Open actionable advisories and recorded vendor fixes. This public package view has no inventory attached and cannot establish whether your deployment is affected. A vendor fix never resolves your finding; verify your installed version and the source guidance.
Open advisories
430 actionable advisories, newest first. Withdrawn advisories and news are excluded. Coverage is partial.
Impact The implementation of tf.rawops.ReverseSequence allows for stack overflow and/or CHECK-fail based denial of service. The implementation fails to validate that seqdim and batchdim arguments are valid. Negative values for seqdim can result in stack overflow or CHECK-failure, depending on the version of Eigen code used to implement the operation. Similar behavior can be exhibited by invalid values of batchdim. Patches We have patched the issue in GitHub commit ecf768cbe50cedc0a45ce1ee223146a3d3d26d23. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. For more information Please consult our security guide for more information regarding the security model and how to contact us with issues and questions. Attribution This vulnerability has been reported by Ying Wang and Yakun Zhang of Baidu X-Team.
Recommended action: Upgrade to 2.4.2, 2.3.3, 2.2.3 or 2.1.4 by release line, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.
Impact The implementation of tf.rawops.MaxPool3DGradGrad exhibits undefined behavior by dereferencing null pointers backing attacker-supplied empty tensors: The implementation fails to validate that the 3 tensor inputs are not empty. If any of them is empty, then accessing the elements in the tensor results in dereferencing a null pointer. Patches We have patched the issue in GitHub commit a3d9f9be9ac2296615644061b40cefcee341dcc4. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. For more information Please consult our security guide for more information regarding the security model and how to contact us with issues and questions. Attribution This vulnerability has been reported by Ying Wang and Yakun Zhang of Baidu X-Team.
Recommended action: Upgrade to 2.4.2, 2.3.3, 2.2.3 or 2.1.4 by release line, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.
Impact The implementation of tf.rawops.MaxPoolGradWithArgmax is vulnerable to a division by 0: The implementation fails to validate that the batch dimension of the tensor is non-zero, before dividing by this quantity. Patches We have patched the issue in GitHub commit 376c352a37ce5a68b721406dc7e77ac4b6cf483d. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. For more information Please consult our security guide for more information regarding the security model and how to contact us with issues and questions. Attribution This vulnerability has been reported by Ying Wang and Yakun Zhang of Baidu X-Team.
Recommended action: Upgrade to 2.4.2, 2.3.3, 2.2.3 or 2.1.4 by release line, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.
Impact The implementation of tf.rawops.SdcaOptimizer triggers undefined behavior due to dereferencing a null pointer: The implementation does not validate that the user supplied arguments satisfy all constraints expected by the op. Patches We have patched the issue in GitHub commit f7cc8755ac6683131fdfa7a8a121f9d7a9dec6fb. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. For more information Please consult our security guide for more information regarding the security model and how to contact us with issues and questions. Attribution This vulnerability has been reported by Ying Wang and Yakun Zhang of Baidu X-Team.
Recommended action: Upgrade to 2.4.2, 2.3.3, 2.2.3 or 2.1.4 by release line, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.
Impact The implementation of tf.rawops.MaxPoolGradWithArgmax can cause reads outside of bounds of heap allocated data if attacker supplies specially crafted inputs: The implementation assumes that the last element of boxes input is 4, as required by the op. Since this is not checked attackers passing values less than 4 can write outside of bounds of heap allocated objects and cause memory corruption: If the last dimension in boxes is less than 4, accesses similar to tboxes(b, bb, 3) will access data outside of bounds. Further during code execution there are also writes to these indices. Patches We have patched the issue in GitHub commit 79865b542f9ffdc9caeb255631f7c56f1d4b6517. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. For more information Please consult our security guide for more information regarding the…
Recommended action: Upgrade to 2.4.2, 2.3.3, 2.2.3 or 2.1.4 by release line, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.
Impact The implementation of tf.rawops.MaxPoolGradWithArgmax can cause reads outside of bounds of heap allocated data if attacker supplies specially crafted inputs: The implementation uses the same value to index in two different arrays but there is no guarantee that the sizes are identical. Patches We have patched the issue in GitHub commit dcd7867de0fea4b72a2b34bd41eb74548dc23886. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. For more information Please consult our security guide for more information regarding the security model and how to contact us with issues and questions. Attribution This vulnerability has been reported by Ying Wang and Yakun Zhang of Baidu X-Team.
Recommended action: Upgrade to 2.4.2, 2.3.3, 2.2.3 or 2.1.4 by release line, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.
Impact The implementation of tf.rawops.MaxPoolGradWithArgmax can cause reads outside of bounds of heap allocated data if attacker supplies specially crafted inputs: The implementation assumes that the inputmin and inputmax tensors have at least one element, as it accesses the first element in two arrays: If the tensors are empty, .flat<T() is an empty object, backed by an empty array. Hence, accesing even the 0th element is a read outside the bounds. Patches We have patched the issue in GitHub commit ef0c008ee84bad91ec6725ddc42091e19a30cf0e. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. For more information Please consult our security guide for more information regarding the security model and how to contact us with issues and questions. Attribution This vulnerability has been reported by Ying Wang and Yakun Zhang…
Recommended action: Upgrade to 2.4.2, 2.3.3, 2.2.3 or 2.1.4 by release line, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.
Impact An attacker can trigger undefined behavior by binding to null pointer in tf.rawops.ParameterizedTruncatedNormal: This is because the implementation does not validate input arguments before accessing the first element of shape: If shape argument is empty, then shapetensor.flat<T() is an empty array. Patches We have patched the issue in GitHub commit 5e52ef5a461570cfb68f3bdbbebfe972cb4e0fd8. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. For more information Please consult our security guide for more information regarding the security model and how to contact us with issues and questions. Attribution This vulnerability has been reported by Ying Wang and Yakun Zhang of Baidu X-Team.
Recommended action: Upgrade to 2.4.2, 2.3.3, 2.2.3 or 2.1.4 by release line, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.
Impact Due to lack of validation in tf.rawops.SparseDenseCwiseMul, an attacker can trigger denial of service via CHECK-fails or accesses to outside the bounds of heap allocated data: Since the implementation only validates the rank of the input arguments but no constraints between dimensions, an attacker can abuse them to trigger internal CHECK assertions (and cause program termination, denial of service) or to write to memory outside of bounds of heap allocated tensor buffers. Patches We have patched the issue in GitHub commit 7ae2af34087fb4b5c8915279efd03da3b81028bc. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. For more information Please consult our security guide for more information regarding the security model and how to contact us with issues and questions. Attribution This vulnerability has been reported by…
Recommended action: Upgrade to 2.4.2, 2.3.3, 2.2.3 or 2.1.4 by release line, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.
Impact An attacker can write outside the bounds of heap allocated arrays by passing invalid arguments to tf.rawops.Dilation2DBackpropInput: This is because the implementation does not validate before writing to the output array. The values for hout and wout are guaranteed to be in range for outbackprop (as they are loop indices bounded by the size of the array). However, there are no similar guarantees relating hinmax/winmax and inbackprop. Patches We have patched the issue in GitHub commit 3f6fe4dfef6f57e768260b48166c27d148f3015f. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. For more information Please consult our security guide for more information regarding the security model and how to contact us with issues and questions. Attribution This vulnerability has been reported by Yakun Zhang and Ying Wang of Baidu…
Recommended action: Upgrade to 2.4.2, 2.3.3, 2.2.3 or 2.1.4 by release line, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.
Impact An attacker can trigger a null pointer dereference in the implementation of tf.rawops.SparseFillEmptyRows: This is because of missing validation that was covered under a TODO. If the denseshape tensor is empty, then denseshapet.vec<() would cause a null pointer dereference in the implementation of the op: Patches We have patched the issue in GitHub commit faa76f39014ed3b5e2c158593b1335522e573c7f. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. For more information Please consult our security guide for more information regarding the security model and how to contact us with issues and questions. Attribution This vulnerability has been reported by Yakun Zhang and Ying Wang of Baidu X-Team.
Recommended action: Upgrade to 2.4.2, 2.3.3, 2.2.3 or 2.1.4 by release line, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.
Impact An attacker can trigger a null pointer dereference in the implementation of tf.rawops.EditDistance: This is because the implementation has incomplete validation of the input parameters. In the above scenario, an attacker causes an allocation of an empty tensor for the output: Because outputshape has 0 elements, the result of output-flat<T() has an empty buffer, so calling setZero would result in a null dereference. Patches We have patched the issue in GitHub commit f4c364a5d6880557f6f5b6eb5cee2c407f0186b3. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. For more information Please consult our security guide for more information regarding the security model and how to contact us with issues and questions. Attribution This vulnerability has been reported by Yakun Zhang and Ying Wang of Baidu X-Team.
Recommended action: Upgrade to 2.4.2, 2.3.3, 2.2.3 or 2.1.4 by release line, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.
Impact An attacker can cause a denial of service by exploiting a CHECK-failure coming from the implementation of tf.rawops.RFFT: The above example causes Eigen code to operate on an empty matrix. This triggers on an assertion and causes program termination. Patches We have patched the issue in GitHub commit 31bd5026304677faa8a0b77602c6154171b9aec1. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. For more information Please consult our security guide for more information regarding the security model and how to contact us with issues and questions. Attribution This vulnerability has been reported by Yakun Zhang and Ying Wang of Baidu X-Team.
Recommended action: Upgrade to 2.4.2, 2.3.3, 2.2.3 or 2.1.4 by release line, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.
Impact An attacker can cause a denial of service by exploiting a CHECK-failure coming from the implementation of tf.rawops.IRFFT: The above example causes Eigen code to operate on an empty matrix. This triggers on an assertion and causes program termination. Patches We have patched the issue in GitHub commit 1c56f53be0b722ca657cbc7df461ed676c8642a2. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. For more information Please consult our security guide for more information regarding the security model and how to contact us with issues and questions. Attribution This vulnerability has been reported by Yakun Zhang and Ying Wang of Baidu X-Team.
Recommended action: Upgrade to 2.4.2, 2.3.3, 2.2.3 or 2.1.4 by release line, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.
Impact An attacker can cause a denial of service by exploiting a CHECK-failure coming from tf.rawops.LoadAndRemapMatrix: This is because the implementation assumes that the ckptpath is always a valid scalar. However, an attacker can send any other tensor as the first argument of LoadAndRemapMatrix. This would cause the rank CHECK in scalar<T()() to trigger and terminate the process. Patches We have patched the issue in GitHub commit 77dd114513d7796e1e2b8aece214a380af26fbf4. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. For more information Please consult our security guide for more information regarding the security model and how to contact us with issues and questions. Attribution This vulnerability has been reported by Yakun Zhang and Ying Wang of Baidu X-Team.
Recommended action: Upgrade to 2.4.2, 2.3.3, 2.2.3 or 2.1.4 by release line, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.
Impact An attacker can cause a heap buffer overflow in tf.rawops.RaggedTensorToTensor: This is because the implementation uses the same index to access two arrays in parallel: Since the user controls the shape of the input arguments, an attacker could trigger a heap OOB access when parentoutputindex is shorter than rowsplit. Patches We have patched the issue in GitHub commit a84358aa12f0b1518e606095ab9cfddbf597c121. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. For more information Please consult our security guide for more information regarding the security model and how to contact us with issues and questions. Attribution This vulnerability has been reported by Ying Wang and Yakun Zhang of Baidu X-Team.
Recommended action: Upgrade to 2.4.2, 2.3.3, 2.2.3 or 2.1.4 by release line, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.
Impact An attacker can access data outside of bounds of heap allocated array in tf.rawops.UnicodeEncode: This is because the implementation assumes that the inputvalue/inputsplits pair specify a valid sparse tensor. Patches We have patched the issue in GitHub commit 51300ba1cc2f487aefec6e6631fef03b0e08b298. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. For more information Please consult our security guide for more information regarding the security model and how to contact us with issues and questions. Attribution This vulnerability has been reported by Ying Wang and Yakun Zhang of Baidu X-Team.
Recommended action: Upgrade to 2.4.2, 2.3.3, 2.2.3 or 2.1.4 by release line, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.
Impact An attacker can cause a heap buffer overflow in tf.rawops.SparseSplit: This is because the implementation accesses an array element based on a user controlled offset: This results in overriding values on the heap. Patches We have patched the issue in GitHub commit 8ba6fa29cd8bf9cef9b718dc31c78c73081f5b31. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. For more information Please consult our security guide for more information regarding the security model and how to contact us with issues and questions. Attribution This vulnerability has been reported by Ying Wang and Yakun Zhang of Baidu X-Team.
Recommended action: Upgrade to 2.4.2, 2.3.3, 2.2.3 or 2.1.4 by release line, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.
Impact An attacker can cause a denial of service via a FPE runtime error in tf.rawops.SparseMatMul: The division by 0 occurs deep in Eigen code because the b tensor is empty. Patches We have patched the issue in GitHub commit 7f283ff806b2031f407db64c4d3edcda8fb9f9f5. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. For more information Please consult our security guide for more information regarding the security model and how to contact us with issues and questions. Attribution This vulnerability has been reported by Ying Wang and Yakun Zhang of Baidu X-Team.
Recommended action: Upgrade to 2.4.2, 2.3.3, 2.2.3 or 2.1.4 by release line, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.
Impact An attacker can cause a denial of service via a FPE runtime error in tf.rawops.Reverse: This is because the implementation performs a division based on the first dimension of the tensor argument: Since this is controlled by the user, an attacker can trigger a denial of service. Patches We have patched the issue in GitHub commit 4071d8e2f6c45c1955a811fee757ca2adbe462c1. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. For more information Please consult our security guide for more information regarding the security model and how to contact us with issues and questions. Attribution This vulnerability has been reported by Ying Wang and Yakun Zhang of Baidu X-Team.
Recommended action: Upgrade to 2.4.2, 2.3.3, 2.2.3 or 2.1.4 by release line, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.
Impact An attacker can cause a denial of service via a FPE runtime error in tf.rawops.FusedBatchNorm: This is because the implementation performs a division based on the last dimension of the x tensor: Since this is controlled by the user, an attacker can trigger a denial of service. Patches We have patched the issue in GitHub commit 1a2a87229d1d61e23a39373777c056161eb4084d. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. For more information Please consult our security guide for more information regarding the security model and how to contact us with issues and questions. Attribution This vulnerability has been reported by Ying Wang and Yakun Zhang of Baidu X-Team.
Recommended action: Upgrade to 2.4.2, 2.3.3, 2.2.3 or 2.1.4 by release line, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.
Impact An attacker can cause a denial of service via a FPE runtime error in tf.rawops.DenseCountSparseOutput: This is because the implementation computes a divisor value from user data but does not check that the result is 0 before doing the division: Since data is given by the values argument, numbatchelements is 0. Patches We have patched the issue in GitHub commit da5ff2daf618591f64b2b62d9d9803951b945e9f. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, and TensorFlow 2.3.3, as these are also affected. For more information Please consult our security guide for more information regarding the security model and how to contact us with issues and questions. Attribution This vulnerability has been reported by Yakun Zhang and Ying Wang of Baidu X-Team.
Recommended action: Upgrade to 2.4.2 or 2.3.3 by release line, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.
Impact An attacker can read data outside of bounds of heap allocated buffer in tf.rawops.QuantizeAndDequantizeV3: This is because the implementation does not validate the value of user supplied axis attribute before using it to index in the array backing the input argument: Patches We have patched the issue in GitHub commit 99085e8ff02c3763a0ec2263e44daec416f6a387. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. For more information Please consult our security guide for more information regarding the security model and how to contact us with issues and questions. Attribution This vulnerability has been reported by Aivul Team from Qihoo 360.
Recommended action: Upgrade to 2.4.2, 2.3.3, 2.2.3 or 2.1.4 by release line, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.
Impact An attacker can cause a denial of service by controlling the values of numsegments tensor argument for UnsortedSegmentJoin: This is because the implementation assumes that the numsegments tensor is a valid scalar: Since the tensor is empty the CHECK involved in .scalar<T()() that checks that the number of elements is exactly 1 will be invalidated and this would result in process termination. Patches We have patched the issue in GitHub commit 704866eabe03a9aeda044ec91a8d0c83fc1ebdbe. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. For more information Please consult our security guide for more information regarding the security model and how to contact us with issues and questions. Attribution This vulnerability has been reported by Ying Wang and Yakun Zhang of Baidu X-Team.
Recommended action: Upgrade to 2.4.2, 2.3.3, 2.2.3 or 2.1.4 by release line, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.
Impact The implementation of MatrixTriangularSolve fails to terminate kernel execution if one validation condition fails: Since OPREQUIRES only sets ctx-status() to a non-OK value and calls return, this allows malicious attackers to trigger an out of bounds read: As the two input tensors are empty, the OPREQUIRES in ValidateInputTensors should fire and interrupt execution. However, given the implementation of OPREQUIRES, after the in0.dims() = 2 fails, execution moves to the initialization of the bcast object. This initialization is done with invalid data and results in heap OOB read. Patches We have patched the issue in GitHub commit 480641e3599775a8895254ffbc0fc45621334f68. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. For more information Please consult our security guide for more information regarding the…
Recommended action: Upgrade to 2.4.2, 2.3.3, 2.2.3 or 2.1.4 by release line, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.