PT-2021-18360 · Google · Tensorflow

Name of the Vulnerable Software and Affected Versions: TensorFlow versions prior to 2.5.0 TensorFlow versions 2.1.4 through 2.4.2 Description: Incomplete validation in SparseAdd results in allowing attackers to exploit undefined behavior, such as dereferencing null pointers and writing outside of...

PT-2021-18309 · Google · Tensorflow

Name of the Vulnerable Software and Affected Versions: TensorFlow versions prior to 2.5.0 TensorFlow version 2.4.2 TensorFlow version 2.3.3 TensorFlow version 2.2.3 TensorFlow version 2.1.4 Description: An attacker can cause a heap buffer overflow in tf.raw ops.SparseSplit because the...

PT-2021-18308 · Google · Tensorflow

Name of the Vulnerable Software and Affected Versions: TensorFlow versions prior to 2.5.0 TensorFlow version 2.4.2 TensorFlow version 2.3.3 TensorFlow version 2.2.3 TensorFlow version 2.1.4 Description: An attacker can cause a denial of service via a FPE runtime error in tf.raw ops.SparseMatMul...

PT-2021-18370 · Google · Tensorflow

Name of the Vulnerable Software and Affected Versions: TensorFlow versions 2.1.4 through 2.4.2 TensorFlow versions prior to 2.5.0 Description: Passing invalid arguments, such as those discovered via fuzzing, to tf.raw ops.SparseCountSparseOutput results in a segfault. Recommendations: For version...

PT-2021-18316 · Google · Tensorflow

Name of the Vulnerable Software and Affected Versions: TensorFlow versions prior to 2.5.0 TensorFlow versions 2.4.2 and earlier TensorFlow versions 2.3.3 and earlier TensorFlow versions 2.2.3 and earlier TensorFlow versions 2.1.4 and earlier Description: An attacker can trigger a null pointer...

Google TensorFlow 缓冲区错误漏洞

Google TensorFlow is an end-to-end open source machine learning platform. A heap out-of-bounds access vulnerability exists in Google TensorFlow SparseDenseCwiseMul. An attacker can exploit the vulnerability by passing an invalid parameter to "tf.raw\u ops.backpropinput" to write outside the...

PT-2021-18274 · Google · Tensorflow

Name of the Vulnerable Software and Affected Versions: TensorFlow versions prior to 2.5.0 TensorFlow version 2.4.2 TensorFlow version 2.3.3 TensorFlow version 2.2.3 TensorFlow version 2.1.4 Description: An attacker can trigger a denial of service via a CHECK-fail in tf.raw...

PT-2021-18281 · Google · Tensorflow

Name of the Vulnerable Software and Affected Versions: TensorFlow versions prior to 2.5.0 TensorFlow versions 2.4.2 and earlier TensorFlow versions 2.3.3 and earlier TensorFlow versions 2.2.3 and earlier TensorFlow versions 2.1.4 and earlier Description: An attacker can trigger a null pointer...

PT-2021-18296 · Google · Tensorflow

Name of the Vulnerable Software and Affected Versions: TensorFlow versions prior to 2.5.0 TensorFlow versions 2.4.2 and earlier TensorFlow versions 2.3.3 and earlier TensorFlow versions 2.2.3 and earlier TensorFlow versions 2.1.4 and earlier Description: An attacker can trigger a denial of servic...

USN-4692-1 tar vulnerabilities

Chris Siebenmann discovered that tar incorrectly handled extracting files resized during extraction when invoked with the --sparse flag. An attacker could possibly use this issue to cause a denial of service. This issue only affected Ubuntu 12.04 ESM, Ubuntu 14.04 ESM, Ubuntu 16.04 LTS and Ubuntu...

SUSE SLES12 Security Update : tar (SUSE-SU-2020:2806-1)

This update for tar fixes the following issues : Security issues fixed : CVE-2019-9923: Fixed a denial of service while parsing certain archives with malformed extended headers in paxdecodeheader bsc1130496. CVE-2018-20482: Fixed a denial of service when the '--sparse' option mishandles file...

Arbitrary Code Execution

tensorlfow is vulnerable to arbitrary code execution. The SparseCountSparseOutput implementation does not validate that the input arguments form a valid sparse tensor, allowing an attacker to execute arbitrary code on the host OS by causing a shape mismatch that can result in accesses outside of...

Google TensorFlow Buffer Overflow Vulnerability (CNVD-2020-54782)

Google TensorFlow is a suite of end-to-end open source platforms for machine learning from Google USA. A security vulnerability exists in Tensorflow SparseFillEmptyRowsGrad versions prior to 1.15.4, 2.0.3, 2.1.2, 2.2.1, 2.3.1, and 2.3.1, which arises from a networked system or product that perfor...

Tensorflow Data Validation Vulnerability

Google TensorFlow is a suite of end-to-end open source platforms for machine learning from Google USA. A security vulnerability exists in Tensorflow version 2.3.0 that stems from the inability of the SparseCountSparseOutput and RaggedCountSparseOutput implementations to verify that the weights...

Information disclosure

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...

PYSEC-2020-276

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...

PYSEC-2020-274

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...

PYSEC-2020-312

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...

PYSEC-2020-122

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...

PYSEC-2020-311

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...