CWE-119

An issue was discovered in Xen through 4.13.x, allowing x86 HVM guest OS users to cause a hypervisor crash. An inverted conditional in x86 HVM guests’ dirty video RAM tracking code allows such guests to make Xen de-reference a pointer guaranteed to point at unmapped space. A malicious or buggy HVM guest may cause the hypervisor to crash, resulting in Denial of Service (DoS) affecting the entire host. Xen versions from 4.8 onwards are affected. Xen versions 4.7 and earlier are not affected. Only x86 systems are affected. Arm systems are not affected. Only x86 HVM guests using shadow paging can leverage the vulnerability. In addition, there needs to be an entity actively monitoring a guest’s video frame buffer (typically for display purposes) in order for such a guest to be able to leverage the vulnerability. x86 PV guests, as well as x86 HVM guests using hardware assisted paging (HAP), cannot leverage the vulnerability.

An issue was discovered in Xen through 4.13.x, allowing Arm guest OS users to cause a hypervisor crash because of a missing alignment check in VCPUOP_register_vcpu_info. The hypercall VCPUOP_register_vcpu_info is used by a guest to register a shared region with the hypervisor. The region will be mapped into Xen address space so it can be directly accessed. On Arm, the region is accessed with instructions that require a specific alignment. Unfortunately, there is no check that the address provided by the guest will be correctly aligned. As a result, a malicious guest could cause a hypervisor crash by passing a misaligned address. A malicious guest administrator may cause a hypervisor crash, resulting in a Denial of Service (DoS). All Xen versions are vulnerable. Only Arm systems are vulnerable. x86 systems are not affected.

RIOT 2020.04 has a buffer overflow in the base64 decoder. The decoding function base64_decode() uses an output buffer estimation function to compute the required buffer capacity and validate against the provided buffer size. The base64_estimate_decode_size() function calculates the expected decoded size with an arithmetic round-off error and does not take into account possible padding bytes. Due to this underestimation, it may be possible to craft base64 input that causes a buffer overflow.

Multiple buffer overflow vulnerabilities in REST API in Brocade Fabric OS versions v8.2.1 through v8.2.1d, and 8.2.2 versions before v8.2.2c could allow remote unauthenticated attackers to perform various attacks.

In Tensorflow before version 2.4.0, when the `boxes` argument of `tf.image.crop_and_resize` has a very large value, the CPU kernel implementation receives it as a C++ `nan` floating point value. Attempting to operate on this is undefined behavior which later produces a segmentation fault. The issue is patched in eccb7ec454e6617738554a255d77f08e60ee0808 and TensorFlow 2.4.0 will be released containing the patch. TensorFlow nightly packages after this commit will also have the issue resolved.

In Tensorflow before version 2.3.1, the `SparseCountSparseOutput` implementation does not validate that the input arguments form a valid sparse tensor. In particular, there is no validation that the `indices` tensor has the same shape as the `values` one. The values in these tensors are always accessed in parallel. Thus, a shape mismatch can result in accesses outside the bounds of heap allocated buffers. The issue is patched in commit 3cbb917b4714766030b28eba9fb41bb97ce9ee02 and is released in TensorFlow version 2.3.1.