| CVE |
Vendors |
Products |
Updated |
CVSS v3.1 |
| This vulnerability allows remote attackers to execute arbitrary code on affected installations of CentOS Web Panel cwp-e17.0.9.8.923. Authentication is not required to exploit this vulnerability. The specific flaw exists within ajax_mod_security.php. The issue results from the lack of proper validation of a user-supplied string before using it to execute a system call. An attacker can leverage this vulnerability to execute code in the context of root. Was ZDI-CAN-9742. |
| This vulnerability allows remote attackers to execute arbitrary code on affected installations of CentOS Web Panel cwp-e17.0.9.8.923. Authentication is not required to exploit this vulnerability. The specific flaw exists within ajax_mod_security.php. When parsing the domain parameter, the process does not properly validate a user-supplied string before using it to execute a system call. An attacker can leverage this vulnerability to execute code in the context of root. Was ZDI-CAN-9735. |
| This vulnerability allows remote attackers to execute arbitrary code on affected installations of CentOS Web Panel cwp-e17.0.9.8.923. Authentication is not required to exploit this vulnerability. The specific flaw exists within ajax_mod_security.php. When parsing the dominio parameter, the process does not properly validate a user-supplied string before using it to execute a system call. An attacker can leverage this vulnerability to execute code in the context of root. Was ZDI-CAN-9732. |
| This vulnerability allows remote attackers to execute arbitrary code on affected installations of CentOS Web Panel cwp-e17.0.9.8.923. Authentication is not required to exploit this vulnerability. The specific flaw exists within ajax_mod_security.php. When parsing the archivo parameter, the process does not properly validate a user-supplied string before using it to execute a system call. An attacker can leverage this vulnerability to execute code in the context of root. Was ZDI-CAN-9731. |
| This vulnerability allows remote attackers to execute arbitrary code on affected installations of CentOS Web Panel cwp-e17.0.9.8.923. Authentication is not required to exploit this vulnerability. The specific flaw exists within ajax_mod_security.php. When parsing the check_ip parameter, the process does not properly validate a user-supplied string before using it to execute a system call. An attacker can leverage this vulnerability to execute code in the context of root. Was ZDI-CAN-9707. |
| This vulnerability allows remote attackers to execute arbitrary code on affected installations of CentOS Web Panel cwp-el7-0.9.8.891. Authentication is not required to exploit this vulnerability. The specific flaw exists within loader_ajax.php. When parsing the line parameter, the process does not properly validate a user-supplied string before using it to execute a system call. An attacker can leverage this vulnerability to execute code in the context of root. Was ZDI-CAN-9259. |
| LibRaw before 0.20-Beta3 has an out-of-bounds write in parse_exif() in metadata\exif_gps.cpp via an unrecognized AtomName and a zero value of tiff_nifds. |
| wifiscanner.js in thingsSDK WiFi Scanner 1.0.1 allows Code Injection because it can be used with options to overwrite the default executable/binary path and its arguments. An attacker can abuse this functionality to execute arbitrary 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. |
| Network Analysis functionality in Askey AP5100W_Dual_SIG_1.01.097 and all prior versions allows remote attackers to execute arbitrary commands via a shell metacharacter in the ping, traceroute, or route options. |
| 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. |
| In the git-tag-annotation-action (open source GitHub Action) before version 1.0.1, an attacker can execute arbitrary (*) shell commands if they can control the value of [the `tag` input] or manage to alter the value of [the `GITHUB_REF` environment variable]. The problem has been patched in version 1.0.1. If you don't use the `tag` input you are most likely safe. The `GITHUB_REF` environment variable is protected by the GitHub Actions environment so attacks from there should be impossible. If you must use the `tag` input and cannot upgrade to `> 1.0.0` make sure that the value is not controlled by another Action. |
| In lookatme (python/pypi package) versions prior to 2.3.0, the package automatically loaded the built-in "terminal" and "file_loader" extensions. Users that use lookatme to render untrusted markdown may have malicious shell commands automatically run on their system. This is fixed in version 2.3.0. As a workaround, the `lookatme/contrib/terminal.py` and `lookatme/contrib/file_loader.py` files may be manually deleted. Additionally, it is always recommended to be aware of what is being rendered with lookatme. |
| 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. |
| 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. |
| 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, if a TFLite saved model uses the same tensor as both input and output of an operator, then, depending on the operator, we can observe a segmentation fault or just memory corruption. We have patched the issue in d58c96946b and will release patch releases for all versions between 1.15 and 2.3. We recommend users to upgrade to TensorFlow 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, 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. |
| In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, to mimic Python's indexing with negative values, TFLite uses `ResolveAxis` to convert negative values to positive indices. However, the only check that the converted index is now valid is only present in debug builds. If the `DCHECK` does not trigger, then code execution moves ahead with a negative index. This, in turn, results in accessing data out of bounds which results in segfaults and/or data corruption. The issue is patched in commit 2d88f470dea2671b430884260f3626b1fe99830a, 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 `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. |
