Type confusion leading to segfault in Tensorflow

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.rawops.ConcatV2 values=1,2,3,4,5,6, axis = 0xb500005b return y test The axis argument...

Division by zero in Tensorflow

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.rawops.AvgPoolGrad originputshape=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, padding='VALID',...

Out of bounds write in Tensorflow

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

Integer overflow in Tensorflow

Impact The implementation of Range suffers from integer overflows. These can trigger undefined behavior or, in some scenarios, extremely large allocations. Patches We have patched the issue in GitHub commit f0147751fd5d2ff23251149ebad9af9f03010732 merging 51733. The fix will be included in...

Out of bounds write in TFLite

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...

Integer overflow in TFLite

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

Division by zero in TFLite

Impact An attacker can craft a TFLite model that would trigger a division by zero in BiasAndClamp implementation: cc inline void BiasAndClampfloat clampmin, float clampmax, int biassize, const float biasdata, int arraysize, float arraydata // ... TFLITEDCHECKEQarraysize % biassize, 0; // ... Ther...

Division by zero in TFLite

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...

Heap overflow in Tensorflow

Impact The implementation of SparseCountSparseOutput is vulnerable to a heap overflow: python import tensorflow as tf import numpy as np tf.rawops.SparseCountSparseOutput indices=-1,-1, values=2, denseshape=1, 1, weights=1, binaryoutput=True, minlength=-1, maxlength=-1, name=None Patches We have...

Null pointer dereference in TensorFlow

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.rawops.QuantizedMaxPool input = tf.constant4, dtype=tf.quint8, mininput = , maxinput = 1, ksize = 1, 1, 1, 1,...

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: python import tensorflow as tf import numpy as np tf.rawops.AddManySparseToTensorsMap...

NULL Pointer Dereference and Access of Uninitialized Pointer in TensorFlow

Impact The code for boosted trees in TensorFlow is still missing validation. This allows malicious users to read and write outside of bounds of heap allocated data as well as trigger denial of service via dereferencing nullptrs or via CHECK-failures. This follows after CVE-2021-41208 where these...

Integer Overflow or Wraparound in TensorFlow

Impact The Grappler component of TensorFlow is vulnerable to a denial of service via CHECK-failure assertion failure in constant folding: cc for const auto& outputprop : outputprops const PartialTensorShape outputshapeoutputprop.shape; // ... The outputprop tensor has a shape that is controlled b...

Null pointer dereference in TensorFlow

Impact When building an XLA compilation cache, if default settings are used, TensorFlow triggers a null pointer dereference: cc string allowedgpus = flr-configproto-gpuoptions.visibledevicelist; In the default scenario, all devices are allowed, so flr-configproto is nullptr. Patches We have patch...

Out of bounds read in Tensorflow

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...

GHSA-GWCX-JRX4-92W2 Segfault in `simplifyBroadcast` in Tensorflow

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 sizet maxRank = 0; for auto shape : llvm::enumerateshapes auto foundshape = analysis.dimensionsForShapeTensorshape.value; if...

Out of bounds read in Tensorflow

Impact TensorFlow's type inference can cause a heap OOB read as the bounds checking is done in a DCHECK which is a no-op during production: cc if nodet.typeid != TFTUNSET int ix = inputidxi; DCHECKix nodet.argssize "input " i " should have an output " ix " but instead only has " nodet.argssize "...

GHSA-VQ36-27G6-P492 Out of bounds read in Tensorflow

Impact TensorFlow's type inference can cause a heap OOB read as the bounds checking is done in a DCHECK which is a no-op during production: cc if nodet.typeid != TFTUNSET int ix = inputidxi; DCHECKix nodet.argssize "input " i " should have an output " ix " but instead only has " nodet.argssize "...

Stack overflow in TensorFlow

Impact The GraphDef format in TensorFlow does not allow self recursive functions. The runtime assumes that this invariant is satisfied. However, a GraphDef containing a fragment such as the following can be consumed when loading a SavedModel: library function signature name: "SomeOp" description:...

Crash due to erroneous `StatusOr` in TensorFlow

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 opregdata-typector != nullptr VLOG3 opdef; const FullTypeDef ctortypedef =...