Newest CVEs

Page 1 of 3010 150473 total

IDDescriptionSeverity
CVE-2020-25775The Trend Micro Security 2020 (v16) consumer family of products is vulnerable to a security race condition arbitrary file deletion vulnerability that could allow an unprivileged user to manipulate the product’s secure erase feature to delete files with a higher set of privileges.No Score
CVE-2020-25774A vulnerability in the Trend Micro Apex One ServerMigrationTool component could allow an attacker to trigger an out-of-bounds red information disclosure which would disclose sensitive information to an unprivileged account. User interaction is required to exploit this vulnerability in that the target must visit a malicious page or open a malicious file.No Score
CVE-2020-25773A vulnerability in the Trend Micro Apex One ServerMigrationTool component could allow an attacker to execute arbitrary code on affected products. User interaction is required to exploit this vulnerability in that the target must import a corrupted configuration file.No Score
CVE-2020-25772An out-of-bounds read information disclosure vulnerabilities in Trend Micro Apex One may allow a local attacker to disclose sensitive information to an unprivileged account on vulnerable installations of the product. An attacker must first obtain the ability to execute low-privileged code on the target in order to exploit these vulnerabilities. The subs affected in this vulnerability makes it unique compared to similar CVEs such as CVE-2020-24564 and CVE-2020-25771. No Score
CVE-2020-25771An out-of-bounds read information disclosure vulnerabilities in Trend Micro Apex One may allow a local attacker to disclose sensitive information to an unprivileged account on vulnerable installations of the product. An attacker must first obtain the ability to execute low-privileged code on the target in order to exploit these vulnerabilities. The subs affected in this vulnerability makes it unique compared to similar CVEs such as CVE-2020-24564 and CVE-2020-25770. No Score
CVE-2020-25770An out-of-bounds read information disclosure vulnerabilities in Trend Micro Apex One may allow a local attacker to disclose sensitive information to an unprivileged account on vulnerable installations of the product. An attacker must first obtain the ability to execute low-privileged code on the target in order to exploit these vulnerabilities. The subs affected in this vulnerability makes it unique compared to similar CVEs such as CVE-2020-24564 and CVE-2020-25771. No Score
CVE-2020-24565An out-of-bounds read information disclosure vulnerabilities in Trend Micro Apex One may allow a local attacker to disclose sensitive information to an unprivileged account on vulnerable installations of the product. An attacker must first obtain the ability to execute low-privileged code on the target in order to exploit these vulnerabilities. The subs affected in this vulnerability makes it unique compared to similar CVEs such as CVE-2020-24564 and CVE-2020-25770. No Score
CVE-2020-24564An out-of-bounds read information disclosure vulnerabilities in Trend Micro Apex One may allow a local attacker to disclose sensitive information to an unprivileged account on vulnerable installations of the product. An attacker must first obtain the ability to execute low-privileged code on the target in order to exploit these vulnerabilities. The subs affected in this vulnerability makes it unique compared to similar CVEs such as CVE-2020-24565 and CVE-2020-25770. No Score
CVE-2020-24563A vulnerability in Trend Micro Apex One may allow a local attacker to manipulate the process of the security agent unload option (if configured), which then could be manipulated to gain a privilege escalation and code execution. An attacker must first obtain the ability to execute low-privileged code on the target in order to exploit this vulnerability.No Score
CVE-2020-24562A vulnerability in Trend Micro OfficeScan XG SP1 on Microsoft Windows may allow an attacker to create a hard link to any file on the system, which then could be manipulated to gain a privilege escalation and code execution. An attacker must first obtain the ability to execute low-privileged code on the target system in order to exploit this vulnerability. This CVE is similar, but not identical to CVE-2020-24556.No Score
CVE-2020-26121An issue was discovered in the FileImporter extension for MediaWiki before 1.34.4. An attacker can import a file even when the target page is protected against "page creation" and the attacker should not be able to create it. This occurs because of a mishandled distinction between an upload restriction and a create restriction. An attacker cannot leverage this to overwrite anything, but can leverage this to force a wiki to have a page with a disallowed title.No Score
CVE-2020-26120XSS exists in the MobileFrontend extension for MediaWiki before 1.34.4 because section.line is mishandled during regex section line replacement from PageGateway. Using crafted HTML, an attacker can elicit an XSS attack via jQuery's parseHTML method, which can cause image callbacks to fire even without the element being appended to the DOM.No Score
CVE-2020-25869An information leak was discovered in MediaWiki before 1.31.10 and 1.32.x through 1.34.x before 1.34.4. Handling of actor ID does not necessarily use the correct database or correct wiki.No Score
CVE-2020-25828An issue was discovered in MediaWiki before 1.31.10 and 1.32.x through 1.34.x before 1.34.4. The non-jqueryMsg version of mw.message().parse() doesn't escape HTML. This affects both message contents (which are generally safe) and the parameters (which can be based on user input). (When jqueryMsg is loaded, it correctly accepts only whitelisted tags in message contents, and escapes all parameters. Situations with an unloaded jqueryMsg are rare in practice, but can for example occur for Special:SpecialPages on a wiki with no extensions installed.)No Score
CVE-2020-25827An 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.No Score
CVE-2020-25815An 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().No Score
CVE-2020-25814In MediaWiki before 1.31.10 and 1.32.x through 1.34.x before 1.34.4, XSS related to jQuery can occur. The attacker creates a message with [javascript:payload xss] and turns it into a jQuery object with mw.message().parse(). The expected result is that the jQuery object does not contain an <a> tag (or it does not have a href attribute, or it's empty, etc.). The actual result is that the object contains an <a href ="javascript... that executes when clicked.No Score
CVE-2020-25813In 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.No Score
CVE-2020-25812An 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.No Score
CVE-2020-26117In 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.No Score
CVE-2020-26116http.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.No Score
CVE-2020-15214In 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.No Score
CVE-2020-15213In 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.No Score
CVE-2020-15212In 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.No Score
CVE-2020-15211In 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.No Score
CVE-2020-15210In 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.No Score
CVE-2020-15209In 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.No Score
CVE-2020-15208In 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.No Score
CVE-2020-15207In 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.No Score
CVE-2020-15206In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, changing the TensorFlow's `SavedModel` protocol buffer and altering the name of required keys results in segfaults and data corruption while loading the model. This can cause a denial of service in products using `tensorflow-serving` or other inference-as-a-service installments. Fixed were added in commits f760f88b4267d981e13f4b302c437ae800445968 and fcfef195637c6e365577829c4d67681695956e7d (both going into TensorFlow 2.2.0 and 2.3.0 but not yet backported to earlier versions). However, this was not enough, as #41097 reports a different failure mode. The issue is patched in commit adf095206f25471e864a8e63a0f1caef53a0e3a6, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.No Score
CVE-2020-15205In 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.No Score
CVE-2020-15204In 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.No Score
CVE-2020-15203In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, by controlling the `fill` argument of tf.strings.as_string, a malicious attacker is able to trigger a format string vulnerability due to the way the internal format use in a `printf` call is constructed. This may result in segmentation fault. The issue is patched in commit 33be22c65d86256e6826666662e40dbdfe70ee83, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.No Score
CVE-2020-15202In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the `Shard` API in TensorFlow expects the last argument to be a function taking two `int64` (i.e., `long long`) arguments. However, there are several places in TensorFlow where a lambda taking `int` or `int32` arguments is being used. In these cases, if the amount of work to be parallelized is large enough, integer truncation occurs. Depending on how the two arguments of the lambda are used, this can result in segfaults, read/write outside of heap allocated arrays, stack overflows, or data corruption. The issue is patched in commits 27b417360cbd671ef55915e4bb6bb06af8b8a832 and ca8c013b5e97b1373b3bb1c97ea655e69f31a575, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.No Score
CVE-2020-15201In Tensorflow before version 2.3.1, the `RaggedCountSparseOutput` implementation does not validate that the input arguments form a valid ragged tensor. In particular, there is no validation that the values in the `splits` tensor generate a valid partitioning of the `values` tensor. Hence, the code is prone to heap buffer overflow. If `split_values` does not end with a value at least `num_values` then the `while` loop condition will trigger a read outside of the bounds of `split_values` once `batch_idx` grows too large. The issue is patched in commit 3cbb917b4714766030b28eba9fb41bb97ce9ee02 and is released in TensorFlow version 2.3.1.No Score
CVE-2020-15200In Tensorflow before version 2.3.1, the `RaggedCountSparseOutput` implementation does not validate that the input arguments form a valid ragged tensor. In particular, there is no validation that the values in the `splits` tensor generate a valid partitioning of the `values` tensor. Thus, the code sets up conditions to cause a heap buffer overflow. A `BatchedMap` is equivalent to a vector where each element is a hashmap. However, if the first element of `splits_values` is not 0, `batch_idx` will never be 1, hence there will be no hashmap at index 0 in `per_batch_counts`. Trying to access that in the user code results in a segmentation fault. The issue is patched in commit 3cbb917b4714766030b28eba9fb41bb97ce9ee02 and is released in TensorFlow version 2.3.1.No Score
CVE-2020-15199In Tensorflow before version 2.3.1, the `RaggedCountSparseOutput` does not validate that the input arguments form a valid ragged tensor. In particular, there is no validation that the `splits` tensor has the minimum required number of elements. Code uses this quantity to initialize a different data structure. Since `BatchedMap` is equivalent to a vector, it needs to have at least one element to not be `nullptr`. If user passes a `splits` tensor that is empty or has exactly one element, we get a `SIGABRT` signal raised by the operating system. The issue is patched in commit 3cbb917b4714766030b28eba9fb41bb97ce9ee02 and is released in TensorFlow version 2.3.1.No Score
CVE-2020-15198In Tensorflow before version 2.3.1, the `SparseCountSparseOutput` implementation does not validate that the input arguments form a valid sparse tensor. In particular, there is no validation that the `indices` tensor has the same shape as the `values` one. The values in these tensors are always accessed in parallel. Thus, a shape mismatch can result in accesses outside the bounds of heap allocated buffers. The issue is patched in commit 3cbb917b4714766030b28eba9fb41bb97ce9ee02 and is released in TensorFlow version 2.3.1.No Score
CVE-2020-15197In Tensorflow before version 2.3.1, the `SparseCountSparseOutput` implementation does not validate that the input arguments form a valid sparse tensor. In particular, there is no validation that the `indices` tensor has rank 2. This tensor must be a matrix because code assumes its elements are accessed as elements of a matrix. However, malicious users can pass in tensors of different rank, resulting in a `CHECK` assertion failure and a crash. This can be used to cause denial of service in serving installations, if users are allowed to control the components of the input sparse tensor. The issue is patched in commit 3cbb917b4714766030b28eba9fb41bb97ce9ee02 and is released in TensorFlow version 2.3.1.No Score
CVE-2020-15196In Tensorflow version 2.3.0, the `SparseCountSparseOutput` and `RaggedCountSparseOutput` implementations don't validate that the `weights` tensor has the same shape as the data. The check exists for `DenseCountSparseOutput`, where both tensors are fully specified. In the sparse and ragged count weights are still accessed in parallel with the data. But, since there is no validation, a user passing fewer weights than the values for the tensors can generate a read from outside the bounds of the heap buffer allocated for the weights. The issue is patched in commit 3cbb917b4714766030b28eba9fb41bb97ce9ee02 and is released in TensorFlow version 2.3.1.No Score
CVE-2020-15195In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the implementation of `SparseFillEmptyRowsGrad` uses a double indexing pattern. It is possible for `reverse_index_map(i)` to be an index outside of bounds of `grad_values`, thus resulting in a heap buffer overflow. 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.No Score
CVE-2020-15194In 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."No Score
CVE-2020-15193In Tensorflow before versions 2.2.1 and 2.3.1, the implementation of `dlpack.to_dlpack` can be made to use uninitialized memory resulting in further memory corruption. This is because the pybind11 glue code assumes that the argument is a tensor. However, there is nothing stopping users from passing in a Python object instead of a tensor. The uninitialized memory address is due to a `reinterpret_cast` Since the `PyObject` is a Python object, not a TensorFlow Tensor, the cast to `EagerTensor` fails. The issue is patched in commit 22e07fb204386768e5bcbea563641ea11f96ceb8 and is released in TensorFlow versions 2.2.1, or 2.3.1.No Score
CVE-2020-15192In Tensorflow before versions 2.2.1 and 2.3.1, if a user passes a list of strings to `dlpack.to_dlpack` there is a memory leak following an expected validation failure. The issue occurs because the `status` argument during validation failures is not properly checked. Since each of the above methods can return an error status, the `status` value must be checked before continuing. The issue is patched in commit 22e07fb204386768e5bcbea563641ea11f96ceb8 and is released in TensorFlow versions 2.2.1, or 2.3.1.No Score
CVE-2020-15191In Tensorflow before versions 2.2.1 and 2.3.1, if a user passes an invalid argument to `dlpack.to_dlpack` the expected validations will cause variables to bind to `nullptr` while setting a `status` variable to the error condition. However, this `status` argument is not properly checked. Hence, code following these methods will bind references to null pointers. This is undefined behavior and reported as an error if compiling with `-fsanitize=null`. The issue is patched in commit 22e07fb204386768e5bcbea563641ea11f96ceb8 and is released in TensorFlow versions 2.2.1, or 2.3.1.No Score
CVE-2020-15190In 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.No Score
CVE-2020-25149An issue was discovered in Observium Professional, Enterprise & Community 20.8.10631. It is vulnerable to directory traversal and local file inclusion due to the fact that there is an unrestricted possibility of loading any file with an inc.php extension. Inclusion of other files (even though limited to the mentioned extension) can lead to Remote Code Execution. This can occur via /device/device=345/?tab=health&metric=../ because of device/health.inc.php.No Score
CVE-2020-25148An issue was discovered in Observium Professional, Enterprise & Community 20.8.10631. It is vulnerable to Cross-Site Scripting (XSS) due to the fact that it is possible to inject and store malicious JavaScript code within it. this can occur via /iftype/type= because of pages/iftype.inc.php.No Score
CVE-2020-25147An issue was discovered in Observium Professional, Enterprise & Community 20.8.10631. It is vulnerable to SQL Injection due to the fact that it is possible to inject malicious SQL statements in malformed parameter types. This can occur via username[0] to the default URI, because of includes/authenticate.inc.php.No Score
CVE-2020-25146An issue was discovered in Observium Professional, Enterprise & Community 20.8.10631. It is vulnerable to Cross-Site Scripting (XSS) due to the fact that it is possible to inject and store malicious JavaScript code within it. This can occur via la_id to the /syslog_rules URI for edit_syslog_rule.No Score

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