PT-2026-59785

Impact The implementation of tf.reshape op in TensorFlow is vulnerable to a denial of service via CHECK-failure assertion failure caused by overflowing the number of elements in a tensor: python import tensorflow as tf tf.reshapetensor=1,shape=tf.constant0 for i in range255, dtype=tf.int64 This i...

PT-2026-59765

Impact An attacker can craft a TFLite model that would cause a write outside of bounds of an array in TFLite. In fact, the attacker can override the linked list used by the memory allocator. This can be leveraged for an arbitrary write primitive under certain conditions. Patches We have patched t...

PT-2026-59792

Impact The Grappler optimizer in TensorFlow can be used to cause a denial of service by altering a SavedModel such that IsSimplifiableReshape would trigger CHECK failures. Patches We have patched the issue in GitHub commits ebc1a2ffe5a7573d905e99bd0ee3568ee07c12c1,...

PT-2026-59810

Impact The simplifyBroadcast function in the MLIR-TFRT infrastructure in TensorFlow is vulnerable to a segfault hence, denial of service, if called with scalar shapes. cc size t maxRank = 0; for auto shape : llvm::enumerateshapes auto found shape = analysis.dimensionsForShapeTensorshape.value; if...

PT-2026-59796

Impact The implementation of tf.raw ops.GetSessionTensor 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 handle = tf.constant"", shape=0, dtype=tf.string tf.raw...

PT-2026-59841

Impact The implementation of tf.raw ops.QuantizedConv2D does not fully validate the input arguments: python import tensorflow as tf input = tf.constant1, shape=1, 2, 3, 3, dtype=tf.quint8 filter = tf.constant1, shape=1, 2, 3, 3, dtype=tf.quint8 bad args min input = tf.constant, shape=0,...

PT-2026-59817

Impact The TensorKey hash function used total estimated AllocatedBytes, which a is an estimate per tensor, and b is a very poor hash function for constants e.g. int32 t. It also tried to access individual tensor bytes through tensor.data of size AllocatedBytes. This led to ASAN failures because t...

PT-2026-59933

Impact The ScatterNd function takes an input argument that determines the indices of of the output tensor. An input index greater than the output tensor or less than zero will either write content at the wrong index or trigger a crash. Patches We have patched the issue in GitHub commit...

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-2026-59755

Impact If a graph node is invalid, TensorFlow can leak memory in the implementation of ImmutableExecutorState::Initialize: cc Status s = params .create kerneln-properties, &item-kernel; if !s.ok item-kernel = nullptr; s = AttachDefs, n; return s; Here, we set item-kernel to nullptr but it is a...

PT-2026-59790

Impact When building an XLA compilation cache, if default settings are used, TensorFlow triggers a null pointer dereference: cc string allowed gpus = flr-config proto-gpu options.visible device list; In the default scenario, all devices are allowed, so flr-config proto is nullptr. Patches We have...

PT-2026-59713

Impact The implementation of QuantizedMaxPool has an undefined behavior where user controlled inputs can trigger a reference binding to null pointer. python import tensorflow as tf tf.raw ops.QuantizedMaxPool input = tf.constant4, dtype=tf.quint8, min input = , max input = 1, ksize = 1, 1, 1, 1,...

PT-2026-59763

Impact An attacker can craft a TFLite model that would cause an integer overflow in embedding lookup operations: cc int embedding size = 1; int lookup size = 1; for int i = 0; i data.i32i; lookup size = dim; output shape-datak = dim; for int i = 1; i datak = dim; Both embedding size and lookup si...

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-2026-59761

Impact The implementation of SobolSampleOp is vulnerable to a denial of service via CHECK-failure assertion failure caused by assuming input0, input1, and input2 to be scalar. python import tensorflow as tf tf.raw ops.SobolSampledim=tf.constant1,0, num results=tf.constant1, skip=tf.constant1...

PT-2026-59781

Impact The implementation of Bincount operations allows malicious users to cause denial of service by passing in arguments which would trigger a CHECK-fail: python import tensorflow as tf tf.raw ops.DenseBincount input=0, 1, 2, size=1, weights=3,2,1, binary output=False There are several conditio...

PT-2026-59813

Impact The implementation of tf.raw ops.StagePeek 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 index = tf.constant, shape=0, dtype=tf.int32 tf.raw ops.StagePeekindex=index,...

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-2026-59856

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-2026-59804

Impact The implementation of MapStage is vulnerable a CHECK-fail if the key tensor is not a scalar: python import tensorflow as tf import numpy as np tf.raw ops.MapStage key = tf.constantvalue=4, shape= 1,2, dtype=tf.int64, indices = np.array6, values = np.array-60, dtypes = tf.int64, capacity=0,...