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.

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.linalg.matrix_rank` receives an empty input `a`, the GPU kernel gives a `CHECK` fail that can be used to trigger a denial of service attack. We have patched the issue in GitHub commit c55b476aa0e0bd4ee99d0f3ad18d9d706cd1260a. 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. 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. When `TensorListFromTensor` receives an `element_shape` of a rank greater than one, it gives a `CHECK` fail that can trigger a denial of service attack. We have patched the issue in GitHub commit 3db59a042a38f4338aa207922fa2f476e000a6ee. 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. 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. 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 `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. 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.

The assertion `stmt->Dbc->FirstStmt' failed in MonetDB Database Server v11.43.13.

Transient DOS in modem due to reachable assertion.

There is an Assertion in '(flags >> CBC_STACK_ADJUST_SHIFT) >= CBC_STACK_ADJUST_BASE || (CBC_STACK_ADJUST_BASE - (flags >> CBC_STACK_ADJUST_SHIFT)) <= context_p->stack_depth' in parser_emit_cbc_backward_branch in JerryScript 2.2.0.

Google Chrome before 6.0.472.59 on Linux does not properly handle cursors, which might allow attackers to cause a denial of service (assertion failure) via unspecified vectors.

A flaw was found in the networking subsystem of the Linux kernel within the handling of the RPL protocol. This issue results from the lack of proper handling of user-supplied data, which can lead to an assertion failure. This may allow an unauthenticated remote attacker to create a denial of service condition on the system.

In Libreswan 3.27 an assertion failure can lead to a pluto IKE daemon restart. An attacker can trigger a NULL pointer dereference by initiating an IKEv2 IKE_SA_INIT exchange, followed by a bogus INFORMATIONAL exchange instead of the normallly expected IKE_AUTH exchange. This affects send_v2N_spi_response_from_state() in programs/pluto/ikev2_send.c that will then trigger a NULL pointer dereference leading to a restart of libreswan.

The validateInputImageSize function in modules/imgcodecs/src/loadsave.cpp in OpenCV 3.4.1 allows remote attackers to cause a denial of service (assertion failure) because (pixels <= (1<<30)) may be false. Note: “OpenCV CV_Assert is not an assertion (C-like assert()), it is regular C++ exception which can raised in case of invalid or non-supported parameters.

"deny-answer-aliases" is a little-used feature intended to help recursive server operators protect end users against DNS rebinding attacks, a potential method of circumventing the security model used by client browsers. However, a defect in this feature makes it easy, when the feature is in use, to experience an assertion failure in name.c. Affects BIND 9.7.0->9.8.8, 9.9.0->9.9.13, 9.10.0->9.10.8, 9.11.0->9.11.4, 9.12.0->9.12.2, 9.13.0->9.13.2.

While handling a particular type of malformed packet BIND erroneously selects a SERVFAIL rcode instead of a FORMERR rcode. If the receiving view has the SERVFAIL cache feature enabled, this can trigger an assertion failure in badcache.c when the request doesn't contain all of the expected information. Affects BIND 9.10.5-S1 to 9.10.5-S4, 9.10.6-S1, 9.10.6-S2.

h2o is an HTTP server with support for HTTP/1.x, HTTP/2 and HTTP/3. When h2o is configured as a reverse proxy and HTTP/3 requests are cancelled by the client, h2o might crash due to an assertion failure. The crash can be exploited by an attacker to mount a Denial-of-Service attack. By default, the h2o standalone server automatically restarts, minimizing the impact. However, HTTP requests that were served concurrently will still be disrupted. The vulnerability has been addressed in commit 1ed32b2. Users may disable the use of HTTP/3 to mitigate the issue.

Suricata is a network Intrusion Detection System, Intrusion Prevention System and Network Security Monitoring engine. Prior to version 7.0.7, rules using datasets with the non-functional / unimplemented "unset" option can trigger an assertion during traffic parsing, leading to denial of service. This issue is addressed in 7.0.7. As a workaround, use only trusted and well tested rulesets.

Suricata is a network Intrusion Detection System, Intrusion Prevention System and Network Security Monitoring engine. Prior to version 7.0.7, invalid ALPN in TLS/QUIC traffic when JA4 matching/logging is enabled can lead to Suricata aborting with a panic. This issue has been addressed in 7.0.7. One may disable ja4 as a workaround.

Possible denial of service due to improper validation of DNS response when DNS client requests with PTR, NAPTR or SRV query type in Snapdragon Auto, Snapdragon Compute, Snapdragon Connectivity, Snapdragon Consumer IOT, Snapdragon Industrial IOT

An issue in FlashMQ v1.14.0 allows attackers to cause an assertion failure via sending a crafted retain message, leading to a Denial of Service (DoS).

In BIND 9.16.19, 9.17.16. Also, version 9.16.19-S1 of BIND Supported Preview Edition When a vulnerable version of named receives a query under the circumstances described above, the named process will terminate due to a failed assertion check. The vulnerability affects only BIND 9 releases 9.16.19, 9.17.16, and release 9.16.19-S1 of the BIND Supported Preview Edition.

Possible denial of service scenario due to improper handling of group management action frame in Snapdragon Auto, Snapdragon Compute, Snapdragon Connectivity, Snapdragon Consumer Electronics Connectivity, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon Mobile, Snapdragon Voice & Music, Snapdragon Wired Infrastructure and Networking

Possible denial of service scenario due to improper input validation of received NAS OTA message in Snapdragon Auto, Snapdragon Compute, Snapdragon Connectivity, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon Mobile

Improper handling of received malformed FTMR request frame can lead to reachable assertion while responding with FTM1 frame in Snapdragon Auto, Snapdragon Compute, Snapdragon Connectivity, Snapdragon Consumer Electronics Connectivity, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon Mobile, Snapdragon Voice & Music, Snapdragon Wired Infrastructure and Networking

Possible assertion due to improper verification while creating and deleting the peer in Snapdragon Auto, Snapdragon Compute, Snapdragon Connectivity, Snapdragon Consumer Electronics Connectivity, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon Mobile, Snapdragon Voice & Music, Snapdragon Wired Infrastructure and Networking

In Jasper 4.2.2, the jpc_streamlist_remove function in src/libjasper/jpc/jpc_dec.c:2407 has an assertion failure vulnerability, allowing attackers to cause a denial of service attack through a specific image file.

In BIND 9.15.6 -> 9.16.5, 9.17.0 -> 9.17.3, An attacker who can establish a TCP connection with the server and send data on that connection can exploit this to trigger the assertion failure, causing the server to exit.

An exploitable denial of service vulnerability exists in the atftpd daemon functionality of atftp 0.7.git20120829-3.1+b1. A specially crafted sequence of RRQ-Multicast requests trigger an assert() call resulting in denial-of-service. An attacker can send a sequence of malicious packets to trigger this vulnerability.

The jpc_dequantize function in jpc_dec.c in JasPer 1.900.13 allows remote attackers to cause a denial of service (assertion failure) via unspecified vectors.

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.