PT-2026-59875

Impact The implementation of tf.histogram fixed width is vulnerable to a crash when the values array contain NaN elements: python import tensorflow as tf import numpy as np tf.histogram fixed widthvalues=np.nan, value range=1,2 The implementation assumes that all floating point operations are...

PT-2026-59923

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-2026-59913

Impact Certain TFLite models that were created using TFLite model converter would crash when loaded in the TFLite interpreter. The culprit is that during quantization the scale of values could be greater than 1 but code was always assuming sub-unit scaling. Thus, since code was calling...

PT-2026-59857

Impact The estimator for the cost of some convolution operations can be made to execute a division by 0: python import tensorflow as tf @tf.function def test: y=tf.raw ops.AvgPoolGrad orig input shape=1,1,1,1, grad=1.0,1.0,1.0,2.0,2.0,2.0,3.0,3.0,3.0, ksize=1,1,1,1, strides=1,1,1,0,...

PT-2026-59842

Impact A GraphDef from a TensorFlow SavedModel can be maliciously altered to cause a TensorFlow process to crash due to encountering a StatusOr value that is an error and forcibly extracting the value from it: cc if op reg data-type ctor != nullptr VLOG3 op def; const FullTypeDef ctor typedef =...

PT-2026-59831

Impact The implementation of tf.raw ops.SparseTensorToCSRSparseMatrix 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 indices = tf.constant53, shape=3, dtype=tf.int64 values =...

PT-2026-59777

Impact The implementation of OpLevelCostEstimator::CalculateTensorSize is vulnerable to an integer overflow if an attacker can create an operation which would involve a tensor with large enough number of elements: cc int64 t OpLevelCostEstimator::CalculateTensorSize const OpInfo::TensorProperties...

PT-2026-59714

Impact An attacker can craft a TFLite model that would trigger a division by zero in the implementation of depthwise convolutions. The parameters of the convolution can be user controlled and are also used within a division operation to determine the size of the padding that needs to be added...

PT-2026-59726

Impact The tf.compat.v1.signal.rfft2d and tf.compat.v1.signal.rfft3d lack input validation and under certain condition can result in crashes due to CHECK-failures. Patches We have patched the issue in GitHub commit 0a8a781e597b18ead006d19b7d23d0a369e9ad73 merging GitHub PR 55274. The fix will be...

PT-2026-59707

Impact The implementation of tf.raw ops.EditDistance has incomplete validation. Users can pass negative values to cause a segmentation fault based denial of service: python import tensorflow as tf hypothesis indices = tf.constant-1250999896764, shape=3, 3, dtype=tf.int64 hypothesis values =...

PT-2026-59708

Impact The implementation of tf.raw ops.LSTMBlockCell 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.LSTMBlockCell x=tf.constant0.837607, shape=28,29, dtype=tf.float32,...

PT-2026-59711

Impact When Conv2DBackpropInput receives empty out backprop inputs e.g. 3, 1, 0, 1, the current CPU/GPU kernels CHECK fail one with dnnl, the other with cudnn. This can be used to trigger a denial of service attack. python import tensorflow as tf import numpy as np input sizes = 3, 1, 1, 2 filter...

PT-2026-59704

Impact A malicious user can cause a use after free behavior when decoding PNG images: cc if / ... error conditions ... / png::CommonFreeDecode&decode; OP REQUIREScontext, false, errors::InvalidArgument"PNG size too large for int: ", decode.width, " by ", decode.height; After...

PT-2026-59728

Impact TensorFlow is vulnerable to a heap OOB write in Grappler: cc Status SetUnknownShapeconst NodeDef node, int output port shape inference::ShapeHandle shape = GetUnknownOutputShapenode, output port; InferenceContext ctx = GetContextnode; if ctx == nullptr return errors::InvalidArgument"Missin...

PT-2026-59727

Impact The Grappler optimizer in TensorFlow can be used to cause a denial of service by altering a SavedModel such that SafeToRemoveIdentity would trigger CHECK failures. Patches We have patched the issue in GitHub commit 92dba16749fae36c246bec3f9ba474d9ddeb7662. The fix will be included in...

PT-2026-59701

Impact The implementation of Dequantize does not fully validate the value of axis and can result in heap OOB accesses: python import tensorflow as tf @tf.function def test: y = tf.raw ops.Dequantize input=tf.constant1,1,dtype=tf.qint32, min range=1.0, max range=10.0, mode='MIN COMBINED', narrow...

PT-2026-59706

Impact The implementation of tf.raw ops.TensorSummaryV2 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 numpy as np import tensorflow as tf tf.raw ops.TensorSummaryV2 tag=np.array'test',...

PT-2026-59884

Impact When Conv2DBackpropInput receives empty out backprop inputs e.g. 3, 1, 0, 1, the current CPU/GPU kernels CHECK fail one with dnnl, the other with cudnn. This can be used to trigger a denial of service attack. python import tensorflow as tf import numpy as np input sizes = 3, 1, 1, 2 filter...

PT-2026-59881

Impact The implementation of tf.raw ops.EditDistance has incomplete validation. Users can pass negative values to cause a segmentation fault based denial of service: python import tensorflow as tf hypothesis indices = tf.constant-1250999896764, shape=3, 3, dtype=tf.int64 hypothesis values =...

PT-2026-59895

Impact Multiple TensorFlow operations misbehave in eager mode when the resource handle provided to them is invalid: python import tensorflow as tf tf.raw ops.QueueIsClosedV2handle= python import tensorflow as tf tf.summary.flushwriter= In graph mode, it would have been impossible to perform these...