorDenseAdd does not fully validate the input arguments: python import tensorflow as tf a indices = tf.constant0, shape=17, 2, dtype=tf.int64 a values = tf.constant, shape=0, dtype=tf.float32 a shape = tf.constant6, 12, shape=2, dtype=tf.int64 b =...

PT-2026-59774

Impact The TFG dialect of TensorFlow MLIR makes several assumptions about the incoming GraphDef before converting it to the MLIR-based dialect. If an attacker changes the SavedModel format on disk to invalidate these assumptions and the GraphDef is then converted to MLIR-based IR then they can...

PT-2026-59776

Impact The implementation of shape inference for Dequantize is vulnerable to an integer overflow weakness: python import tensorflow as tf input = tf.constant1,1,dtype=tf.qint32 @tf.function def test: y = tf.raw ops.Dequantize input=input, min range=1.0, max range=10.0, mode='MIN COMBINED', narrow...

PT-2026-59812

Impact The implementation of tf.raw ops.QuantizeAndDequantizeV4Grad does not fully validate the input arguments. This results in a CHECK-failure which can be used to trigger a denial of service attack: python import tensorflow as tf tf.raw ops.QuantizeAndDequantizeV4Grad gradients=tf.constant1,...

PT-2026-59754

Impact Under certain scenarios, Grappler component of TensorFlow is vulnerable to an integer overflow during cost estimation for crop and resize. Since the cropping parameters are user controlled, a malicious person can trigger undefined behavior. Patches We have patched the issue in GitHub commi...

PT-2026-59948

Impact The implementation of tf.raw ops.QuantizeAndDequantizeV4Grad does not fully validate the input arguments. This results in a CHECK-failure which can be used to trigger a denial of service attack: python import tensorflow as tf tf.raw ops.QuantizeAndDequantizeV4Grad gradients=tf.constant1,...

cogdata (>=0.0.6, <=0.0.7), icetk (>=0.0.5, <=0.0.7) +3 more potentially affected by CVE-2026-0994 via protobuf (3.19.0rc2)

protobuf PYPI version 3.19.0rc2 is affected by a known vulnerability. The following packages have a transitive dependency on protobuf and may be impacted: - cogdata =0.0.6, =0.0.5, =3.0.0a2, =4.7.2 - motion-master-proto 5.2.0 - tensorflow-checkpoint-reader 0.1.2 Source cves: CVE-2026-0994 Source...

hydra-rbp (>=0.1.21.29, <=0.1.21.38), phasespace (>=1.5.0, <=1.6.0) +4 more potentially affected by CVE-2025-12060 via keras (2.7.0rc2)

keras PYPI version 2.7.0rc2 is affected by a known vulnerability. The following packages have a transitive dependency on keras and may be impacted: - hydra-rbp =0.1.21.29, =1.5.0, =2.6.0rc1, =0.18.0.dev20210402154254, =0.3.0, =0.3.2 Source cves: CVE-2025-12060 Source advisory: OSV:PYSEC-2026-1486...

tensorflow (2.6.0rc0) potentially affected by CVE-2025-12060 via keras (2.6.0rc0)

keras PYPI version 2.6.0rc0 is affected by a known vulnerability. The following packages have a transitive dependency on keras and may be impacted: - tensorflow 2.6.0rc0 Source cves: CVE-2025-12060 Source advisory: OSV:PYSEC-2026-1486...

Hugging Face Transformers Regular Expression Denial of Service (ReDoS) vulnerability

A Regular Expression Denial of Service ReDoS vulnerability exists in the Hugging Face Transformers library, specifically in the converttfweightnametoptweightname function. This function, responsible for converting TensorFlow weight names to PyTorch format, uses a regex pattern /^/^// that can be...

cogdata (>=0.0.6, <=0.0.7), icetk (>=0.0.5, <=0.0.7) +3 more potentially affected by CVE-2025-4565 via protobuf (3.19.0rc2)

protobuf PYPI version 3.19.0rc2 is affected by a known vulnerability. The following packages have a transitive dependency on protobuf and may be impacted: - cogdata =0.0.6, =0.0.5, =3.0.0a2, =4.7.2 - motion-master-proto 5.2.0 - tensorflow-checkpoint-reader 0.1.2 Source cves: CVE-2025-4565 Source...

TensorFlow has segfault in array_ops.upper_bound

Impactarrayops.upperbound causes a segfault when not given a rank 2 tensor. PatchesWe have patched the issue in GitHub commit 915884fdf5df34aaedd00fc6ace33a2cfdefa586.The fix will be included in TensorFlow 2.13. We will also cherrypick this commit in TensorFlow 2.12.1. For more informationPlease...

PYSEC-2026-1962 TensorFlow has segfault in array_ops.upper_bound

Impact arrayops.upperbound causes a segfault when not given a rank 2 tensor. Patches We have patched the issue in GitHub commit 915884fdf5df34aaedd00fc6ace33a2cfdefa586. The fix will be included in TensorFlow 2.13. We will also cherrypick this commit in TensorFlow 2.12.1. For more information...

PYSEC-2026-1954 Invalid char to bool conversion when printing a tensor

Impact When printing a tensor, we get it's data as a const char array since that's the underlying storage and then we typecast it to the element type. However, conversions from char to bool are undefined if the char is not 0 or 1, so sanitizers/fuzzers will crash. Patches We have patched the issu...

Invalid char to bool conversion when printing a tensor

ImpactWhen printing a tensor, we get it's data as a const char array since that's the underlying storage and then we typecast it to the element type. However, conversions from char to bool are undefined if the char is not 0 or 1, so sanitizers/fuzzers will crash. PatchesWe have patched the issue ...

PYSEC-2026-1654 MLFlow unsafe deserialization

Deserialization of untrusted data can occur in versions of the MLflow platform running version 2.0.0rc0 or newer, enabling a maliciously uploaded Tensorflow model to run arbitrary code on an end user’s system when interacted with...

MLFlow unsafe deserialization

Deserialization of untrusted data can occur in versions of the MLflow platform running version 2.0.0rc0 or newer, enabling a maliciously uploaded Tensorflow model to run arbitrary code on an end user’s system when interacted with...

Authenticated Local Privilege Escalation vulnerability in Intel Optimization for Tensorflow

Improper buffer restrictions in the IntelR Optimization for Tensorflow software before version 2.12 may allow an authenticated user to potentially enable escalation of privilege via local access...