| CVE |
Vendors |
Products |
Updated |
CVSS v3.1 |
| Live555 through 1.08 mishandles huge requests for the same MP3 stream, leading to recursion and s stack-based buffer over-read. An attacker can leverage this to launch a DoS attack. |
| fs/nfsd/trace.h in the Linux kernel before 5.13.4 might allow remote attackers to cause a denial of service (out-of-bounds read in strlen) by sending NFS traffic when the trace event framework is being used for nfsd. |
| read_header_tga in gd_tga.c in the GD Graphics Library (aka LibGD) through 2.3.2 allows remote attackers to cause a denial of service (out-of-bounds read) via a crafted TGA file. |
| Corel DrawStandard 2020 22.0.0.474 is affected by an Out-of-bounds Read vulnerability when parsing a crafted file. An unauthenticated attacker could leverage this vulnerability to access unauthorized system memory in the context of the current user. Exploitation of this issue requires user interaction in that a victim must open a malicious CDR file. |
| Word97Import200.dll in Corel WordPerfect 2020 20.0.0.200 is affected by an Out-of-bounds Read vulnerability when parsing a crafted file. An unauthenticated attacker could leverage this vulnerability to access unauthorized system memory in the context of the current user. Exploitation of this issue requires user interaction in that a victim must open a malicious DOC file. |
| CdrCore.dll in Corel DrawStandard 2020 22.0.0.474 is affected by an Out-of-bounds Read vulnerability when parsing a crafted file. An unauthenticated attacker could leverage this vulnerability to access unauthorized system memory in the context of the current user. Exploitation of this issue requires user interaction in that a victim must open a malicious CDR file. |
| UAX200.dll in Corel Presentations 2020 20.0.0.200 is affected by an Out-of-bounds Read vulnerability when parsing a crafted file. An unauthenticated attacker could leverage this vulnerability to access unauthorized system memory in the context of the current user. Exploitation of this issue requires user interaction in that a victim must open a malicious PPT file. |
| IPPP82.FLT in Corel Presentations 2020 20.0.0.200 is affected by an Out-of-bounds Read vulnerability when parsing a crafted file. An unauthenticated attacker could leverage this vulnerability to access unauthorized system memory in the context of the current user. Exploitation of this issue requires user interaction in that a victim must open a malicious PPT file. This is different from CVE-2021-38102. |
| IPPP72.FLT in Corel Presentations 2020 20.0.0.200 is affected by an Out-of-bounds Read vulnerability when parsing a crafted file. An unauthenticated attacker could leverage this vulnerability to access unauthorized system memory in the context of the current user. Exploitation of this issue requires user interaction in that a victim must open a malicious PPT file. |
| IPPP82.FLT in Corel Presentations 2020 20.0.0.200 is affected by an Out-of-bounds Read vulnerability when parsing a crafted file. An unauthenticated attacker could leverage this vulnerability to access unauthorized system memory in the context of the current user. Exploitation of this issue requires user interaction in that a victim must open a malicious PPT file. This is different from CVE-2021-38105. |
| Out of bounds read in WebAudio in Google Chrome prior to 95.0.4638.54 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page. |
| Out of bounds read in libjpeg-turbo in Google Chrome prior to 94.0.4606.54 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page. |
| TensorFlow is an end-to-end open source platform for machine learning. In affected versions TFLite's [`GatherNd` implementation](https://github.com/tensorflow/tensorflow/blob/149562d49faa709ea80df1d99fc41d005b81082a/tensorflow/lite/kernels/gather_nd.cc#L124) does not support negative indices but there are no checks for this situation. Hence, an attacker can read arbitrary data from the heap by carefully crafting a model with negative values in `indices`. Similar issue exists in [`Gather` implementation](https://github.com/tensorflow/tensorflow/blob/149562d49faa709ea80df1d99fc41d005b81082a/tensorflow/lite/kernels/gather.cc). We have patched the issue in GitHub commits bb6a0383ed553c286f87ca88c207f6774d5c4a8f and eb921122119a6b6e470ee98b89e65d721663179d. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. |
| TensorFlow is an end-to-end open source platform for machine learning. In affected versions TFLite's [`expand_dims.cc`](https://github.com/tensorflow/tensorflow/blob/149562d49faa709ea80df1d99fc41d005b81082a/tensorflow/lite/kernels/expand_dims.cc#L36-L50) contains a vulnerability which allows reading one element outside of bounds of heap allocated data. If `axis` is a large negative value (e.g., `-100000`), then after the first `if` it would still be negative. The check following the `if` statement will pass and the `for` loop would read one element before the start of `input_dims.data` (when `i = 0`). We have patched the issue in GitHub commit d94ffe08a65400f898241c0374e9edc6fa8ed257. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. |
| TensorFlow is an end-to-end open source platform for machine learning. In affected versions it is possible to nest a `tf.map_fn` within another `tf.map_fn` call. However, if the input tensor is a `RaggedTensor` and there is no function signature provided, code assumes the output is a fully specified tensor and fills output buffer with uninitialized contents from the heap. The `t` and `z` outputs should be identical, however this is not the case. The last row of `t` contains data from the heap which can be used to leak other memory information. The bug lies in the conversion from a `Variant` tensor to a `RaggedTensor`. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/ragged_tensor_from_variant_op.cc#L177-L190) does not check that all inner shapes match and this results in the additional dimensions. The same implementation can result in data loss, if input tensor is tweaked. We have patched the issue in GitHub commit 4e2565483d0ffcadc719bd44893fb7f609bb5f12. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. |
| TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can read from outside of bounds of heap allocated data by sending specially crafted illegal arguments to `tf.raw_ops.SdcaOptimizerV2`. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/sdca_internal.cc#L320-L353) does not check that the length of `example_labels` is the same as the number of examples. We have patched the issue in GitHub commit a4e138660270e7599793fa438cd7b2fc2ce215a6. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. |
| TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can read from outside of bounds of heap allocated data by sending specially crafted illegal arguments to `tf.raw_ops.UpperBound`. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/searchsorted_op.cc#L85-L104) does not validate the rank of `sorted_input` argument. A similar issue occurs in `tf.raw_ops.LowerBound`. We have patched the issue in GitHub commit 42459e4273c2e47a3232cc16c4f4fff3b3a35c38. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. |
| TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can read from outside of bounds of heap allocated data by sending specially crafted illegal arguments to `BoostedTreesSparseCalculateBestFeatureSplit`. The [implementation](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/boosted_trees/stats_ops.cc) needs to validate that each value in `stats_summary_indices` is in range. We have patched the issue in GitHub commit e84c975313e8e8e38bb2ea118196369c45c51378. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. |
| TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can cause undefined behavior via binding a reference to null pointer in all binary cwise operations that don't require broadcasting (e.g., gradients of binary cwise operations). The [implementation](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/cwise_ops_common.h#L264) assumes that the two inputs have exactly the same number of elements but does not check that. Hence, when the eigen functor executes it triggers heap OOB reads and undefined behavior due to binding to nullptr. We have patched the issue in GitHub commit 93f428fd1768df147171ed674fee1fc5ab8309ec. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. |
| TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can trigger a read from outside of bounds of heap allocated data by sending invalid arguments to `tf.raw_ops.ResourceScatterUpdate`. The [implementation](https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/resource_variable_ops.cc#L919-L923) has an incomplete validation of the relationship between the shapes of `indices` and `updates`: instead of checking that the shape of `indices` is a prefix of the shape of `updates` (so that broadcasting can happen), code only checks that the number of elements in these two tensors are in a divisibility relationship. We have patched the issue in GitHub commit 01cff3f986259d661103412a20745928c727326f. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. |
