CVE Feed

An issue was discovered in Hoosk CMS v1.8.0. There is a SQL injection vulnerability in install/index.php

A vulnerability in the Pulse Connect Secure < 9.1R8.2 admin web interface could allow an authenticated attacker to gain arbitrary file reading access through Pulse Collaboration via XML External Entity (XXE) vulnerability.

A vulnerability in the authenticated user web interface of Pulse Connect Secure and Pulse Policy Secure < 9.1R8.2 could allow attackers to conduct Cross-Site Scripting (XSS).

An issue was discovered in Hoosk CmS v1.8.0. There is an Remote Code Execution vulnerability in install/index.php

DO NOT USE THIS CANDIDATE NUMBER. ConsultIDs: none. Reason: This candidate was withdrawn by its CNA. Further investigation showed that it was not a security issue. Notes: none

An SSRF issue was discovered in Zoho Application Control Plus before version 10.0.511. The mail gateway configuration feature allows an attacker to perform a scan in order to discover open ports on a machine as well as available machines on the network segment on which the instance of the product is deployed.

An issue was discovered in Ozeki NG SMS Gateway through 4.17.6. It stores SMS messages in .NET serialized format on the filesystem. By generating (and writing to the disk) malicious .NET serialized files, an attacker can trick the product into deserializing them, resulting in arbitrary code execution.

An issue was discovered in Zoho Application Control Plus before version 10.0.511. The Element Configuration feature (to configure elements included in the scope of elements managed by the product) allows an attacker to retrieve the entire list of the IP ranges and subnets configured in the product and consequently obtain information about the cartography of the internal networks to which the product has access.

An issue was discovered in Ruby through 2.5.8, 2.6.x through 2.6.6, and 2.7.x through 2.7.1. WEBrick, a simple HTTP server bundled with Ruby, had not checked the transfer-encoding header value rigorously. An attacker may potentially exploit this issue to bypass a reverse proxy (which also has a poor header check), which may lead to an HTTP Request Smuggling attack.

A 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.

The 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.

An 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.

A 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.

An 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.

An 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.

A 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.

An 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.

An 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.

A 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.

A flaw was found in dpdk in versions before 18.11.10 and before 19.11.5. A flawed bounds checking in the copy_data function leads to a buffer overflow allowing an attacker in a virtual machine to write arbitrary data to any address in the vhost_crypto application. The highest threat from this vulnerability is to data confidentiality and integrity as well as system availability.

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. Virtio ring descriptors, and the data they describe are in a region of memory accessible by from both the virtual machine and the host. An attacker in a VM can change the contents of the memory after vhost_crypto has validated it. The highest threat from this vulnerability is to data confidentiality and integrity as well as system availability.

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.

In Apache Hadoop versions 3.0.0-alpha2 to 3.0.0, 2.9.0 to 2.9.2, 2.8.0 to 2.8.5, any users can access some servlets without authentication when Kerberos authentication is enabled and SPNEGO through HTTP is not enabled.

An 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.)

An 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.

An 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.

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.

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.

In 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.

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().

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.

XSS 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.

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 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 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 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.

In 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.

In 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.

In 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.

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 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.

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-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, 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-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, 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 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.

In 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.