In keymange, there is a possible out of bounds write due to a missing bounds check. This could lead to local escalation of privilege with System execution privileges needed. User interaction is not needed for exploitation. Patch ID: ALPS07826586; Issue ID: ALPS07826586.

Adobe Flash Player before 18.0.0.329 and 19.x and 20.x before 20.0.0.306 on Windows and OS X and before 11.2.202.569 on Linux, Adobe AIR before 20.0.0.260, Adobe AIR SDK before 20.0.0.260, and Adobe AIR SDK & Compiler before 20.0.0.260 allow attackers to execute arbitrary code or cause a denial of service (memory corruption) via unspecified vectors, a different vulnerability than CVE-2016-0964, CVE-2016-0965, CVE-2016-0966, CVE-2016-0967, CVE-2016-0969, CVE-2016-0970, CVE-2016-0972, CVE-2016-0976, CVE-2016-0977, CVE-2016-0978, CVE-2016-0979, CVE-2016-0980, and CVE-2016-0981.

TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a heap buffer overflow by passing crafted inputs to `tf.raw_ops.StringNGrams`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/1cdd4da14282210cc759e468d9781741ac7d01bf/tensorflow/core/kernels/string_ngrams_op.cc#L171-L185) fails to consider corner cases where input would be split in such a way that the generated tokens should only contain padding elements. If input is such that `num_tokens` is 0, then, for `data_start_index=0` (when left padding is present), the marked line would result in reading `data[-1]`. 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.

TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.ReverseSequence` allows for stack overflow and/or `CHECK`-fail based denial of service. The implementation(https://github.com/tensorflow/tensorflow/blob/5b3b071975e01f0d250c928b2a8f901cd53b90a7/tensorflow/core/kernels/reverse_sequence_op.cc#L114-L118) fails to validate that `seq_dim` and `batch_dim` arguments are valid. Negative values for `seq_dim` 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 `batch_dim`. 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.

In all Qualcomm products with Android releases from CAF using the Linux kernel, there is a TOCTOU race condition in Secure UI.

In imgsys, there is a possible memory corruption due to improper input validation. This could lead to local escalation of privilege with System execution privileges needed. User interaction is needed for exploitation. Patch ID: ALPS07420968; Issue ID: ALPS07420976.

cdf_read_property_info in cdf.c in file through 5.37 does not restrict the number of CDF_VECTOR elements, which allows a heap-based buffer overflow (4-byte out-of-bounds write).

log.c in Squid Analysis Report Generator (sarg) through 2.3.11 allows local privilege escalation. By default, it uses a fixed temporary directory /tmp/sarg. As the root user, sarg creates this directory or reuses an existing one in an insecure manner. An attacker can pre-create the directory, and place symlinks in it (after winning a /tmp/sarg/denied.int_unsort race condition). The outcome will be corrupted or newly created files in privileged file system locations.

TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a heap buffer overflow in `tf.raw_ops.SparseSplit`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/699bff5d961f0abfde8fa3f876e6d241681fbef8/tensorflow/core/util/sparse/sparse_tensor.h#L528-L530) accesses an array element based on a user controlled offset. 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.

there is a possible out of bounds write due to buffer overflow. This could lead to remote code execution with no additional execution privileges needed. User interaction is not needed for exploitation.

In avdt_msg_asmbl of avdt_msg.cc, there is a possible out of bounds write due to an integer overflow. This could lead to paired device escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.MaxPool3DGradGrad` is vulnerable to a heap buffer overflow. The implementation(https://github.com/tensorflow/tensorflow/blob/596c05a159b6fbb9e39ca10b3f7753b7244fa1e9/tensorflow/core/kernels/pooling_ops_3d.cc#L694-L696) does not check that the initialization of `Pool3dParameters` completes successfully. Since the constructor(https://github.com/tensorflow/tensorflow/blob/596c05a159b6fbb9e39ca10b3f7753b7244fa1e9/tensorflow/core/kernels/pooling_ops_3d.cc#L48-L88) uses `OP_REQUIRES` 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. 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.

In camsys, there is a possible use after free due to a race condition. This could lead to local escalation of privilege with System execution privileges needed. User interaction is not needed for exploitation. Patch ID: ALPS07341261; Issue ID: ALPS07326570.

In Import of C2SurfaceSyncObj.cpp, there is a possible out of bounds write due to a missing bounds check. This could lead to local information disclosure with System execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android-12 Android-12L Android-13Android ID: A-240140929

In tbd of tbd, there is a possible memory corruption due to a race condition. This could lead to local escalation of privilege with System execution privileges needed. User interaction is not needed for exploitation.

Google Chrome before 11.0.696.71 does not properly handle blobs, which allows remote attackers to execute arbitrary code via unspecified vectors that trigger an out-of-bounds write.

In preloader, there is a possible out of bounds write due to a missing bounds check. This could lead to local escalation of privilege with System execution privileges needed. User interaction is not needed for exploitation. Patch ID: ALPS07733998 / ALPS07874388 (For MT6880 and MT6890 only); Issue ID: ALPS07733998 / ALPS07874388 (For MT6880 and MT6890 only).

In gps, there is a possible out of bounds write due to a missing bounds check. This could lead to local escalation of privilege with System execution privileges needed. User interaction is not needed for exploitation. Patch ID: ALPS07767811; Issue ID: ALPS07767811.

TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a heap buffer overflow to occur in `Conv2DBackpropFilter`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/1b0296c3b8dd9bd948f924aa8cd62f87dbb7c3da/tensorflow/core/kernels/conv_grad_filter_ops.cc#L495-L497) computes the size of the filter tensor but does not validate that it matches the number of elements in `filter_sizes`. Later, when reading/writing to this buffer, code uses the value computed here, instead of the number of elements in the tensor. 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.

In wlan, there is a possible out of bounds write due to a missing bounds check. This could lead to local escalation of privilege with System execution privileges needed. User interaction is not needed for exploitation. Patch ID: ALPS07588413; Issue ID: ALPS07588413.

TensorFlow is an end-to-end open source platform for machine learning. The validation in `tf.raw_ops.QuantizeAndDequantizeV2` allows invalid values for `axis` argument:. The validation(https://github.com/tensorflow/tensorflow/blob/eccb7ec454e6617738554a255d77f08e60ee0808/tensorflow/core/kernels/quantize_and_dequantize_op.cc#L74-L77) uses `||` to mix two different conditions. If `axis_ < -1` the condition in `OP_REQUIRES` will still be true, but this value of `axis_` results in heap underflow. This allows attackers to read/write to other data on the heap. 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.

In TBD of TBD, there is a possible stack buffer overflow due to a missing bounds check. This could lead to remote code execution with no additional execution privileges needed. User interaction is not needed for exploitation.

TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.FractionalAvgPoolGrad` is vulnerable to a heap buffer overflow. The implementation(https://github.com/tensorflow/tensorflow/blob/dcba796a28364d6d7f003f6fe733d82726dda713/tensorflow/core/kernels/fractional_avg_pool_op.cc#L216) fails to validate that the pooling sequence arguments have enough elements as required by the `out_backprop` tensor shape. 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.

In vcu, there is a possible memory corruption due to a logic error. This could lead to local escalation of privilege with System execution privileges needed. User interaction is not needed for exploitation. Patch ID: ALPS07559819; Issue ID: ALPS07559819.

TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.io.decode_raw` produces incorrect results and crashes the Python interpreter when combining `fixed_length` and wider datatypes. The implementation of the padded version(https://github.com/tensorflow/tensorflow/blob/1d8903e5b167ed0432077a3db6e462daf781d1fe/tensorflow/core/kernels/decode_padded_raw_op.cc) is buggy due to a confusion about pointer arithmetic rules. First, the code computes(https://github.com/tensorflow/tensorflow/blob/1d8903e5b167ed0432077a3db6e462daf781d1fe/tensorflow/core/kernels/decode_padded_raw_op.cc#L61) the width of each output element by dividing the `fixed_length` value to the size of the type argument. The `fixed_length` argument is also used to determine the size needed for the output tensor(https://github.com/tensorflow/tensorflow/blob/1d8903e5b167ed0432077a3db6e462daf781d1fe/tensorflow/core/kernels/decode_padded_raw_op.cc#L63-L79). This is followed by reencoding code(https://github.com/tensorflow/tensorflow/blob/1d8903e5b167ed0432077a3db6e462daf781d1fe/tensorflow/core/kernels/decode_padded_raw_op.cc#L85-L94). The erroneous code is the last line above: it is moving the `out_data` pointer by `fixed_length * sizeof(T)` bytes whereas it only copied at most `fixed_length` bytes from the input. This results in parts of the input not being decoded into the output. Furthermore, because the pointer advance is far wider than desired, this quickly leads to writing to outside the bounds of the backing data. This OOB write leads to interpreter crash in the reproducer mentioned here, but more severe attacks can be mounted too, given that this gadget allows writing to periodically placed locations in memory. 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.

TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.MaxPoolGradWithArgmax` can cause reads outside of bounds of heap allocated data if attacker supplies specially crafted inputs. The implementation(https://github.com/tensorflow/tensorflow/blob/31bd5026304677faa8a0b77602c6154171b9aec1/tensorflow/core/kernels/image/draw_bounding_box_op.cc#L116-L130) assumes that the last element of `boxes` input is 4, as required by [the op](https://www.tensorflow.org/api_docs/python/tf/raw_ops/DrawBoundingBoxesV2). Since this is not checked attackers passing values less than 4 can write outside of bounds of heap allocated objects and cause memory corruption. If the last dimension in `boxes` is less than 4, accesses similar to `tboxes(b, bb, 3)` will access data outside of bounds. Further during code execution there are also writes to these indices. 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.

Race condition in Google Chrome before 22.0.1229.92 allows remote attackers to execute arbitrary code via vectors related to audio devices.

In preloader, there is a possible out of bounds write due to a missing bounds check. This could lead to local escalation of privilege with System execution privileges needed. User interaction is not needed for exploitation. Patch ID: ALPS07734012 / ALPS07874363 (For MT6880, MT6890, MT6980 and MT6990 only); Issue ID: ALPS07734012 / ALPS07874363 (For MT6880, MT6890, MT6980 and MT6990 only).

In several functions of Exynos modem files, there is a possible out of bounds write due to a missing bounds check. This could lead to remote code execution with System execution privileges needed. User interaction is not needed for exploitation.

Adobe Flash Player before 18.0.0.343 and 19.x through 21.x before 21.0.0.213 on Windows and OS X and before 11.2.202.616 on Linux allows attackers to execute arbitrary code or cause a denial of service (memory corruption) via unspecified vectors, a different vulnerability than CVE-2016-1012, CVE-2016-1020, CVE-2016-1021, CVE-2016-1022, CVE-2016-1023, CVE-2016-1024, CVE-2016-1026, CVE-2016-1027, CVE-2016-1028, CVE-2016-1029, CVE-2016-1032, and CVE-2016-1033.

Race condition in the sandbox launcher implementation in Google Chrome before 11.0.696.57 on Linux allows remote attackers to cause a denial of service or possibly have unspecified other impact via unknown vectors.

Out of bounds read and write in ANGLE in Google Chrome prior to 114.0.5735.90 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page. (Chromium security severity: High)

In lcsm_SendRrAcquiAssist of lcsm_bcm_assist.c, there is a possible out of bounds write due to a missing bounds check. This could lead to remote code execution with no additional execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android kernelAndroid ID: A-246169606References: N/A

In eatt_l2cap_reconfig_completed of eatt_impl.h, there is a possible out of bounds write due to an integer overflow. This could lead to remote code execution with no additional execution privileges needed. User interaction is not needed for exploitation.

In swpm, there is a possible out of bounds write due to a race condition. This could lead to local information disclosure with System execution privileges needed. User interaction is not needed for exploitation. Patch ID: ALPS07780926; Issue ID: ALPS07780928.

In wlan, there is a possible out of bounds write due to a missing bounds check. This could lead to local escalation of privilege with System execution privileges needed. User interaction is not needed for exploitation. Patch ID: ALPS07796914; Issue ID: ALPS07796914.

An issue was discovered in drivers/media/platform/vivid in the Linux kernel through 5.3.8. It is exploitable for privilege escalation on some Linux distributions where local users have /dev/video0 access, but only if the driver happens to be loaded. There are multiple race conditions during streaming stopping in this driver (part of the V4L2 subsystem). These issues are caused by wrong mutex locking in vivid_stop_generating_vid_cap(), vivid_stop_generating_vid_out(), sdr_cap_stop_streaming(), and the corresponding kthreads. At least one of these race conditions leads to a use-after-free.

In keyinstall, there is a possible out of bounds write due to a missing bounds check. This could lead to local escalation of privilege with System execution privileges needed. User interaction is not needed for exploitation. Patch ID: ALPS07628168; Issue ID: ALPS07589144.

In libimpl-ril, there is a possible out of bounds write due to a missing bounds check. This could lead to local denial of service with System execution privileges needed.

In libimpl-ril, there is a possible out of bounds write due to a missing bounds check. This could lead to local denial of service with System execution privileges needed.

An issue was discovered in Rsyslog v8.1908.0. contrib/pmaixforwardedfrom/pmaixforwardedfrom.c has a heap overflow in the parser for AIX log messages. The parser tries to locate a log message delimiter (in this case, a space or a colon) but fails to account for strings that do not satisfy this constraint. If the string does not match, then the variable lenMsg will reach the value zero and will skip the sanity check that detects invalid log messages. The message will then be considered valid, and the parser will eat up the nonexistent colon delimiter. In doing so, it will decrement lenMsg, a signed integer, whose value was zero and now becomes minus one. The following step in the parser is to shift left the contents of the message. To do this, it will call memmove with the right pointers to the target and destination strings, but the lenMsg will now be interpreted as a huge value, causing a heap overflow.

In DRM/oemcrypto, there is a possible out of bounds write due to an incorrect calculation of buffer size.This could lead to remote escalation of privilege with System execution privileges needed

In TrustZone in all Android releases from CAF using the Linux kernel, a Time-of-Check Time-of-Use Race Condition vulnerability could potentially exist.

In iwnpi server, there is a possible out of bounds write due to a missing bounds check. This could lead to local denial of service with System execution privileges needed.

In ppmp_protect_mfcfw_buf of code/drm_fw.c, there is a possible corrupt memory due to a logic error in the code. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

Race condition in the L2TPv3 IP Encapsulation feature in the Linux kernel before 4.8.14 allows local users to gain privileges or cause a denial of service (use-after-free) by making multiple bind system calls without properly ascertaining whether a socket has the SOCK_ZAPPED status, related to net/l2tp/l2tp_ip.c and net/l2tp/l2tp_ip6.c.

Race condition in Google Chrome before 9.0.597.84 allows remote attackers to execute arbitrary code via vectors related to audio.

In ppmp_protect_mfcfw_buf of code/drm_fw.c, there is a possible memory corruption due to improper input validation. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.