Integer overflows in Tensorflow

Impact The implementation of AddManySparseToTensorsMap is vulnerable to an integer overflow which results in a CHECK-fail when building new TensorShape objects so, an assert failure based denial of service:pythonimport tensorflow as tfimport numpy as nptf.rawops.AddManySparseToTensorsMap...

PYSEC-2026-3240 Integer overflows in Tensorflow

Impact The implementations of SparseCwise ops are vulnerable to integer overflows. These can be used to trigger large allocations so, OOM based denial of service or CHECK-fails when building new TensorShape objects so, assert failures based denial of service: python import tensorflow as tf import...

Integer overflows in Tensorflow

Impact The implementations of SparseCwise ops are vulnerable to integer overflows. These can be used to trigger large allocations so, OOM based denial of service or CHECK-fails when building new TensorShape objects so, assert failures based denial of service:pythonimport tensorflow as tfimport...

PYSEC-2026-3241 Crash when type cannot be specialized in Tensorflow

Impact Under certain scenarios, TensorFlow can fail to specialize a type during shape inference: cc void InferenceContext::PreInputInit const OpDef& opdef, const std::vector& inputtensors, const std::vector& inputtensorsasshapes const auto ret = fulltype::SpecializeTypeattrs, opdef;...

PYSEC-2026-3204 Reachable Assertion in Tensorflow

Impact When decoding a tensor from protobuf, a TensorFlow process can encounter cases where a CHECK assertion is invalidated based on user controlled arguments, if the tensors have an invalid dtype and 0 elements or an invalid shape. This allows attackers to cause denial of services in TensorFlow...

Crash when type cannot be specialized in Tensorflow

ImpactUnder certain scenarios, TensorFlow can fail to specialize a type during shape inference:ccvoid InferenceContext::PreInputInit const OpDef& opdef, const std::vector& inputtensors, const std::vector& inputtensorsasshapes const auto ret = fulltype::SpecializeTypeattrs, opdef;...

Out of bounds read in Tensorflow

Impact The implementation of shape inference for ReverseSequence does not fully validate the value of batchdim and can result in a heap OOB read:pythonimport tensorflow as [email protected] test: y = tf.rawops.ReverseSequence input = 'aaa','bbb', seqlengths = 1,1,1, seqdim = -10, batchdim = -10...

PYSEC-2026-3121 Out of bounds read in Tensorflow

Impact The implementation of shape inference for ReverseSequence does not fully validate the value of batchdim and can result in a heap OOB read: python import tensorflow as tf @tf.function def test: y = tf.rawops.ReverseSequence input = 'aaa','bbb', seqlengths = 1,1,1, seqdim = -10, batchdim = -...

PYSEC-2026-3159 Integer overflow in Tensorflow

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.rawops.Dequantize input=input, minrange=1.0, maxrange=10.0, mode='MINCOMBINED',...

PYSEC-2026-3113 Abort caused by allocating a vector that is too large in Tensorflow

Impact During shape inference, TensorFlow can allocate a large vector based on a value from a tensor controlled by the user: cc const auto numdims = Valueshapedim; std::vector dims; dims.reservenumdims; Patches We have patched the issue in GitHub commit 1361fb7e29449629e1df94d44e0427ebec8c83c7. T...

Integer overflow in Tensorflow

Impact The implementation of shape inference for Dequantize is vulnerable to an integer overflow weakness:pythonimport tensorflow as tfinput = tf.constant1,1,[email protected] test: y = tf.rawops.Dequantize input=input, minrange=1.0, maxrange=10.0, mode='MINCOMBINED',...

Abort caused by allocating a vector that is too large in Tensorflow

ImpactDuring shape inference, TensorFlow can allocate a large vector based on a value from a tensor controlled by the user:cc const auto numdims = Valueshapedim; std::vector dims; dims.reservenumdims; Patches We have patched the issue in GitHub commit 1361fb7e29449629e1df94d44e0427ebec8c83c7.The...

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-2026-59776

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-2026-59732

Impact During shape inference, TensorFlow can allocate a large vector based on a value from a tensor controlled by the user: cc const auto num dims = Valueshape dim; std::vector dims; dims.reservenum dims; Patches We have patched the issue in GitHub commit 1361fb7e29449629e1df94d44e0427ebec8c83c7...

PT-2026-59739

Impact The implementation of shape inference for ReverseSequence does not fully validate the value of batch dim and can result in a heap OOB read: python import tensorflow as tf @tf.function def test: y = tf.raw ops.ReverseSequence input = 'aaa','bbb', seq lengths = 1,1,1, seq dim = -10, batch di...

PT-2026-59828

Impact The implementation of shape inference for ConcatV2 can be used to trigger a denial of service attack via a segfault caused by a type confusion: python import tensorflow as tf @tf.function def test: y = tf.raw ops.ConcatV2 values=1,2,3,4,5,6, axis = 0xb500005b return y test The axis argumen...

PYSEC-2026-2019 vLLM vulnerable to DoS with incorrect shape of multimodal embedding inputs

Summary Users can crash the vLLM engine serving multimodal models by passing multimodal embedding inputs with correct ndim but incorrect shape e.g. hidden dimension is wrong, regardless of whether the model is intended to support such inputs as defined in the Supported Models page. The issue has...

vLLM vulnerable to DoS with incorrect shape of multimodal embedding inputs

SummaryUsers can crash the vLLM engine serving multimodal models by passing multimodal embedding inputs with correct ndim but incorrect shape e.g. hidden dimension is wrong, regardless of whether the model is intended to support such inputs as defined in the Supported Models page.The issue has...

PYSEC-2026-1959 TensorFlow vulnerable to Out-of-Bounds Read in DynamicStitch

Impact If the parameter indices for DynamicStitch does not match the shape of the parameter data, it can trigger an stack OOB read. python import tensorflow as tf func = tf.rawops.DynamicStitch para='indices': 0xdeadbeef, 405, 519, 758, 1015, 'data': 110.27793884277344, 120.29475402832031,...