| CVE |
Vendors |
Products |
Updated |
CVSS v3.1 |
| An issue was discovered in PassMark BurnInTest through 9.1, OSForensics through 7.1, and PerformanceTest through 10. The driver's IOCTL request handler attempts to copy the input buffer onto the stack without checking its size and can cause a buffer overflow. This could lead to arbitrary Ring-0 code execution and escalation of privileges. This affects DirectIo32.sys and DirectIo64.sys. |
| In nDPI through 3.2, the Oracle protocol dissector has a heap-based buffer over-read in ndpi_search_oracle in lib/protocols/oracle.c. |
| In nDPI through 3.2, the H.323 dissector is vulnerable to a heap-based buffer over-read in ndpi_search_h323 in lib/protocols/h323.c, as demonstrated by a payload packet length that is too short. |
| This vulnerability allows network-adjacent attackers to execute arbitrary code on affected installations of NETGEAR R6700 V1.0.4.84_10.0.58 routers. Authentication is not required to exploit this vulnerability. The specific flaw exists within the handling of string table file uploads. A crafted gui_region in a string table file can trigger an overflow of a fixed-length stack-based buffer. An attacker can leverage this vulnerability to execute code in the context of the web server. Was ZDI-CAN-9756. |
| This vulnerability allows network-adjacent attackers to bypass authentication on affected installations of NETGEAR R6700 V1.0.4.84_10.0.58 routers. Authentication is not required to exploit this vulnerability. The specific flaw exists within the httpd service, which listens on TCP port 80 by default. The issue results from the lack of proper validation of the length of user-supplied data prior to copying it to a fixed-length, stack-based buffer. An attacker can leverage this vulnerability to execute code in the context of root. Was ZDI-CAN-9703. |
| In MediaInfoLib in MediaArea MediaInfo 20.03, there is a stack-based buffer over-read in Streams_Fill_PerStream in Multiple/File_MpegPs.cpp (aka an off-by-one during MpegPs parsing). |
| 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. |
| An issue was discovered in ajv.validate() in Ajv (aka Another JSON Schema Validator) 6.12.2. A carefully crafted JSON schema could be provided that allows execution of other code by prototype pollution. (While untrusted schemas are recommended against, the worst case of an untrusted schema should be a denial of service, not execution of code.) |
| In SQLite before 3.32.3, select.c mishandles query-flattener optimization, leading to a multiSelectOrderBy heap overflow because of misuse of transitive properties for constant propagation. |
| 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. |
| An issue was discovered in OpenEXR before v2.5.2. Invalid chunkCount attributes could cause a heap buffer overflow in getChunkOffsetTableSize() in IlmImf/ImfMisc.cpp. |
| SuiteCRM through 7.11.13 allows CSV Injection via registration fields in the Accounts, Contacts, Opportunities, and Leads modules. These fields are mishandled during a Download Import File Template operation. |
| 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 2.4.0, an attacker can pass an invalid `axis` value to `tf.quantization.quantize_and_dequantize`. This results in accessing a dimension outside the rank of the input tensor in the C++ kernel implementation. However, dim_size only does a DCHECK to validate the argument and then uses it to access the corresponding element of an array. Since in normal builds, `DCHECK`-like macros are no-ops, this results in segfault and access out of bounds of the array. 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 Anuko Time Tracker before verion 1.19.23.5325, due to not properly filtered user input a CSV export of a report could contain cells that are treated as formulas by spreadsheet software (for example, when a cell value starts with an equal sign). This is fixed in version 1.19.23.5325. |
| Crossbeam is a set of tools for concurrent programming. In crossbeam-channel before version 0.4.4, the bounded channel incorrectly assumes that `Vec::from_iter` has allocated capacity that same as the number of iterator elements. `Vec::from_iter` does not actually guarantee that and may allocate extra memory. The destructor of the `bounded` channel reconstructs `Vec` from the raw pointer based on the incorrect assumes described above. This is unsound and causing deallocation with the incorrect capacity when `Vec::from_iter` has allocated different sizes with the number of iterator elements. This has been fixed in crossbeam-channel 0.4.4. |
| ORY Fosite is a security first OAuth2 & OpenID Connect framework for Go. In Fosite before version 0.34.1, the OAuth 2.0 Client's registered redirect URLs and the redirect URL provided at the OAuth2 Authorization Endpoint where compared using strings.ToLower while they should have been compared with a simple string match. This allows an attacker to register a client with allowed redirect URL https://example.com/callback. Then perform an OAuth2 flow and requesting redirect URL https://example.com/CALLBACK. Instead of an error (invalid redirect URL), the browser is redirected to https://example.com/CALLBACK with a potentially successful OAuth2 response, depending on the state of the overall OAuth2 flow (the user might still deny the request for example). This vulnerability has been patched in ORY Fosite v0.34.1. |
| 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. |
| In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 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 only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code. |
| In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, when determining the common dimension size of two tensors, TFLite uses a `DCHECK` which is no-op outside of debug compilation modes. Since the function always returns the dimension of the first tensor, malicious attackers can craft cases where this is larger than that of the second tensor. In turn, this would result in reads/writes outside of bounds since the interpreter will wrongly assume that there is enough data in both tensors. The issue is patched in commit 8ee24e7949a203d234489f9da2c5bf45a7d5157d, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. |
