CVE-2024-3660

CVE-2024-3660 is an arbitrary code execution vulnerability in TensorFlow Keras (

CVE-2024-3660 Arbitrary code injection vulnerability in Keras framework < 2.13

A arbitrary code injection vulnerability in TensorFlow's Keras framework 2.13 allows attackers to execute arbitrary code with the same permissions as the application using a model that allow arbitrary code irrespective of the application...

CVE-2024-3660

A arbitrary code injection vulnerability in TensorFlow's Keras framework 2.13 allows attackers to execute arbitrary code with the same permissions as the application using a model that allow arbitrary code irrespective of the application...

TensorFlow Keras 安全漏洞

TensorFlow Keras is a framework open-sourced by TensorFlow. It provides an approachable and efficient interface for solving machine learning problems. A security vulnerability exists in TensorFlow Keras versions prior to 2.13, which stems from the presence of an arbitrary code injection...

Keras 2 Lambda Layers Allow Arbitrary Code Injection in TensorFlow Models

Overview Lambda Layers in third party TensorFlow-based Keras models allow attackers to inject arbitrary code into versions built prior to Keras 2.13 that may then unsafely run with the same permissions as the running application. For example, an attacker could use this feature to trojanize a...

PT-2024-27091

Name of the Vulnerable Software and Affected Versions TensorFlow's Keras framework versions prior to 2.13 Description A code injection issue in TensorFlow's Keras framework allows attackers to execute arbitrary code with the same permissions as the application. This can be achieved by using a mod...

BIT-TENSORFLOW-2020-15190 Segfault in Tensorflow

In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the tf.rawops.Switch operation takes as input a tensor and a boolean and outputs two tensors. Depending on the boolean value, one of the tensors is exactly the input tensor whereas the other one should be an empty tensor. Howeve...

BIT-TENSORFLOW-2020-15191 Undefined behavior in Tensorflow

In Tensorflow before versions 2.2.1 and 2.3.1, if a user passes an invalid argument to dlpack.todlpack the expected validations will cause variables to bind to nullptr while setting a status variable to the error condition. However, this status argument is not properly checked. Hence, code...

BIT-TENSORFLOW-2020-15192 Memory leak in Tensorflow

In Tensorflow before versions 2.2.1 and 2.3.1, if a user passes a list of strings to dlpack.todlpack there is a memory leak following an expected validation failure. The issue occurs because the status argument during validation failures is not properly checked. Since each of the above methods ca...

BIT-TENSORFLOW-2020-15193 Memory corruption in Tensorflow

In Tensorflow before versions 2.2.1 and 2.3.1, the implementation of dlpack.todlpack can be made to use uninitialized memory resulting in further memory corruption. This is because the pybind11 glue code assumes that the argument is a tensor. However, there is nothing stopping users from passing ...

BIT-TENSORFLOW-2020-15194 Denial of Service in Tensorflow

In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the SparseFillEmptyRowsGrad implementation has incomplete validation of the shapes of its arguments. Although reverseindexmapt and gradvaluest are accessed in a similar pattern, only reverseindexmapt is validated to be of proper...

BIT-TENSORFLOW-2020-15195 Heap buffer overflow in Tensorflow

In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the implementation of SparseFillEmptyRowsGrad uses a double indexing pattern. It is possible for reverseindexmapi to be an index outside of bounds of gradvalues, thus resulting in a heap buffer overflow. The issue is patched in...

BIT-TENSORFLOW-2020-15196 Heap buffer overflow in Tensorflow

In Tensorflow version 2.3.0, the SparseCountSparseOutput and RaggedCountSparseOutput implementations don't validate that the weights tensor has the same shape as the data. The check exists for DenseCountSparseOutput, where both tensors are fully specified. In the sparse and ragged count weights a...

BIT-TENSORFLOW-2020-15197 Denial of Service in Tensorflow

In Tensorflow before version 2.3.1, the SparseCountSparseOutput implementation does not validate that the input arguments form a valid sparse tensor. In particular, there is no validation that the indices tensor has rank 2. This tensor must be a matrix because code assumes its elements are access...

BIT-TENSORFLOW-2020-15198 Heap buffer overflow in Tensorflow

In Tensorflow before version 2.3.1, the SparseCountSparseOutput implementation does not validate that the input arguments form a valid sparse tensor. In particular, there is no validation that the indices tensor has the same shape as the values one. The values in these tensors are always accessed...

BIT-TENSORFLOW-2020-15199 Denial of Service in Tensorflow

In Tensorflow before version 2.3.1, the RaggedCountSparseOutput does not validate that the input arguments form a valid ragged tensor. In particular, there is no validation that the splits tensor has the minimum required number of elements. Code uses this quantity to initialize a different data...

BIT-TENSORFLOW-2020-15200 Segfault in Tensorflow

In Tensorflow before version 2.3.1, the RaggedCountSparseOutput implementation does not validate that the input arguments form a valid ragged tensor. In particular, there is no validation that the values in the splits tensor generate a valid partitioning of the values tensor. Thus, the code sets ...

BIT-TENSORFLOW-2020-15201 Heap buffer overflow in Tensorflow

In Tensorflow before version 2.3.1, the RaggedCountSparseOutput implementation does not validate that the input arguments form a valid ragged tensor. In particular, there is no validation that the values in the splits tensor generate a valid partitioning of the values tensor. Hence, the code is...

BIT-TENSORFLOW-2020-15202 Integer truncation in Shard API usage

In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the Shard API in TensorFlow expects the last argument to be a function taking two int64 i.e., long long arguments. However, there are several places in TensorFlow where a lambda taking int or int32 arguments is being used. In...

BIT-TENSORFLOW-2020-15203 Denial of Service in Tensorflow

In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, by controlling the fill argument of tf.strings.asstring, a malicious attacker is able to trigger a format string vulnerability due to the way the internal format use in a printf call is constructed. This may result in segmentati...