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 Withdrawn advisories and news are excluded.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 An attacker can craft a TFLite model that would cause an integer overflow in TfLiteIntArrayCreate: The TfLiteIntArrayGetSizeInBytes returns an int instead of a sizet: An attacker can control model inputs such that computedsize overflows the size of int datatype. Patches We have patched the issue in GitHub commit a1e1511dde36b3f8aa27a6ec630838e7ea40e091. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, 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 Wang Xuan of Qihoo 360 AIVul Team.
Recommended action: Upgrade to 2.7.1, 2.6.3 or 2.5.3 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 trigger denial of service via assertion failure by altering a SavedModel on disk such that AttrDefs of some operation are duplicated. Patches We have patched the issue in GitHub commit c2b31ff2d3151acb230edc3f5b1832d2c713a9e0. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, 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.
Recommended action: Upgrade to 2.7.1, 2.6.3 or 2.5.3 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 craft a TFLite model that would trigger a division by zero in BiasAndClamp implementation: There is no check that the biassize is non zero. Patches We have patched the issue in GitHub commit 8c6f391a2282684a25cbfec7687bd5d35261a209. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, 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 Wang Xuan of Qihoo 360 AIVul Team.
Recommended action: Upgrade to 2.7.1, 2.6.3 or 2.5.3 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 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 before applying the convolution. There is no check before this division that the divisor is stricly positive. Patches We have patched the issue in GitHub commit e5b0eec199c2d03de54fd6a7fd9275692218e2bc. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, 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 Wang Xuan of Qihoo 360 AIVul Team.
Recommended action: Upgrade to 2.7.1, 2.6.3 or 2.5.3 by release line, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.
Impact The implementation of SparseCountSparseOutput is vulnerable to a heap overflow: Patches We have patched the issue in GitHub commits 2b7100d6cdff36aa21010a82269bc05a6d1cc74a and adbbabdb0d3abb3cdeac69e38a96de1d678b24b3. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, 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 Faysal Hossain Shezan from University of Virginia.
Recommended action: Upgrade to 2.7.1, 2.6.3 or 2.5.3 by release line, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.
Impact The implementation of QuantizedMaxPool has an undefined behavior where user controlled inputs can trigger a reference binding to null pointer. Patches We have patched the issue in GitHub commit 53b0dd6dc5957652f35964af16b892ec9af4a559. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, 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 Faysal Hossain Shezan from University of Virginia.
Recommended action: Upgrade to 2.7.1, 2.6.3 or 2.5.3 by release line, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.
Impact The implementation of SparseCountSparseOutput can be made to crash a TensorFlow process by an integer overflow whose result is then used in a memory allocation: Patches We have patched the issue in GitHub commit 6f4d3e8139ec724dbbcb40505891c81dd1052c4a. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, 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 Faysal Hossain Shezan from University of Virginia.
Recommended action: Upgrade to 2.7.1, 2.6.3 or 2.5.3 by release line, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.
Impact The implementation of Bincount operations allows malicious users to cause denial of service by passing in arguments which would trigger a CHECK-fail: There are several conditions that the input arguments must satisfy. Some are not caught during shape inference and others are not caught during kernel implementation. This results in CHECK failures later when the output tensors get allocated. Patches We have patched the issue in GitHub commit 7019ce4f68925fd01cdafde26f8d8c938f47e6f9. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, 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 Faysal Hossain Shezan from University of Virginia.
Recommended action: Upgrade to 2.7.1, 2.6.3 or 2.5.3 by release line, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.
Impact The implementation of SparseTensorSliceDataset has an undefined behavior: under certain condition it can be made to dereference a nullptr value: The 3 input arguments represent a sparse tensor. However, there are some preconditions that these arguments must satisfy but these are not validated in the implementation. Patches We have patched the issue in GitHub commit 965b97e4a9650495cda5a8c210ef6684b4b9eceb. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, 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 Faysal Hossain Shezan from University of Virginia.
Recommended action: Upgrade to 2.7.1, 2.6.3 or 2.5.3 by release line, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.
Impact The implementations of SparseCwise ops are vulnerable to integer overflows. These can be used to trigger large allocations (so, OOM based denial of service) or CHECK-fails when building new TensorShape objects (so, assert failures based denial of service): We are missing some validation on the shapes of the input tensors as well as directly constructing a large TensorShape with user-provided dimensions. The latter is an instance of TFSA-2021-198 (CVE-2021-41197) and is easily fixed by replacing a call to TensorShape constructor with a call to BuildTensorShape static helper factory. Patches We have patched the issue in GitHub commits 1b54cadd19391b60b6fcccd8d076426f7221d5e8 and e952a89b7026b98fe8cbe626514a93ed68b7c510. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range. For more information Please consult our security guide for more…
Recommended action: Upgrade to 2.7.1, 2.6.3 or 2.5.3 by release line, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.
Impact 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): We are missing some validation on the shapes of the input tensors as well as directly constructing a large TensorShape with user-provided dimensions. The latter is an instance of TFSA-2021-198 (CVE-2021-41197) and is easily fixed by replacing a call to TensorShape constructor with a call to BuildTensorShape static helper factory. Patches We have patched the issue in GitHub commits b51b82fe65ebace4475e3c54eb089c18a4403f1c and a68f68061e263a88321c104a6c911fe5598050a8. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, 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…
Recommended action: Upgrade to 2.7.1, 2.6.3 or 2.5.3 by release line, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.
Impact Multiple operations in TensorFlow can be used to trigger a denial of service via CHECK-fails (i.e., assertion failures). This is similar to TFSA-2021-198 (CVE-2021-41197) and has similar fixes. Patches We have patched the reported issues in multiple GitHub commits. It is possible that other similar instances exist in TensorFlow, we will issue fixes as these are discovered. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, 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 Faysal Hossain Shezan from University of Virginia.
Recommended action: Upgrade to 2.7.1, 2.6.3 or 2.5.3 by release line, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.
Impact 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 APIs were still vulnerable to multiple security issues. Note: Given that the boosted trees implementation in TensorFlow is unmaintained, it is recommend to no longer use these APIs. Instead, please use the downstream TensorFlow Decision Forests project which is newer and supports more features. These APIs are now deprecated in TensorFlow 2.8. We will remove TensorFlow's boosted trees APIs in subsequent releases. Patches We have patched the known issues in multiple GitHub commits. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range. This should…
Recommended action: Upgrade to 2.7.1, 2.6.3 or 2.5.3 by release line, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.
Impact The implementation of tf.sparse.split does not fully validate the input arguments. Hence, a malicious user can trigger a denial of service via a segfault or a heap OOB read: The code assumes axis is a scalar. This is another instance of TFSA-2021-190 (CVE-2021-41206). Patches We have patched the issue in GitHub commit 61bf91e768173b001d56923600b40d9a95a04ad5 (merging #53695). The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, 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 externally via a GitHub issue.
Recommended action: Upgrade to 2.7.1, 2.6.3 or 2.5.3 by release line, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.
Impact The Grappler component of TensorFlow is vulnerable to a denial of service via CHECK-failure (assertion failure) in constant folding: The outputprop tensor has a shape that is controlled by user input and this can result in triggering one of the CHECKs in the PartialTensorShape constructor. This is an instance of TFSA-2021-198 (CVE-2021-41197). Patches We have patched the issue in GitHub commit be7b286d40bc68cb0b56f702186cc4837d508058. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, 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.
Recommended action: Upgrade to 2.7.1, 2.6.3 or 2.5.3 by release line, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.
Impact When decoding a tensor from protobuf, TensorFlow might do a null-dereference if attributes of some mutable arguments to some operations are missing from the proto. This is guarded by a DCHECK: However, DCHECK is a no-op in production builds and an assertion failure in debug builds. In the first case execution proceeds to the dereferencing of the null pointer, whereas in the second case it results in a crash due to the assertion failure. Patches We have patched the issue in GitHub commit 8a513cec4bec15961fbfdedcaa5376522980455c. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, and TensorFlow 2.6.3, 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.
Recommended action: Upgrade to 2.7.1, 2.6.3 or 2.5.3 by release line, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.
Impact When building an XLA compilation cache, if default settings are used, TensorFlow triggers a null pointer dereference: In the default scenario, all devices are allowed, so flr-configproto is nullptr. Patches We have patched the issue in GitHub commit e21af685e1828f7ca65038307df5cc06de4479e8. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, 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.
Recommended action: Upgrade to 2.7.1, 2.6.3 or 2.5.3 by release line, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.
Impact 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 cause a crash in the Python interpreter. Under certain scenarios, heap OOB read/writes are possible. These issues have been discovered via fuzzing and it is possible that more weaknesses exist. We will patch them as they are discovered. Patches We have patched the issue in multiple GitHub commits and these will be included in TensorFlow 2.8.0 and TensorFlow 2.7.1, as both are 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.
Recommended action: Upgrade to 2.7.1 or later, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.
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: This would result in a stack overflow during execution as resolving each NodeDef means resolving the function itself and its nodes. Patches We have patched the issue in GitHub commit 448a16182065bd08a202d9057dd8ca541e67996c. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, 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.
Recommended action: Upgrade to 2.7.1, 2.6.3 or 2.5.3 by release line, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.
Impact 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: If ctortype is an error status, ValueOrDie results in a crash. Patches We have patched the issue in GitHub commit 955059813cc325dc1db5e2daa6221271406d4439. We have patched the issue in multiple GitHub commits and these will be included in TensorFlow 2.8.0 and TensorFlow 2.7.1, as both are 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.
Recommended action: Upgrade to 2.7.1 or later, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.
Impact Under certain scenarios, Grappler component of TensorFlow can trigger a null pointer dereference. There are 2 places where this can occur, for the same malicious alteration of a SavedModel file (fixing the first one would trigger the same dereference in the second place): First, during constant folding, the GraphDef might not have the required nodes for the binary operation: If a node is missing, the correposning mulchild would be null, and the dereference in the subsequent line would be incorrect. We have a similar issue during IsIdentityConsumingSwitch: Patches We have patched the issue in GitHub commits 0a365c029e437be0349c31f8d4c9926b69fa3fa1 and 045deec1cbdebb27d817008ad5df94d96a08b1bf. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, 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 2.7.1, 2.6.3 or 2.5.3 by release line, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.
Impact When decoding a tensor from protobuf, a TensorFlow process can encounter cases where a CHECK assertion is invalidated based on user controlled arguments, if the tensors have an invalid dtype and 0 elements or an invalid shape. This allows attackers to cause denial of services in TensorFlow processes. Patches We have patched the issue in GitHub commit 5b491cd5e41ad63735161cec9c2a568172c8b6a3. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, 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.
Recommended action: Upgrade to 2.7.1, 2.6.3 or 2.5.3 by release line, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.
Impact Under certain scenarios, TensorFlow can fail to specialize a type during shape inference: However, DCHECK is a no-op in production builds and an assertion failure in debug builds. In the first case execution proceeds to the ValueOrDie line. This results in an assertion failure as ret contains an error Status, not a value. In the second case we also get a crash due to the assertion failure. Patches We have patched the issue in GitHub commit cb164786dc891ea11d3a900e90367c339305dc7b. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, and TensorFlow 2.6.3, 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.
Recommended action: Upgrade to 2.7.1, 2.6.3 or 2.5.3 by release line, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.
Impact A malicious user can cause a denial of service by altering a SavedModel such that Grappler optimizer would attempt to build a tensor using a reference dtype. This would result in a crash due to a CHECK-fail in the Tensor constructor as reference types are not allowed. Patches We have patched the issue in GitHub commit 6b5adc0877de832b2a7c189532dbbbc64622eeb6. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, 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.
Recommended action: Upgrade to 2.7.1, 2.6.3 or 2.5.3 by release line, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.
Impact Under certain scenarios, Grappler component of TensorFlow is vulnerable to an integer overflow during cost estimation for crop and resize. Since the cropping parameters are user controlled, a malicious person can trigger undefined behavior. Patches We have patched the issue in GitHub commit 0aaaae6eca5a7175a193696383f582f53adab23f. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, 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.
Recommended action: Upgrade to 2.7.1, 2.6.3 or 2.5.3 by release line, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.