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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.
TensorFlow is an end-to-end open source platform for machine learning. Incomplete validation in `tf.raw_ops.CTCLoss` allows an attacker to trigger an OOB read from heap. The fix will be included in TensorFlow 2.5.0. We will also cherrypick these commits 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.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.
Exim 4 before 4.94.2 has Improper Initialization that can lead to recursion-based stack consumption or other consequences. This occurs because use of certain getc functions is mishandled when a client uses BDAT instead of DATA.
Due to an improper Initialization vulnerability on Juniper Networks Junos OS QFX5100-96S devices with QFX 5e Series image installed, ddos-protection configuration changes will not take effect beyond the default DDoS (Distributed Denial of Service) settings when configured from the CLI. The DDoS protection (jddosd) daemon allows the device to continue to function while protecting the packet forwarding engine (PFE) during the DDoS attack. When this issue occurs, the default DDoS settings within the PFE apply, as CPU bound packets will be throttled and dropped in the PFE when the limits are exceeded. To check if the device has this issue, the administrator can execute the following command to monitor the status of DDoS protection: user@device> show ddos-protection protocols error: the ddos-protection subsystem is not running This issue affects only QFX5100-96S devices. No other products or platforms are affected by this issue. This issue affects: Juniper Networks Junos OS on QFX5100-96S: 17.3 versions prior to 17.3R3-S10; 17.4 versions prior to 17.4R3-S4; 18.1 versions prior to 18.1R3-S10; 18.2 versions prior to 18.2R3-S3; 18.3 versions prior to 18.3R3-S2; 18.4 versions prior to 18.4R2-S4, 18.4R3-S1; 19.1 versions prior to 19.1R3, 19.1R3-S4; 19.2 versions prior to 19.2R2; 19.3 versions prior to 19.3R3; 19.4 versions prior to 19.4R2;
Junos os evolved
On Juniper Networks Junos OS Evolved devices, receipt of a specific IPv6 packet may cause an established IPv6 BGP session to terminate, creating a Denial of Service (DoS) condition. Continued receipt and processing of this packet will create a sustained Denial of Service (DoS) condition. This issue does not affect IPv4 BGP sessions. This issue affects IBGP or EBGP peer sessions with IPv6. This issue affects: Juniper Networks Junos OS Evolved: 19.4 versions prior to 19.4R2-S3-EVO; 20.1 versions prior to 20.1R2-S3-EVO; 20.2 versions prior to 20.2R2-S1-EVO; 20.3 versions prior to 20.3R2-EVO. This issue does not affect Juniper Networks Junos OS releases.
In avrc_proc_vendor_command of avrc_api.cc, there is a possible leak of heap data due to uninitialized data. This could lead to remote information disclosure with no additional execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android-11 Android-8.1 Android-9 Android-10Android ID: A-174150451
A memory initialization issue was addressed with improved memory handling. This issue is fixed in iOS 14.4 and iPadOS 14.4. An attacker in a privileged position may be able to perform a denial of service attack.
A flaw possibility of race condition and incorrect initialization of the process id was found in the Linux kernel child/parent process identification handling while filtering signal handlers. A local attacker is able to abuse this flaw to bypass checks to send any signal to a privileged process.
A flaw was found in http-proxy-agent, prior to version 2.1.0. It was discovered http-proxy-agent passes an auth option to the Buffer constructor without proper sanitization. This could result in a Denial of Service through the usage of all available CPU resources and data exposure through an uninitialized memory leak in setups where an attacker could submit typed input to the auth parameter.
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