CWE-476

Appweb before 7.2.2 and 8.x before 8.1.0, when built with CGI support, mishandles an HTTP request with a Range header that lacks an exact range. This may result in a NULL pointer dereference and cause a denial of service.

The Linux kernel before version 5.8 is vulnerable to a NULL pointer dereference in drivers/tty/serial/8250/8250_core.c:serial8250_isa_init_ports() that allows local users to cause a denial of service by using the p->serial_in pointer which uninitialized.

In QEMU 4.2.0, a MemoryRegionOps object may lack read/write callback methods, leading to a NULL pointer dereference.

An issue was discovered in OpenEXR before 2.5.2. An invalid tiled input file could cause invalid memory access in TiledInputFile::TiledInputFile() in IlmImf/ImfTiledInputFile.cpp, as demonstrated by a NULL pointer dereference.

In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the `tf.raw_ops.Switch` operation takes as input a tensor and a boolean and outputs two tensors. Depending on the boolean value, one of the tensors is exactly the input tensor whereas the other one should be an empty tensor. However, the eager runtime traverses all tensors in the output. Since only one of the tensors is defined, the other one is `nullptr`, hence we are binding a reference to `nullptr`. This is undefined behavior and reported as an error if compiling with `-fsanitize=null`. In this case, this results in a segmentation fault The issue is patched in commit da8558533d925694483d2c136a9220d6d49d843c, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.

In eager mode, TensorFlow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1 does not set the session state. Hence, calling `tf.raw_ops.GetSessionHandle` or `tf.raw_ops.GetSessionHandleV2` results in a null pointer dereference In linked snippet, in eager mode, `ctx->session_state()` returns `nullptr`. Since code immediately dereferences this, we get a segmentation fault. The issue is patched in commit 9a133d73ae4b4664d22bd1aa6d654fec13c52ee1, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.