Integer Overflow or Wraparound

NoMachine Enterprise Client is affected by Integer Overflow. IOCTL Handler 0x22001B in the NoMachine Enterprise Client above 4.0.346 and below 7.7.4 allow local attackers to execute arbitrary code in kernel mode or cause a denial of service (memory corruption and OS crash) via specially crafted I/O Request Packet.Show less

NoMachine Cloud Server is affected by Integer Overflow. IOCTL Handler 0x22001B in the NoMachine Cloud Server above 4.0.346 and below 7.7.4 allow local attackers to execute arbitrary code in kernel mode or cause a denial of service (memory corruption and OS crash) via specially crafted I/O Request Packet.Show less

NoMachine Enterprise Desktop is affected by Integer Overflow. IOCTL Handler 0x22001B in the NoMachine Enterprise Desktop above 4.0.346 and below 7.7.4 allow local attackers to execute arbitrary code in kernel mode or cause a denial of service (memory corruption and OS crash) via specially crafted I/O Request Packet.Show less

NoMachine Server is affected by Integer Overflow. IOCTL Handler 0x22001B in the NoMachine Server above 4.0.346 and below 7.7.4 allow local attackers to execute arbitrary code in kernel mode or cause a denial of service (memory corruption and OS crash) via specially crafted I/O Request Packet.Show less

runc is a CLI tool for spawning and running containers on Linux according to the OCI specification. In runc, netlink is used internally as a serialization system for specifying the relevant container configuration to the `C` portion of the code (responsible for the based namespace setup of containers). In all versions of runc prior to 1.0.3, the encoder did not handle the possibility of an integer overflow in the 16-bit length field for the byte array attribute type, meaning that a large enough malicious byte array attribute could result in the length overflowing and the attribute contents being parsed as netlink messages for container configuration. This vulnerability requires the attacker to have some control over the configuration of the container and would allow the attacker to bypass the namespace restrictions of the container by simply adding their own netlink payload which disables all namespaces. The main users impacted are those who allow untrusted images with untrusted configurations to run on their machines (such as with shared cloud infrastructure). runc version 1.0.3 contains a fix for this bug. As a workaround, one may try disallowing untrusted namespace paths from your container. It should be noted that untrusted namespace paths would allow the attacker to disable namespace protections entirely even in the absence of this bug.Show less

The vulnerability function is enabled when the streamer service related to the AfreecaTV communicated through web socket using 21201 port. A stack-based buffer overflow leading to remote code execution was discovered in strcpy() operate by "FanTicket" field. It is because of stored data without validation of length.Show less

In asf extractor, there is a possible out of bounds read due to an integer overflow. This could lead to local information disclosure with no additional execution privileges needed. User interaction is not needed for exploitation. Patch ID: ALPS05489178; Issue ID: ALPS05585817.Show less

In asf extractor, there is a possible out of bounds read due to an integer overflow. This could lead to local information disclosure with no additional execution privileges needed. User interaction is not needed for exploitation. Patch ID: ALPS05489178; Issue ID: ALPS05561383.Show less

TensorFlow is an open source platform for machine learning. In affected versions an attacker can trigger undefined behavior, integer overflows, segfaults and `CHECK`-fail crashes if they can change saved checkpoints from outside of TensorFlow. This is because the checkpoints loading infrastructure is missing validation for invalid file formats. The fixes will be included in TensorFlow 2.7.0. We will also cherrypick these commits on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.Show less

TensorFlow is an open source platform for machine learning. In affected versions if `tf.image.resize` is called with a large input argument then the TensorFlow process will crash due to a `CHECK`-failure caused by an overflow. The number of elements in the output tensor is too much for the `int64_t` type and the overflow is detected via a `CHECK` statement. This aborts the process. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.Show less

TensorFlow is an open source platform for machine learning. In affected versions if `tf.tile` is called with a large input argument then the TensorFlow process will crash due to a `CHECK`-failure caused by an overflow. The number of elements in the output tensor is too much for the `int64_t` type and the overflow is detected via a `CHECK` statement. This aborts the process. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.Show less

TensorFlow is an open source platform for machine learning. In affected versions TensorFlow allows tensor to have a large number of dimensions and each dimension can be as large as desired. However, the total number of elements in a tensor must fit within an `int64_t`. If an overflow occurs, `MultiplyWithoutOverflow` would return a negative result. In the majority of TensorFlow codebase this then results in a `CHECK`-failure. Newer constructs exist which return a `Status` instead of crashing the binary. This is similar to CVE-2021-29584. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.Show less

TensorFlow is an open source platform for machine learning. In affected versions the implementation of `tf.math.segment_*` operations results in a `CHECK`-fail related abort (and denial of service) if a segment id in `segment_ids` is large. This is similar to CVE-2021-29584 (and similar other reported vulnerabilities in TensorFlow, localized to specific APIs): the implementation (both on CPU and GPU) computes the output shape using `AddDim`. However, if the number of elements in the tensor overflows an `int64_t` value, `AddDim` results in a `CHECK` failure which provokes a `std::abort`. Instead, code should use `AddDimWithStatus`. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.Show less