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
427 actionable advisories, newest first. Withdrawn advisories and news are excluded. Coverage is partial.
Impact The implementation of the SpaceToBatchNd TFLite operator is vulnerable to a division by zero error: An attacker can craft a model such that one dimension of the block input is 0. Hence, the corresponding value in blockshape is 0. Patches We have patched the issue in GitHub commit 6d36ba65577006affb272335b7c1abd829010708. 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 members of the 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.
The implementation of the EmbeddingLookup TFLite operator is vulnerable to a division by zero error: An attacker can craft a model such that the first dimension of the value input is 0. Patches We have patched the issue in GitHub commit f61c57bd425878be108ec787f4d96390579fb83e. 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 members of the 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 The implementation of the DepthToSpace TFLite operator is vulnerable to a division by zero error: An attacker can craft a model such that params-blocksize is 0. Patches We have patched the issue in GitHub commit 106d8f4fb89335a2c52d7c895b7a7485465ca8d9. 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 members of the 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 TFLite's convolution code has multiple division where the divisor is controlled by the user and not checked to be non-zero. For example: Patches We have patched the issue in GitHub commit ff489d95a9006be080ad14feb378f2b4dac35552. 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 members of the 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 The implementation of the BatchToSpaceNd TFLite operator is vulnerable to a division by zero error: An attacker can craft a model such that one dimension of the block input is 0. Hence, the corresponding value in blockshape is 0. Patches We have patched the issue in GitHub commit 2c74674348a4708ced58ad6eb1b23354df8ee044. 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 members of the 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 The fix for CVE-2020-15209 missed the case when the target shape of Reshape operator is given by the elements of a 1-D tensor. As such, the fix for the vulnerability allowed passing a null-buffer-backed tensor with a 1D shape: Patches We have patched the issue in GitHub commit f8378920345f4f4604202d4ab15ef64b2aceaa16. 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 members of the 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 TFlite graphs must not have loops between nodes. However, this condition was not checked and an attacker could craft models that would result in infinite loop during evaluation. In certain cases, the infinite loop would be replaced by stack overflow due to too many recursive calls. For example, the While implementation could be tricked into a scneario where both the body and the loop subgraphs are the same. Evaluating one of the subgraphs means calling the Eval function for the other and this quickly exhaust all stack space. Patches We have patched the issue in GitHub commit 9c1dc920d8ffb4893d6c9d27d1f039607b326743 (for the While operator) and in GitHub commit c6173f5fe66cdbab74f4f869311fe6aae2ba35f4 (in general). 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…
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 implementations of the Minimum and Maximum TFLite operators can be used to read data outside of bounds of heap allocated objects, if any of the two input tensor arguments are empty. This is because the broadcasting implementation indexes in both tensors with the same index but does not validate that the index is within bounds: Patches We have patched the issue in GitHub commit 953f28dca13c92839ba389c055587cfe6c723578. 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 members of the 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 The reference implementation of the GatherNd TFLite operator is vulnerable to a division by zero error: An attacker can craft a model such that params input would be an empty tensor. In turn, paramsshape.Dims(.) would be zero, in at least one dimension. Patches We have patched the issue in GitHub commit 8e45822aa0b9f5df4b4c64f221e64dc930a70a9d. 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 members of the 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 The optimized implementation of the TransposeConv TFLite operator is vulnerable to a division by zero error: An attacker can craft a model such that stride{h,w} values are 0. Code calling this function must validate these arguments. Patches We have patched the issue in GitHub commit 801c1c6be5324219689c98e1bd3e0ca365ee834d. 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 members of the 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 The Prepare step of the SpaceToDepth TFLite operator does not check for 0 before division. An attacker can craft a model such that params-blocksize would be zero. Patches We have patched the issue in GitHub commit 0d45ea1ca641b21b73bcf9c00e0179cda284e7e7. 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 members of the 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 Optimized pooling implementations in TFLite fail to check that the stride arguments are not 0 before calling ComputePaddingHeightWidth. Since users can craft special models which will have params-stride{height,width} be zero, this will result in a division by zero. Patches We have patched the issue in GitHub commit 5f7975d09eac0f10ed8a17dbb6f5964977725adc. 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 members of the 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 The TFLite computation for size of output after padding, ComputeOutSize, does not check that the stride argument is not 0 before doing the division. Users can craft special models such that ComputeOutSize is called with stride set to 0. Patches We have patched the issue in GitHub commit 49847ae69a4e1a97ae7f2db5e217c77721e37948. 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 members of the 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 trigger a denial of service via a CHECK-fail in caused by an integer overflow in constructing a new tensor shape: This is because the implementation builds a dense shape without checking that the dimensions would not result in overflow: The TensorShape constructor uses a CHECK operation which triggers when InitDims returns a non-OK status. In our scenario, this occurs when adding a dimension from the argument results in overflow: This is a legacy implementation of the constructor and operations should use BuildTensorShapeBase or AddDimWithStatus to prevent CHECK-failures in the presence of overflows. Patches We have patched the issue in GitHub commit 4c0ee937c0f61c4fc5f5d32d9bb4c67428012a60. 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…
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.FusedBatchNorm is vulnerable to a heap buffer overflow: If the tensors are empty, the same implementation can trigger undefined behavior by dereferencing null pointers: The implementation fails to validate that scale, offset, mean and variance (the last two only when required) all have the same number of elements as the number of channels of x. This results in heap out of bounds reads when the buffers backing these tensors are indexed past their boundary. If the tensors are empty, the validation mentioned in the above paragraph would also trigger and prevent the undefined behavior. Patches We have patched the issue in GitHub commit 6972f9dfe325636b3db4e0bc517ee22a159365c0. 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…
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.Dequantize, an attacker can trigger a read from outside of bounds of heap allocated data: The implementation accesses the minrange and maxrange tensors in parallel but fails to check that they have the same shape: Patches We have patched the issue in GitHub commit 5899741d0421391ca878da47907b1452f06aaf1b. 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 Due to lack of validation in tf.rawops.CTCBeamSearchDecoder, an attacker can trigger denial of service via segmentation faults: The implementation fails to detect cases when the input tensor is empty and proceeds to read data from a null buffer. Patches We have patched the issue in GitHub commit b1b323042264740c398140da32e93fb9c2c9f33e. 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 The implementation of tf.rawops.FractionalMaxPoolGrad triggers an undefined behavior if one of the input tensors is empty: The code is also vulnerable to a denial of service attack as a CHECK condition becomes false and aborts the process The implementation fails to validate that input and output tensors are not empty and are of the same rank. Each of these unchecked assumptions is responsible for the above issues. Patches We have patched the issue in GitHub commit 32fdcbff9d06d010d908fcc4bd4b36eb3ce15925. 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.MaxPoolGrad is vulnerable to a heap buffer overflow: The implementation fails to validate that indices used to access elements of input/output arrays are valid: Whereas accesses to inputbackpropflat are guarded by FastBoundsCheck, the indexing in outbackpropflat can result in OOB access. Patches We have patched the issue in GitHub commit a74768f8e4efbda4def9f16ee7e13cf3922ac5f7. 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.FractionalAvgPoolGrad is vulnerable to a heap buffer overflow: The implementation fails to validate that the pooling sequence arguments have enough elements as required by the outbackprop tensor shape. Patches We have patched the issue in GitHub commit 12c727cee857fa19be717f336943d95fca4ffe4f. 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.AvgPool3DGrad is vulnerable to a heap buffer overflow: The implementation assumes that the originputshape and grad tensors have similar first and last dimensions but does not check that this assumption is validated. Patches We have patched the issue in GitHub commit 6fc9141f42f6a72180ecd24021c3e6b36165fe0d. 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 is vulnerable to a heap buffer overflow: The implementation does not check that the initialization of Pool3dParameters completes successfully: Since the constructor uses OPREQUIRES to validate conditions, the first assertion that fails interrupts the initialization of params, making it contain invalid data. In turn, this might cause a heap buffer overflow, depending on default initialized values. Patches We have patched the issue in GitHub commit 63c6a29d0f2d692b247f7bf81f8732d6442fad09. 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 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.