A flaw was found in dpdk in versions before 18.11.10 and before 19.11.5. A complete lack of validation of attacker-controlled parameters can lead to a buffer over read. The results of the over read are then written back to the guest virtual machine memory. This vulnerability can be used by an attacker in a virtual machine to read significant amounts of host memory. The highest threat from this vulnerability is to data confidentiality and system availability.

An integer underflow in dpdk versions before 18.11.10 and before 19.11.5 in the `move_desc` function can lead to large amounts of CPU cycles being eaten up in a long running loop. An attacker could cause `move_desc` to get stuck in a 4,294,967,295-count iteration loop. Depending on how `vhost_crypto` is being used this could prevent other VMs or network tasks from being serviced by the busy DPDK lcore for an extended period.

A flaw was found in dpdk in versions before 18.11.10 and before 19.11.5. A lack of bounds checking when copying iv_data from the VM guest memory into host memory can lead to a large buffer overflow. The highest threat from this vulnerability is to data confidentiality and integrity as well as system availability.

url.cpp in libproxy through 0.4.15 is prone to a buffer overflow when PAC is enabled, as demonstrated by a large PAC file that is delivered without a Content-length header.

In PHP versions 7.2.x below 7.2.34, 7.3.x below 7.3.23 and 7.4.x below 7.4.11, when PHP is processing incoming HTTP cookie values, the cookie names are url-decoded. This may lead to cookies with prefixes like __Host confused with cookies that decode to such prefix, thus leading to an attacker being able to forge cookie which is supposed to be secure. See also CVE-2020-8184 for more information.

In PHP versions 7.2.x below 7.2.34, 7.3.x below 7.3.23 and 7.4.x below 7.4.11, when AES-CCM mode is used with openssl_encrypt() function with 12 bytes IV, only first 7 bytes of the IV is actually used. This can lead to both decreased security and incorrect encryption data.

In rfb/CSecurityTLS.cxx and rfb/CSecurityTLS.java in TigerVNC before 1.11.0, viewers mishandle TLS certificate exceptions. They store the certificates as authorities, meaning that the owner of a certificate could impersonate any server after a client had added an exception.

In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the `data_splits` argument of `tf.raw_ops.StringNGrams` lacks validation. This allows a user to pass values that can cause heap overflow errors and even leak contents of memory In the linked code snippet, all the binary strings after `ee ff` are contents from the memory stack. Since these can contain return addresses, this data leak can be used to defeat ASLR. The issue is patched in commit 0462de5b544ed4731aa2fb23946ac22c01856b80, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.

In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, a crafted TFLite model can force a node to have as input a tensor backed by a `nullptr` buffer. This can be achieved by changing a buffer index in the flatbuffer serialization to convert a read-only tensor to a read-write one. The runtime assumes that these buffers are written to before a possible read, hence they are initialized with `nullptr`. However, by changing the buffer index for a tensor and implicitly converting that tensor to be a read-write one, as there is nothing in the model that writes to it, we get a null pointer dereference. The issue is patched in commit 0b5662bc, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.

In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the `SparseFillEmptyRowsGrad` implementation has incomplete validation of the shapes of its arguments. Although `reverse_index_map_t` and `grad_values_t` are accessed in a similar pattern, only `reverse_index_map_t` is validated to be of proper shape. Hence, malicious users can pass a bad `grad_values_t` to trigger an assertion failure in `vec`, causing denial of service in serving installations. The issue is patched in commit 390611e0d45c5793c7066110af37c8514e6a6c54, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1."