go-unixfs is an implementation of a unix-like filesystem on top of an ipld merkledag. Trying to read malformed HAMT sharded directories can cause panics and virtual memory leaks. If you are reading untrusted user input, an attacker can then trigger a panic. This is caused by bogus `fanout` parameter in the HAMT directory nodes. Users are advised to upgrade to version 0.4.3 to resolve this issue. Users unable to upgrade should not feed untrusted user data to the decoding functions.

github.com/ipfs/go-unixfsnode is an ADL IPLD prime node that wraps go-codec-dagpb's implementation of protobuf to enable pathing. In versions priot to 1.5.2 trying to read malformed HAMT sharded directories can cause panics and virtual memory leaks. If you are reading untrusted user input, an attacker can then trigger a panic. This is caused by bogus fanout parameter in the HAMT directory nodes. Users are advised to upgrade. There are no known workarounds for this vulnerability.

go-bitfield is a simple bitfield package for the go language aiming to be more performant that the standard library. When feeding untrusted user input into the size parameter of `NewBitfield` and `FromBytes` functions, an attacker can trigger `panic`s. This happen when the `size` is a not a multiple of `8` or is negative. There were already a note in the `NewBitfield` documentation, however known users of this package are subject to this issue. Users are advised to upgrade. Users unable to upgrade should ensure that `size` is a multiple of 8 before calling `NewBitfield` or `FromBytes`.

libp2p-rust is the official rust language Implementation of the libp2p networking stack. In versions prior to 0.49.3, the Gossipsub implementation accepts attacker-controlled PRUNE backoff values and may perform unchecked time arithmetic when storing backoff state. A specially crafted PRUNE control message with an extremely large backoff (e.g. u64::MAX) can lead to Duration/Instant overflow during backoff update logic, triggering a panic in the networking state machine. This is remotely reachable over a normal libp2p connection and does not require authentication. Any application exposing a libp2p Gossipsub listener and using the affected backoff-handling path can be crashed by a network attacker that can reach the service port. The attack can be repeated by reconnecting and replaying the crafted control message. This issue has been fixed in version 0.49.3.

Yamux is a stream multiplexer over reliable, ordered connections such as TCP/IP. From 0.13.0 to before 0.13.9, a specially crafted WindowUpdate can cause arithmetic overflow in send-window accounting, which triggers a panic in the connection state machine. This is remotely reachable over a normal network connection and does not require authentication. This vulnerability is fixed in 0.13.9.

TensorFlow is an open source platform for machine learning. The implementation of `Conv2DBackpropInput` requires `input_sizes` to be 4-dimensional. Otherwise, it gives a `CHECK` failure which can be used to trigger a denial of service attack. We have patched the issue in GitHub commit 50156d547b9a1da0144d7babe665cf690305b33c. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue.

TensorFlow is an open source platform for machine learning. `FractionalMaxPoolGrad` validates its inputs with `CHECK` failures instead of with returning errors. If it gets incorrectly sized inputs, the `CHECK` failure can be used to trigger a denial of service attack. We have patched the issue in GitHub commit 8741e57d163a079db05a7107a7609af70931def4. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue.

TensorFlow is an open source platform for machine learning. In `core/kernels/list_kernels.cc's TensorListReserve`, `num_elements` is assumed to be a tensor of size 1. When a `num_elements` of more than 1 element is provided, then `tf.raw_ops.TensorListReserve` fails the `CHECK_EQ` in `CheckIsAlignedAndSingleElement`. We have patched the issue in GitHub commit b5f6fbfba76576202b72119897561e3bd4f179c7. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue.

TensorFlow is an open source platform for machine learning. `ParameterizedTruncatedNormal` assumes `shape` is of type `int32`. A valid `shape` of type `int64` results in a mismatched type `CHECK` fail that can be used to trigger a denial of service attack. We have patched the issue in GitHub commit 72180be03447a10810edca700cbc9af690dfeb51. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue.

TensorFlow is an open source platform for machine learning. When `SetSize` receives an input `set_shape` that is not a 1D tensor, it gives a `CHECK` fails that can be used to trigger a denial of service attack. We have patched the issue in GitHub commit cf70b79d2662c0d3c6af74583641e345fc939467. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue.

TensorFlow is an open source platform for machine learning. The implementation of SobolSampleOp is vulnerable to a denial of service via CHECK-failure (assertion failure) caused by assuming `input(0)`, `input(1)`, and `input(2)` to be scalar. This issue has been patched in GitHub commit c65c67f88ad770662e8f191269a907bf2b94b1bf. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue.

TensorFlow is an open source platform for machine learning. If `Save` or `SaveSlices` is run over tensors of an unsupported `dtype`, it results in a `CHECK` fail that can be used to trigger a denial of service attack. We have patched the issue in GitHub commit 5dd7b86b84a864b834c6fa3d7f9f51c87efa99d4. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue.

TensorFlow is an open source platform for machine learning. `DenseBincount` assumes its input tensor `weights` to either have the same shape as its input tensor `input` or to be length-0. A different `weights` shape will trigger a `CHECK` fail that can be used to trigger a denial of service attack. We have patched the issue in GitHub commit bf4c14353c2328636a18bfad1e151052c81d5f43. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue.

TensorFlow is an open source platform for machine learning. When `mlir::tfg::ConvertGenericFunctionToFunctionDef` is given empty function attributes, it crashes. We have patched the issue in GitHub commit ad069af92392efee1418c48ff561fd3070a03d7b. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue.

TensorFlow is an open source platform for machine learning. When `tf.quantization.fake_quant_with_min_max_vars_gradient` receives input `min` or `max` that is nonscalar, it gives a `CHECK` fail that can trigger a denial of service attack. We have patched the issue in GitHub commit f3cf67ac5705f4f04721d15e485e192bb319feed. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue.

TensorFlow is an open source platform for machine learning. If `EmptyTensorList` receives an input `element_shape` with more than one dimension, it gives a `CHECK` fail that can be used to trigger a denial of service attack. We have patched the issue in GitHub commit c8ba76d48567aed347508e0552a257641931024d. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue.

TensorFlow is an open source platform for machine learning. If `tf.sparse.cross` receives an input `separator` that is not a scalar, it gives a `CHECK` fail that can be used to trigger a denial of service attack. We have patched the issue in GitHub commit 83dcb4dbfa094e33db084e97c4d0531a559e0ebf. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue.

TensorFlow is an open source platform for machine learning. The `RaggedRangOp` function takes an argument `limits` that is eventually used to construct a `TensorShape` as an `int64`. If `limits` is a very large float, it can overflow when converted to an `int64`. This triggers an `InvalidArgument` but also throws an abort signal that crashes the program. We have patched the issue in GitHub commit 37cefa91bee4eace55715eeef43720b958a01192. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue.

TensorFlow is an open source platform for machine learning. If `FakeQuantWithMinMaxVarsPerChannel` is given `min` or `max` tensors of a rank other than one, it results in a `CHECK` fail that can be used to trigger a denial of service attack. We have patched the issue in GitHub commit 785d67a78a1d533759fcd2f5e8d6ef778de849e0. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue.

TensorFlow is an open source platform for machine learning. When `Unbatch` receives a nonscalar input `id`, it gives a `CHECK` fail that can trigger a denial of service attack. We have patched the issue in GitHub commit 4419d10d576adefa36b0e0a9425d2569f7c0189f. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue.

TensorFlow is an open source platform for machine learning. The implementation of `FractionalAvgPoolGrad` does not fully validate the input `orig_input_tensor_shape`. This results in an overflow that results in a `CHECK` failure which can be used to trigger a denial of service attack. We have patched the issue in GitHub commit 03a659d7be9a1154fdf5eeac221e5950fec07dad. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue.

ImageMagick is free and open-source software used for editing and manipulating digital images. In versions below both 7.1.2-19 and 6.9.13-44, the viff encoder contains an integer truncation/wraparound issue on 32-bit builds that could trigger an out of bounds heap write, potentially causing a crash. This issue has been fixed in versions 6.9.13-44 and 7.1.2-19.

TensorFlow is an open source platform for machine learning. When `RandomPoissonV2` receives large input shape and rates, it gives a `CHECK` fail that can trigger a denial of service attack. We have patched the issue in GitHub commit 552bfced6ce4809db5f3ca305f60ff80dd40c5a3. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue.

TensorFlow is an open source platform for machine learning. When `TensorListScatter` and `TensorListScatterV2` receive an `element_shape` of a rank greater than one, they give a `CHECK` fail that can trigger a denial of service attack. We have patched the issue in GitHub commit bb03fdf4aae944ab2e4b35c7daa051068a8b7f61. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue.

TensorFlow is an open source platform for machine learning. When `RangeSize` receives values that do not fit into an `int64_t`, it crashes. We have patched the issue in GitHub commit 37e64539cd29fcfb814c4451152a60f5d107b0f0. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue.

TensorFlow is an open source platform for machine learning. When `AudioSummaryV2` receives an input `sample_rate` with more than one element, it gives a `CHECK` fails that can be used to trigger a denial of service attack. We have patched the issue in GitHub commit bf6b45244992e2ee543c258e519489659c99fb7f. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue.

TensorFlow is an open source platform for machine learning. When `tf.random.gamma` receives large input shape and rates, it gives a `CHECK` fail that can trigger a denial of service attack. We have patched the issue in GitHub commit 552bfced6ce4809db5f3ca305f60ff80dd40c5a3. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue.

Integer wraparound in multiple PostgreSQL libpq client library functions allows an application input provider or network peer to cause libpq to undersize an allocation and write out-of-bounds by hundreds of megabytes. This results in a segmentation fault for the application using libpq. Versions before PostgreSQL 18.1, 17.7, 16.11, 15.15, 14.20, and 13.23 are affected.

yajl-ruby is a C binding to the YAJL JSON parsing and generation library. The 1.x branch and the 2.x branch of `yajl` contain an integer overflow which leads to subsequent heap memory corruption when dealing with large (~2GB) inputs. The reallocation logic at `yajl_buf.c#L64` may result in the `need` 32bit integer wrapping to 0 when `need` approaches a value of 0x80000000 (i.e. ~2GB of data), which results in a reallocation of buf->alloc into a small heap chunk. These integers are declared as `size_t` in the 2.x branch of `yajl`, which practically prevents the issue from triggering on 64bit platforms, however this does not preclude this issue triggering on 32bit builds on which `size_t` is a 32bit integer. Subsequent population of this under-allocated heap chunk is based on the original buffer size, leading to heap memory corruption. This vulnerability mostly impacts process availability. Maintainers believe exploitation for arbitrary code execution is unlikely. A patch is available and anticipated to be part of yajl-ruby version 1.4.2. As a workaround, avoid passing large inputs to YAJL.

An exploitable denial of service vulnerability exists within the handling of security data in FreeRDP 2.0.0-beta1+android11. A specially crafted challenge packet can cause the program termination leading to a denial of service condition. An attacker can compromise the server or use man in the middle to trigger this vulnerability.

An exploitable denial of service vulnerability exists within the handling of challenge packets in FreeRDP 2.0.0-beta1+android11. A specially crafted challenge packet can cause the program termination leading to a denial of service condition. An attacker can compromise the server or use man in the middle to trigger this vulnerability.

go-tuf is a Go implementation of The Update Framework (TUF). Starting in version 2.0.0 and prior to version 2.3.1, if the TUF repository (or any of its mirrors) returns invalid TUF metadata JSON (valid JSON but not well formed TUF metadata), the client will panic during parsing, causing a denial of service. The panic happens before any signature is validated. This means that a compromised repository/mirror/cache can DoS clients without having access to any signing key. Version 2.3.1 fixes the issue. No known workarounds are available.

A vulnerability has been found in Open5GS up to 2.7.6. Affected is the function ogs_pfcp_pdr_find_or_add/ogs_pfcp_far_find_or_add/ogs_pfcp_urr_find_or_add/ogs_pfcp_qer_find_or_add in the library lib/pfcp/context.c of the component QER/FAR/URR/PDR. The manipulation leads to reachable assertion. It is possible to initiate the attack remotely. The attack's complexity is rated as high. The exploitability is told to be difficult. The exploit has been disclosed to the public and may be used. The identifier of the patch is 442369dcd964f03d95429a6a01a57ed21f7779b7. Applying a patch is the recommended action to fix this issue.

A vulnerability, which was classified as problematic, was found in Axiomatic Bento4 up to 1.6.0-641. Affected is the function AP4_DataBuffer::SetDataSize of the file Mp4Decrypt.cpp of the component mp4decrypt. The manipulation leads to allocation of resources. It is possible to launch the attack remotely. The complexity of an attack is rather high. The exploitability is told to be difficult. The exploit has been disclosed to the public and may be used.

An issue was discovered in uriparser through 0.9.7. ComposeQueryMallocExMm in UriQuery.c has an integer overflow via a long string.

Fast DDS is a C++ implementation of the DDS (Data Distribution Service) standard of the OMG (Object Management Group ). Prior to versions 3.4.1, 3.3.1, and 2.6.11, when the security mode is enabled, modifying the DATA Submessage within an SPDP packet sent by a publisher causes an Out-Of-Memory (OOM) condition, resulting in remote termination of Fast-DDS. If t he fields of `PID_IDENTITY_TOKEN` or `PID_PERMISSIONS_TOKEN` in the DATA Submessage are tampered with — specifically by ta mpering with the the `vecsize` value read by `readOctetVector` — a 32-bit integer overflow can occur, causing `std::vector ::resize` to request an attacker-controlled size and quickly trigger OOM and remote process termination. Versions 3.4.1, 3 .3.1, and 2.6.11 patch the issue.

A race condition which may occur when discarding malformed packets can result in BIND exiting due to a REQUIRE assertion failure in dispatch.c. Versions affected: BIND 9.11.0 -> 9.11.7, 9.12.0 -> 9.12.4-P1, 9.14.0 -> 9.14.2. Also all releases of the BIND 9.13 development branch and version 9.15.0 of the BIND 9.15 development branch and BIND Supported Preview Edition versions 9.11.3-S1 -> 9.11.7-S1.

A defect in code added to support QNAME minimization can cause named to exit with an assertion failure if a forwarder returns a referral rather than resolving the query. This affects BIND versions 9.14.0 up to 9.14.6, and 9.15.0 up to 9.15.4.

hyper is an HTTP library for Rust. In versions prior to 0.14.10, hyper's HTTP server and client code had a flaw that could trigger an integer overflow when decoding chunk sizes that are too big. This allows possible data loss, or if combined with an upstream HTTP proxy that allows chunk sizes larger than hyper does, can result in "request smuggling" or "desync attacks." The vulnerability is patched in version 0.14.10. Two possible workarounds exist. One may reject requests manually that contain a `Transfer-Encoding` header or ensure any upstream proxy rejects `Transfer-Encoding` chunk sizes greater than what fits in 64-bit unsigned integers.

Redis before 6cbea7d allows a replica to cause an assertion failure in a primary server by sending a non-administrative command (specifically, a SET command). NOTE: this was fixed for Redis 6.2.x and 7.x in 2021. Versions before 6.2 were not intended to have safety guarantees related to this.

Pexip Infinity 32.0 through 37.1 before 37.2, in certain configurations of OTJ (One Touch Join) for Teams SIP Guest Join, has Improper Input Validation in the OTJ service, allowing a remote attacker to trigger a software abort via a crafted calendar invite, leading to a denial of service.

In PHP versions 7.3.x below 7.3.29, 7.4.x below 7.4.21 and 8.0.x below 8.0.8, when using Firebird PDO driver extension, a malicious database server could cause crashes in various database functions, such as getAttribute(), execute(), fetch() and others by returning invalid response data that is not parsed correctly by the driver. This can result in crashes, denial of service or potentially memory corruption.

Using a specially-crafted message, an attacker may potentially cause a BIND server to reach an inconsistent state if the attacker knows (or successfully guesses) the name of a TSIG key used by the server. Since BIND, by default, configures a local session key even on servers whose configuration does not otherwise make use of it, almost all current BIND servers are vulnerable. In releases of BIND dating from March 2018 and after, an assertion check in tsig.c detects this inconsistent state and deliberately exits. Prior to the introduction of the check the server would continue operating in an inconsistent state, with potentially harmful results.

When a specific BGP flowspec configuration is enabled and upon receipt of a specific matching BGP packet meeting a specific term in the flowspec configuration, a reachable assertion failure occurs, causing the routing protocol daemon (rpd) process to crash with a core file being generated. Affected releases are Juniper Networks Junos OS: 12.1X46 versions prior to 12.1X46-D77 on SRX Series; 12.3 versions prior to 12.3R12-S10; 12.3X48 versions prior to 12.3X48-D70 on SRX Series; 14.1X53 versions prior to 14.1X53-D47 on EX2200/VC, EX3200, EX3300/VC, EX4200, EX4300, EX4550/VC, EX4600, EX6200, EX8200/VC (XRE), QFX3500, QFX3600, QFX5100; 15.1 versions prior to 15.1R3; 15.1F versions prior to 15.1F3; 15.1X49 versions prior to 15.1X49-D140 on SRX Series; 15.1X53 versions prior to 15.1X53-D59 on EX2300/EX3400.

TensorFlow is an open source platform for machine learning. The implementation of tf.reshape op in TensorFlow is vulnerable to a denial of service via CHECK-failure (assertion failure) caused by overflowing the number of elements in a tensor. This issue has been patched in GitHub commit 61f0f9b94df8c0411f0ad0ecc2fec2d3f3c33555. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue.

TensorFlow is an open source platform for machine learning. When `Conv2DBackpropInput` receives empty `out_backprop` inputs (e.g. `[3, 1, 0, 1]`), the current CPU/GPU kernels `CHECK` fail (one with dnnl, the other with cudnn). This can be used to trigger a denial of service attack. We have patched the issue in GitHub commit 27a65a43cf763897fecfa5cdb5cc653fc5dd0346. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue.

TensorFlow is an open source platform for machine learning. The implementation of `AvgPool3DGradOp` does not fully validate the input `orig_input_shape`. This results in an overflow that results in a `CHECK` failure which can be used to trigger a denial of service attack. We have patched the issue in GitHub commit 9178ac9d6389bdc54638ab913ea0e419234d14eb. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue.

TensorFlow is an open source platform for machine learning. The `UnbatchGradOp` function takes an argument `id` that is assumed to be a scalar. A nonscalar `id` can trigger a `CHECK` failure and crash the program. It also requires its argument `batch_index` to contain three times the number of elements as indicated in its `batch_index.dim_size(0)`. An incorrect `batch_index` can trigger a `CHECK` failure and crash the program. We have patched the issue in GitHub commit 5f945fc6409a3c1e90d6970c9292f805f6e6ddf2. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue.

TensorFlow is an open source platform for machine learning. When `tf.quantization.fake_quant_with_min_max_vars_per_channel_gradient` receives input `min` or `max` of rank other than 1, it gives a `CHECK` fail that can trigger a denial of service attack. We have patched the issue in GitHub commit f3cf67ac5705f4f04721d15e485e192bb319feed. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range.There are no known workarounds for this issue.

TensorFlow is an open source platform for machine learning. If `LRNGrad` is given an `output_image` input tensor that is not 4-D, it results in a `CHECK` fail that can be used to trigger a denial of service attack. We have patched the issue in GitHub commit bd90b3efab4ec958b228cd7cfe9125be1c0cf255. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue.