tensorflow-gpu@2.8.0 vulnerabilities

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Heap-based Buffer Overflow. Attackers can access heap memory which is not in the control of user, leading to a crash or remote code execution. The fix will be included in TensorFlow version 2.12.0 and will also cherrypick this commit on TensorFlow version 2.11.1.

Upgrade tensorflow-gpu to version 2.12.0 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to NULL Pointer Dereference when SparseSparseMaximum is given invalid sparse tensors as inputs.

Upgrade tensorflow-gpu to version 2.12.0 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) when running with XLA, tf.raw_ops.ParallelConcat segfaults with a nullptr dereference when given a parameter shape with rank that is not greater than zero.

Upgrade tensorflow-gpu to version 2.12.0 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to NULL Pointer Dereference due to a null pointer error in RandomShuffle with XLA enabled.

Upgrade tensorflow-gpu to version 2.12.0 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) due to a floating point exception in TensorListSplit with XLA.

Upgrade tensorflow-gpu to version 2.12.0 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to NULL Pointer Dereference. The function tf.raw_ops.LookupTableImportV2 cannot handle scalars in the values parameter and gives a null pointer exception.

Upgrade tensorflow-gpu to version 2.12.0 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Incorrect Comparison. Constructing a tflite model with a paramater filter_input_channel of less than 1 gives a float pointer exception.

Upgrade tensorflow-gpu to version 2.12.0 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS). When running with XLA, tf.raw_ops.Bincount segfaults when given a parameter weights that is neither the same shape as parameter arr nor a length-0 tensor.

Upgrade tensorflow-gpu to version 2.12.0 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Buffer Overflow in TAvgPoolGrad.

Upgrade tensorflow-gpu to version 2.12.0 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Integer Overflow to Buffer Overflow when 2^31 <= num_frames * height * width * channels < 2^32, for example Full HD screencast of at least 346 frames.

Upgrade tensorflow-gpu to version 2.12.0 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Integer Overflow or Wraparound in EditDistance. A fix is included in TensorFlow version 2.12.0 and version 2.11.1.

Upgrade tensorflow-gpu to version 2.12.0 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Out-of-Bounds due to mismatched integer type sizes in ValueMap::Manager::GetValueOrCreatePlaceholder, because there is a bug with the tfg-translate call to InitMlir.

Upgrade tensorflow-gpu to version 2.12.0 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) due to a floating point exception if the stride and window size are not positive for tf.raw_ops.AvgPoolGrad.

Upgrade tensorflow-gpu to version 2.12.0 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to NULL Pointer Dereference. When ctx->step_containter() is a null ptr, the Lookup function will be executed with a null pointer.

Upgrade tensorflow-gpu to version 2.12.0 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS). When the parameter summarize of tf.raw_ops.Print is zero, the new method SummarizeArray<bool> will reference to a nullptr, leading to a seg fault.

Upgrade tensorflow-gpu to version 2.12.0 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Double Free. The nn_ops.fractional_avg_pool_v2 and nn_ops.fractional_max_pool_v2 functions require the first and fourth elements of their parameter pooling_ratio to be equal to 1.0, as pooling on batch and channel dimensions is not supported.

Upgrade tensorflow-gpu to version 2.12.0 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to NULL Pointer Dereference in QuantizedMatMulWithBiasAndDequantize with MKL enabled.

Upgrade tensorflow-gpu to version 2.12.0 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) due to a floating point exception in AudioSpectrogram.

Upgrade tensorflow-gpu to version 2.12.0 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Out-of-bounds Read if the parameter indices for DynamicStitch does not match the shape of the parameter data.

Upgrade tensorflow-gpu to version 2.12.0 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Out-of-bounds Read in GRUBlockCellGrad.

Upgrade tensorflow-gpu to version 2.12.0 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) due to another discovered instance of CVE-2022-35991, in TensorListScatter and TensorListScatterV2 via non scalar inputs.

Upgrade tensorflow-gpu to version 2.8.4, 2.9.3, 2.10.1 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Heap-based Buffer Overflow in QuantizeAndDequantizeV2, via the MakeGrapplerFunctionItem function, if the inputs are greater than or equal to the sizes of outputs.

Upgrade tensorflow-gpu to version 2.8.4, 2.9.3, 2.10.1 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) due to another discovered instance of CVE-2022-35935 in SobolSample via assumed scalar inputs.

Upgrade tensorflow-gpu to version 2.8.4, 2.9.3, 2.10.1 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Out-of-bounds Write via the MakeGrapplerFunctionItem function, if the inputs given are greater than or equal to the sizes of the outputs.

Upgrade tensorflow-gpu to version 2.8.4, 2.9.3, 2.10.1 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Out-of-Bounds in DynamicStitch due to missing validation when it receives a differing number of inputs, such as when it is called with an indices size 1 and a data size 2.

Upgrade tensorflow-gpu to version 2.10.1, 2.11.0 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Always-Incorrect Control Flow Implementation when a numpy array is created with a shape such that one element is zero and the sum of others is a large number.

Upgrade tensorflow-gpu to version 2.8.4, 2.9.3, 2.10.1, 2.11.0 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Buffer Overflow via tf.raw_ops.ImageProjectiveTransformV2 when a large output shape is given.

Upgrade tensorflow-gpu to version 2.8.4, 2.9.3, 2.10.1, 2.11.0 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Incorrect Calculation of Buffer Size via tf.keras.losses.poisson which receives a y_pred and y_true that are passed through functor::mul in BinaryOp. If the resulting dimensions overflow an int32, TensorFlow will crash due to a size mismatch during broadcast assignment.

Upgrade tensorflow-gpu to version 2.9.3, 2.10.1, 2.11.0 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Out-of-bounds Read when the BaseCandidateSamplerOp function receives a value in true_classes larger than range_max.

Upgrade tensorflow-gpu to version 2.8.4, 2.9.3, 2.10.1, 2.11.0 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Buffer Overflow via tf.raw_ops.FusedResizeAndPadConv2D when a large tensor shape is given.

Upgrade tensorflow-gpu to version 2.8.4, 2.9.3, 2.10.1, 2.11.0 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Incorrect Calculation of Buffer Size when tf.raw_ops.ResizeNearestNeighborGrad is given a large size input.

Upgrade tensorflow-gpu to version 2.8.4, 2.9.3, 2.10.1, 2.11.0 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Improper Input Validation due to a missing check of tf.image.generate_bounding_box_proposals that receives a scores input that must be of rank 4 when running on GPU.

Upgrade tensorflow-gpu to version 2.8.4, 2.9.3, 2.10.1, 2.11.0 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) because the conversions from char to bool are undefined if the char is not 0 or 1. This can happen when printing a tensor: the data is got as a const char* array and then it is typecasted to the element type.

Upgrade tensorflow-gpu to version 2.8.4, 2.9.3, 2.10.1, 2.11.0 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) when ThreadUnsafeUnigramCandidateSampler is given input filterbank_channel_count greater than the allowed max size.

Upgrade tensorflow-gpu to version 2.8.4, 2.9.3, 2.10.1, 2.11.0 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Reachable Assertion when tf.raw_ops.TensorListResize is given a nonscalar value for input size. It will results in a CHECK fail which can be used to trigger a denial of service attack.

Upgrade tensorflow-gpu to version 2.8.4, 2.9.3, 2.10.1, 2.11.0 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Out-of-bounds Read. This is If MirrorPadGrad is given outsize input paddings.

Upgrade tensorflow-gpu to version 2.8.4, 2.9.3, 2.10.1, 2.11.0 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) when FractionMaxPoolGrad is given outsize inputs row_pooling_sequence and col_pooling_sequence.

Upgrade tensorflow-gpu to version 2.8.4, 2.9.3, 2.10.1, 2.11.0 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) when tf.raw_ops.TensorListConcat is given element_shape=[].

Upgrade tensorflow-gpu to version 2.8.4, 2.9.3, 2.10.1, 2.11.0 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) when BCast::ToShape is given input larger than an int32, even if it is being supposed to handle up to an int64.

Upgrade tensorflow-gpu to version 2.8.4, 2.9.3, 2.10.1, 2.11.0 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to NULL Pointer Dereference because the pywrap code fails to parse the tensor and returns a nullptr if a list of quantized tensors is assigned to an attribute.

Upgrade tensorflow-gpu to version 2.8.4, 2.9.3, 2.10.1, 2.11.0 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Buffer Overflow. The reference kernel of the CONV_3D_TRANSPOSE TensorFlow Lite operator wrongly increments the data_ptr when adding the bias to the result. Instead of data_ptr += num_channels; it should be data_ptr += output_num_channels; as if the number of input channels is different than the number of output channels, the wrong result will be returned and a buffer overflow will occur if num_channels > output_num_channels. An attacker can craft a model with a specific number of input channels. It is then possible to write specific values through the bias of the layer outside the bounds of the buffer.

Note: This attack only works if the reference kernel resolver is used in the interpreter.

Upgrade tensorflow-gpu to version 2.8.4, 2.9.3, 2.10.1, 2.11.0 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) when an input encoded is not a valid CompositeTensorVariant tensor. This will trigger a segfault in tf.raw_ops.CompositeTensorVariantToComponents.

Upgrade tensorflow-gpu to version 2.8.4, 2.9.3, 2.10.1, 2.11.0 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS). This is vulnerable when an input token that is not a UTF-8 bytestring will trigger a CHECK fail in tf.raw_ops.PyFunc.

Upgrade tensorflow-gpu to version 2.8.4, 2.9.3, 2.10.1, 2.11.0 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Out-of-bounds Write in FractionalMax(AVG)Pool with illegal pooling_ratio. Attackers can access heap memory that is not in the user's control, leading to a crash or remote code execution.

Upgrade tensorflow-gpu to version 2.8.4, 2.9.3, 2.10.1, 2.11.0 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) when SparseFillEmptyRowsGrad is given empty inputs.

Upgrade tensorflow-gpu to version 2.8.4, 2.9.3, 2.10.1, 2.11.0 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) when the input sparse_matrix is not a matrix with a shape with rank 0. As a result, a CHECK fail will be triggered in tf.raw_ops.SparseMatrixNNZ.

Upgrade tensorflow-gpu to version 2.8.4, 2.9.3, 2.10.1, 2.11.0 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS). This is due to the inputs dense_features or example_state_data not being of rank 2 which will trigger a CHECK fail in SdcaOptimizer.

Upgrade tensorflow-gpu to version 2.8.4, 2.9.3, 2.10.1, 2.11.0 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to NULL Pointer Dereference when mlir::tfg::ConvertGenericFunctionToFunctionDef is given empty function attributes.

Upgrade tensorflow-gpu to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) when FakeQuantWithMinMaxVarsPerChannel is given min or max tensors of a rank other than one, it results in a CHECK fail that can be used to trigger the exploitation.

Upgrade tensorflow-gpu to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) when mlir::tfg::GraphDefImporter::ConvertNodeDef tries to convert NodeDefs without an op name.

Upgrade tensorflow-gpu to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) when RandomPoissonV2 receives large input shapes and rates, it gives a CHECK fail that can trigger the exploitation.

Upgrade tensorflow-gpu to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) when tf.sparse.cross receives an input separator that is not a scalar, it gives a CHECK fail that can be used to trigger exploitation of this vulnerability.

Upgrade tensorflow-gpu to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) when Conv2DBackpropInput receives empty out_backprop inputs (e.g. [3, 1, 0, 1]).

Upgrade tensorflow-gpu to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) when AudioSummaryV2 receives an input sample_rate with more than one element, it gives a CHECK fails that can be used to trigger the exploitation of the vulnerability.

Upgrade tensorflow-gpu to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) when tensorflow::full_type::SubstituteFromAttrs receives a FullTypeDef& t that is not exactly three args.

Upgrade tensorflow-gpu to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) when tf.quantization.fake_quant_with_min_max_vars_gradient receives input min or max that is nonscalar, it gives a CHECK fail that can trigger the exploitation of this vulnerability.

Upgrade tensorflow-gpu to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) when QuantizeAndDequantizeV3 is given a nonscalar num_bits input tensor, it results in a CHECK fail that can be used to trigger the exploitation of this vulnerability.

Upgrade tensorflow-gpu to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) in tf.reshape due to a CHECK-failure caused by overflowing the number of elements in a tensor.

Upgrade tensorflow-gpu to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) when tf.random.gamma receives large input shapes and rates.

Upgrade tensorflow-gpu to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Out-of-bounds Write via the scatter_nd function in TF Lite, when an input index is greater than the output tensor or less than zero.

Upgrade tensorflow-gpu to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) via CHECK-failure caused by assuming input(0), input(1), and input(2) to be scalar.

Upgrade tensorflow-gpu to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Out-of-bounds Read. The GatherNd function takes arguments that determine the sizes of inputs and outputs. If the inputs given are greater than or equal to the sizes of the outputs, it triggers the exploitation of the vulnerability.

Upgrade tensorflow-gpu to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) when the AvgPoolOp function takes an argument ksize that must be positive but is not checked. A negative ksize can trigger a CHECK failure and crash the program.

Upgrade tensorflow-gpu to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) when RaggedTensorToVariant is given a rt_nested_splits list that contains tensors of ranks other than one, it results in a CHECK fail that can be used to trigger the exploitation of this vulnerability.

Upgrade tensorflow-gpu to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) when DenseBincount assumes its input tensor weights to either have the same shape as its input tensor input or to be length-0. A different weights shape will trigger a CHECK fail that can be used to trigger the exploitation of this vulnerability.

Upgrade tensorflow-gpu to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) when SparseBincount is given inputs for indices, values, and dense_shape that do not make a valid sparse tensor, it results in a segfault that can be used to trigger exploitation.

Upgrade tensorflow-gpu to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) when LRNGrad is given an output_image input tensor that is not 4-D.

Upgrade tensorflow-gpu to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) when the FractionalMaxPoolGrad function validates its inputs with CHECK failures instead of returning errors. If it gets incorrectly sized inputs, the CHECK failure can be used to trigger exploitation.

Upgrade tensorflow-gpu to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) when Save or SaveSlices is run over tensors of an unsupported dtype, it results in a CHECK fail that can be used to trigger exploitation.

Upgrade tensorflow-gpu to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) when tf.linalg.matrix_rank receives an empty input a, the GPU kernel gives a CHECK fail that can be used to trigger exploitation.

Upgrade tensorflow-gpu to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) when tf.quantization.fake_quant_with_min_max_vars_per_channel_gradient receives input min or max of rank other than 1, it gives a CHECK fail that can trigger exploitation.

Upgrade tensorflow-gpu to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) when QuantizedRelu or QuantizedRelu6 are given nonscalar inputs for min_features or max_features, it results in a segfault that can be used to trigger exploitation.

Upgrade tensorflow-gpu to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) when the implementation of Conv2DBackpropInput requires input_sizes to be 4-dimensional. Otherwise, it gives a CHECK failure which can be used to trigger exploitation.

Upgrade tensorflow-gpu to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) when MaxPool receives a window size input array ksize with dimensions greater than its input tensor input, the GPU kernel gives a CHECK fail that can be used to trigger exploitation.

Upgrade tensorflow-gpu to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Improper Input Validation due to improper validation of orig_input_shape in AvgPoolGrad.

Upgrade tensorflow-gpu to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to NULL Pointer Dereference when LowerBound or UpperBound is given an empty sorted_inputs input.

Upgrade tensorflow-gpu to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) when ParameterizedTruncatedNormal assumes shape is of type int32. A valid shape of type int64 results in a mismatched type CHECK fail that can be used to trigger exploitation.

Upgrade tensorflow-gpu to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) when QuantizeDownAndShrinkRange is given nonscalar inputs for input_min or input_max, it results in a segfault that can be used to trigger an exploitation.

Upgrade tensorflow-gpu to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) when RaggedBincount is given an empty input tensor splits, it results in a segfault that can be used to trigger the exploitation of this vulnerability.

Upgrade tensorflow-gpu to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) when TensorListScatter and TensorListScatterV2 receive an element_shape of a rank greater than one, they give a CHECK fail that can trigger the exploitation of the vulnerability.

Upgrade tensorflow-gpu to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) when TensorListFromTensor receives an element_shape of a rank greater than one.

Upgrade tensorflow-gpu to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) when the FakeQuantWithMinMaxVars function is given min or max tensors of a nonzero rank.

Upgrade tensorflow-gpu to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Integer Overflow or Wraparound. The RaggedRangOp function takes an argument limits that is eventually used to construct a TensorShape as an int64. If limits is a very large float, it can overflow when converted to an int64. This triggers an InvalidArgument but also throws an abort signal that crashes the program.

Upgrade tensorflow-gpu to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) when QuantizedInstanceNorm is given x_min or x_max tensors of a nonzero rank.

Upgrade tensorflow-gpu to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) when CollectiveGather receives a scalar input input, which results in a CHECK failure.

Upgrade tensorflow-gpu to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) when Unbatch receives a nonscalar input id, it results in a CHECK fail.

Upgrade tensorflow-gpu to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) when QuantizedAdd is given min_input or max_input tensors of a nonzero rank.

Upgrade tensorflow-gpu to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) due to improper input validation of orig_input_tensor_shape in FractionalAvgPoolGrad.

Upgrade tensorflow-gpu to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) due to improper input validation of orig_input_shape in AvgPool3DGradOp.

Upgrade tensorflow-gpu to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) when QuantizedMatMul is given nonscalar input for min_a, max_a, min_b, and max_b.

Upgrade tensorflow-gpu to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) when QuantizedAvgPool is given min_input or max_input tensors of a nonzero rank.

import tensorflow as tf

ksize = [1, 2, 2, 1]
strides = [1, 2, 2, 1]
padding = "SAME"
input = tf.constant(1, shape=[1,4,4,2], dtype=tf.quint8)
min_input = tf.constant([], shape=[0], dtype=tf.float32)
max_input = tf.constant(0, shape=[1], dtype=tf.float32)
tf.raw_ops.QuantizedAvgPool(input=input, min_input=min_input, max_input=max_input, ksize=ksize, strides=strides, padding=padding)

Upgrade tensorflow-gpu to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) when a nonscalar id is provided to the UnbatchGradOp function, and batch_index is incorrect.

Upgrade tensorflow-gpu to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) due to improper input validation in BlockLSTMGradV2.

Upgrade tensorflow-gpu to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) when QuantizedBiasAdd is given min_input, max_input, min_bias, max_bias tensors of a nonzero rank, it results in a segfault.

Upgrade tensorflow-gpu to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) due to a CHECK failure in TensorListReserve via missing validation. Exploiting this vulnerability is possible when a num_elements of more than 1 element is provided, then tf.raw_ops.TensorListReserve fails the CHECK_EQ in CheckIsAlignedAndSingleElement.

Upgrade tensorflow-gpu to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) due to a CHECK fail in SetSize, when SetSize receives an input set_shape that is not a 1D tensor.

Upgrade tensorflow-gpu to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) due to a CHECK fail in EmptyTensorList when it receives an input element_shape with more than one dimension.

Upgrade tensorflow-gpu to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to NULL Pointer Dereference when Eig can be fed an incorrect Tout input, resulting in a CHECK fail that can trigger a denial of service attack.

Upgrade tensorflow-gpu to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Improper Input Validation when converting transposed convolutions using per-channel weight quantization, the converter segfaults and crashes the Python process.

Upgrade tensorflow-gpu to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to NULL Pointer Dereference through mlir::tfg::TFOp::nameAttr, when it receives null type list attributes.

Upgrade tensorflow-gpu to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Improper Input Validation when Requantize is given input_min, input_max, requested_output_min, requested_output_max tensors of a nonzero rank, it results in a segfault that can be used to trigger a denial of service attack.

Upgrade tensorflow-gpu to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Division by zero when Conv2D is given empty input and the filter and padding sizes are valid, the output is all-zeros.

Upgrade tensorflow-gpu to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) when providing an empty function attributes to mlir::tfg::ConvertGenericFunctionToFunctionDef.

Upgrade tensorflow-gpu to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Integer Overflow or Wraparound when RangeSize function receives values that do not fit into an int64_t.

Upgrade tensorflow-gpu to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) due to a CHECK failure when DrawBoundingBoxes receives an input boxes that are not of dtype float.

Upgrade tensorflow-gpu to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS). The implementation of depthwise ops in TensorFlow is vulnerable to a denial of service via CHECK-failure (assertion failure) caused by overflowing the number of elements in a tensor:

import tensorflow as tf

input = tf.constant(1, shape=[1, 4, 4, 3], dtype=tf.float32)
filter_sizes = tf.constant(1879048192, shape=[13], dtype=tf.int32)
out_backprop = tf.constant(1, shape=[1, 4, 4, 3], dtype=tf.float32)
tf.raw_ops.DepthwiseConv2dNativeBackpropFilter(
    input=input, filter_sizes=filter_sizes, out_backprop=out_backprop, strides=[1, 1, 1, 1], padding="SAME")

This is due to an incomplete fix for CVE-2021-41197.

Upgrade tensorflow-gpu to version 2.8.1, 2.7.2, 2.6.4 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) via the implementation of tf.histogram_fixed_width, when the values array contains Not a Number (NaN) elements. The implementation assumes that all floating-point operations are defined and then converts a floating-point result to an integer index. If values contains NaN then the result of the division is still NaN and the cast to int32 would result in a crash.

Note: This only occurs on the CPU implementation.

Upgrade tensorflow-gpu to version 2.6.4, 2.7.2, 2.8.1, 2.9.0 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Improper Input Validation because the implementation of tf.raw_ops.QuantizedConv2D does not fully validate the input arguments. In this case, references get bound to nullptr for each argument that is empty.

Upgrade tensorflow-gpu to version 2.6.4, 2.7.2, 2.8.1, 2.9.0 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to NULL Pointer Dereference when the resource handle is empty.

Upgrade tensorflow-gpu to version 2.6.4, 2.7.2, 2.8.1, 2.9.0 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS). Certain TFLite models that were created using the TFLite model converter would crash when loaded in the TFLite interpreter. During quantization, the scale of values could be greater than 1 but the code always assumes sub-unit scaling. Thus, since the code was calling QuantizeMultiplierSmallerThanOneExp, the TFLITE_CHECK_LT assertion would trigger and abort the process.

Upgrade tensorflow-gpu to version 2.6.4, 2.7.2, 2.8.1, 2.9.0 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) because the implementation of tf.raw_ops.StagePeek does not fully validate the input arguments, assuming that the index argument is a scalar when accessing its value.

Upgrade tensorflow-gpu to version 2.6.4, 2.7.2, 2.8.1, 2.9.0 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS). The implementation of tf.raw_ops.UnsortedSegmentJoin does not fully validate the input arguments. This results in a CHECK-failure which can be used to trigger a denial of service attack. The code assumes num_segments is a positive scalar but there is no validation. Since this value is used to allocate the output tensor, a negative value would result in a CHECK-failure (assertion failure), as per TFSA-2021-198.

Upgrade tensorflow-gpu to version 2.6.4, 2.7.2, 2.8.1, 2.9.0 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) due to the implementation of `tf.ragged.constant not fully validating the input arguments.

Upgrade tensorflow-gpu to version 2.6.4, 2.7.2, 2.8.1, 2.9.0 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Type Confusion because the macros used for writing assertions (e.g., CHECK_LT, CHECK_GT, etc.) have incorrect logic when comparing size_t and int values. Due to type conversion rules, several of the macros would be triggered incorrectly.

Upgrade tensorflow-gpu to version 2.6.4, 2.7.2, 2.8.1, 2.9.0 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to NULL Pointer Dereference when calling tf.compat.v1.* ops which don't have support for quantized types.

Upgrade tensorflow-gpu to version 2.6.4, 2.7.2, 2.8.1, 2.9.0 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Heap-based Buffer Overflow. 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 access individual tensor bytes through tensor.data() of size AllocatedBytes(). This behavior results in ASAN failures because the AllocatedBytes() is an estimate of total bytes allocated by a tensor, including any pointed-to constructs (e.g. strings), and does not refer to contiguous bytes in the .data() buffer.

Upgrade tensorflow-gpu to version 2.8.1, 2.9.0 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Integer Overflow or Wraparound due to the improper implementation of tf.raw_ops.SpaceToBatchND which can result in a CHECK-failure.

Upgrade tensorflow-gpu to version 2.6.4, 2.7.2, 2.8.1, 2.9.0 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Improper Input Validation because the implementation of tf.raw_ops.SparseTensorDenseAdd does not fully validate the input arguments. In this case, a reference gets bound to a nullptr during kernel execution which is an undefined behavior.

Upgrade tensorflow-gpu to version 2.6.4, 2.7.2, 2.8.1, 2.9.0 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Out-of-bounds Write because the implementation of tf.raw_ops.EditDistance have an incomplete validation which allows users to pass negative values for loc.

Upgrade tensorflow-gpu to version 2.6.4, 2.7.2, 2.8.1, 2.9.0 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) due to improper input validation under certain condition in tf.compat.v1.signal.rfft2d and tf.compat.v1.signal.rfft3d, results in CHECK failures.

Upgrade tensorflow-gpu to version 2.6.4, 2.7.2, 2.8.1, 2.9.0 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) where the implementation of tf.raw_ops.DeleteSessionTensor does not fully validate the input arguments.

Upgrade tensorflow-gpu to version 2.6.4, 2.7.2, 2.8.1, 2.9.0 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Remote Code Execution (RCE) due to improper fix of CVE-2021-41228. Exploiting this vulnerability is possible via the saved_model_cli tool and can be abused to open a reverse shell.

Upgrade tensorflow-gpu to version 2.6.4, 2.7.2, 2.8.1, 2.9.0 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) due to the implementation of tf.raw_ops.Conv3DBackpropFilterV2 which does not fully validate that filter_sizes argument is a vector.

Upgrade tensorflow-gpu to version 2.6.4, 2.7.2, 2.8.1, 2.9.0 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) due to the implementation of tf.raw_ops.SparseTensorToCSRSparseMatrix which does not fully validate the input arguments. It assumes that dense_shape is a vector and indices is a matrix (as part of requirements for sparse tensors) without validating it.

Upgrade tensorflow-gpu to version 2.6.4, 2.7.2, 2.8.1, 2.9.0 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) due to the implementation of tf.raw_ops.UnsortedSegmentJoin which does not fully validate the input arguments, assuming num_segments is a scalar without validating this before accessing its value.

Upgrade tensorflow-gpu to version 2.6.4, 2.7.2, 2.8.1, 2.9.0 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) due to the implementation of tf.raw_ops.LoadAndRemapMatrix which does not fully validate the input arguments, assuming initializing_values is a vector without validating it before accessing its value.

Upgrade tensorflow-gpu to version 2.6.4, 2.7.2, 2.8.1, 2.9.0 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) where the implementation of tf.raw_ops.LSTMBlockCell does not fully validate the input arguments. The code does not validate the ranks of any of the arguments to this API call. This results in CHECK-failures when the elements of the tensor are accessed.

Upgrade tensorflow-gpu to version 2.6.4, 2.7.2, 2.8.1, 2.9.0 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) where the implementation of tf.raw_ops.TensorSummaryV2 does not fully validate the input arguments.

Upgrade tensorflow-gpu to version 2.6.4, 2.7.2, 2.8.1, 2.9.0 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) where the implementation of tf.raw_ops.DeleteSessionTensor does not fully validate the input arguments.

Upgrade tensorflow-gpu to version 2.6.4, 2.7.2, 2.8.1, 2.9.0 or higher.

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) where 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. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

Upgrade tensorflow-gpu to version 2.6.4, 2.7.2, 2.8.1, 2.9.0 or higher.