CVE-2020-25815
An issue was discovered in MediaWiki 1.32.x through 1.34.x before 1.34.4. LogEventList::getFiltersDesc is insecurely using message text to build options names for an HTML multi-select field. The relevant code should use escaped() instead of text().
Source: CVE-2020-25815
CVE-2020-25813
In MediaWiki before 1.31.10 and 1.32.x through 1.34.x before 1.34.4, Special:UserRights exposes the existence of hidden users.
Source: CVE-2020-25813
CVE-2020-25827
An issue was discovered in the OATHAuth extension in MediaWiki before 1.31.10 and 1.32.x through 1.34.x before 1.34.4. For Wikis using OATHAuth on a farm/cluster (such as via CentralAuth), rate limiting of OATH tokens is only done on a single site level. Thus, multiple requests can be made across many wikis/sites concurrently.
Source: CVE-2020-25827
CVE-2020-25812
An issue was discovered in MediaWiki 1.34.x before 1.34.4. On Special:Contributions, the NS filter uses unescaped messages as keys in the option key for an HTMLForm specifier. This is vulnerable to a mild XSS if one of those messages is changed to include raw HTML.
Source: CVE-2020-25812
CVE-2020-26116
http.client in Python 3.x before 3.5.10, 3.6.x before 3.6.12, 3.7.x before 3.7.9, and 3.8.x before 3.8.5 allows CRLF injection if the attacker controls the HTTP request method, as demonstrated by inserting CR and LF control characters in the first argument of HTTPConnection.request.
Source: CVE-2020-26116
CVE-2020-26117
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.
Source: CVE-2020-26117
CVE-2020-15209
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.
Source: CVE-2020-15209
CVE-2020-15214
In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger a write out bounds / segmentation fault if the segment ids are not sorted. Code assumes that the segment ids are in increasing order, using the last element of the tensor holding them to determine the dimensionality of output tensor. This results in allocating insufficient memory for the output tensor and in a write outside the bounds of the output array. This usually results in a segmentation fault, but depending on runtime conditions it can provide for a write gadget to be used in future memory corruption-based exploits. The issue is patched in commit 204945b19e44b57906c9344c0d00120eeeae178a and is released in TensorFlow versions 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that the segment ids are sorted, although this only handles the case when the segment ids are stored statically in the model. A similar validation could be done if the segment ids are generated at runtime between inference steps. If the segment ids are generated as outputs of a tensor during inference steps, then there are no possible workaround and users are advised to upgrade to patched code.
Source: CVE-2020-15214
CVE-2020-15213
In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger a denial of service by causing an out of memory allocation in the implementation of segment sum. Since code uses the last element of the tensor holding them to determine the dimensionality of output tensor, attackers can use a very large value to trigger a large allocation. The issue is patched in commit 204945b19e44b57906c9344c0d00120eeeae178a and is released in TensorFlow versions 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to limit the maximum value in the segment ids tensor. This only handles the case when the segment ids are stored statically in the model, but a similar validation could be done if the segment ids are generated at runtime, between inference steps. However, if the segment ids are generated as outputs of a tensor during inference steps, then there are no possible workaround and users are advised to upgrade to patched code.
Source: CVE-2020-15213
CVE-2020-15212
In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger writes outside of bounds of heap allocated buffers by inserting negative elements in the segment ids tensor. Users having access to `segment_ids_data` can alter `output_index` and then write to outside of `output_data` buffer. This might result in a segmentation fault but it can also be used to further corrupt the memory and can be chained with other vulnerabilities to create more advanced exploits. The issue is patched in commit 204945b19e44b57906c9344c0d00120eeeae178a and is released in TensorFlow versions 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that the segment ids are all positive, although this only handles the case when the segment ids are stored statically in the model. A similar validation could be done if the segment ids are generated at runtime between inference steps. If the segment ids are generated as outputs of a tensor during inference steps, then there are no possible workaround and users are advised to upgrade to patched code.
Source: CVE-2020-15212