A Incorrect Authorization vulnerability in chkstat of SUSE Linux Enterprise Server 12-SP5; openSUSE Leap 15.3, openSUSE Leap 15.4, openSUSE Leap Micro 5.2 did not consider group writable path components, allowing local attackers with access to a group what can write to a location included in the path to a privileged binary to influence path resolution. This issue affects: SUSE Linux Enterprise Server 12-SP5 permissions versions prior to 20170707. openSUSE Leap 15.3 permissions versions prior to 20200127. openSUSE Leap 15.4 permissions versions prior to 20201225. openSUSE Leap Micro 5.2 permissions versions prior to 20181225.

An issue was discovered in uriparser before 0.9.6. It performs invalid free operations in uriFreeUriMembers and uriMakeOwner.

An issue was discovered in uriparser before 0.9.6. It performs invalid free operations in uriNormalizeSyntax.

gdhcp in ConnMan before 1.39 could be used by network-adjacent attackers to leak sensitive stack information, allowing further exploitation of bugs in gdhcp.

An issue was discovered in SDDM before 0.19.0. It incorrectly starts the X server in a way that - for a short time period - allows local unprivileged users to create a connection to the X server without providing proper authentication. A local attacker can thus access X server display contents and, for example, intercept keystrokes or access the clipboard. This is caused by a race condition during Xauthority file creation.

A null pointer dereference flaw was found in samba's Winbind service in versions before 4.11.15, before 4.12.9 and before 4.13.1. A local user could use this flaw to crash the winbind service causing denial of service.

An issue was discovered in the Linux kernel through 5.9.1, as used with Xen through 4.14.x. Guest OS users can cause a denial of service (host OS hang) via a high rate of events to dom0, aka CID-e99502f76271.

ImageMagick 7.0.10-34 allows Division by Zero in OptimizeLayerFrames in MagickCore/layer.c, which may cause a denial of service.

An unspecified vulnerability in Java SE could allow an unauthenticated attacker to obtain sensitive information resulting in a low confidentiality impact using unknown attack vectors.

An unspecified vulnerability in Java SE related to the Libraries component could allow an unauthenticated attacker to cause no confidentiality impact, low integrity impact, and no availability impact.

A flaw was found in the way the Libraries component of OpenJDK handled blacklists of untrusted certificates. Alternate certificate encodings were not considered, causing certain certificate fingerprints to not be blacklisted, possibly leading to untrusted certificates being accepted.

An unspecified vulnerability in Java SE related to the Hotspot component could allow an unauthenticated attacker to cause low confidentiality impact, low integrity impact, and no availability impact.

An unspecified vulnerability in Java SE related to the JNDI component could allow an unauthenticated attacker to obtain sensitive information resulting in a low confidentiality impact using unknown attack vectors.

An unspecified vulnerability in Java SE related to the Serialization component could allow an unauthenticated attacker to cause a denial of service resulting in a low availability impact using unknown attack vectors.

In kdeconnect-kde (aka KDE Connect) before 20.08.2, an attacker on the local network could send crafted packets that trigger use of large amounts of CPU, memory, or network connection slots, aka a Denial of Service attack.

A missing rate limit in the Preferred Providers app 1.7.0 allowed an attacker to set the password an uncontrolled amount of times.

Impact 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.

Patches 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.

Workarounds A potential workaround would be to add a custom Verifier to the model loading code to ensure that no operator reuses tensors as both inputs and outputs. Care should be taken to check all types of inputs (i.e., constant or variable tensors as well as optional tensors).

For more information Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.

Attribution This vulnerability has been discovered from a variant analysis of GHSA-cvpc-8phh-8f45.

Impact In TensorFlow Lite, 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: https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/lite/kernels/kernelutil.cc#L36

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: https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/lite/c/common.h#L82

This results in special casing during validation at model loading time: https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/lite/core/subgraph.cc#L566-L580

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.

Patches We have patched the issue in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83). We 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.

Workarounds 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.

For more information Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.

Attribution This vulnerability has been reported by members of the Aivul Team from Qihoo 360.

Impact 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: https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/lite/core/subgraph.cc#L1224-L1227

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.

Patches We have patched the issue in 0b5662bc 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.

For more information Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.

Attribution This vulnerability has been reported by members of the Aivul Team from Qihoo 360 but was also discovered through variant analysis of GHSA-cvpc-8phh-8f45.

Impact In eager mode, TensorFlow does not set the session state. Hence, calling tf.rawops.GetSessionHandle or tf.rawops.GetSessionHandleV2 results in a null pointer dereference: https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/core/kernels/sessionops.cc#L45

In the above snippet, in eager mode, ctx->sessionstate() returns nullptr. Since code immediately dereferences this, we get a segmentation fault.

Patches We have patched the issue in 9a133d73ae4b4664d22bd1aa6d654fec13c52ee1 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.

For more information Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.

Attribution This vulnerability has been reported by members of the Aivul Team from Qihoo 360.

Impact If a user passes an invalid argument to dlpack.todlpack 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: https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/c/eager/dlpack.cc#L265-L267

Hence, code following these methods will bind references to null pointers: https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/c/eager/dlpack.cc#L279-L285

This is undefined behavior and reported as an error if compiling with -fsanitize=null.

Patches We have patched the issue in 22e07fb204386768e5bcbea563641ea11f96ceb8 and will release a patch release for all affected versions.

We recommend users to upgrade to TensorFlow 2.2.1 or 2.3.1.

For more information Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.

Attribution This vulnerability has been discovered during variant analysis of GHSA-rjjg-hgv6-h69v.

Impact The SparseFillEmptyRowsGrad implementation has incomplete validation of the shapes of its arguments: https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/core/kernels/sparsefillemptyrowsop.cc#L235-L241

Although reverseindexmapt and gradvaluest are accessed in a similar pattern, only reverseindexmapt is validated to be of proper shape. Hence, malicious users can pass a bad gradvaluest to trigger an assertion failure in vec, causing denial of service in serving installations.

Patches We have patched the issue in 390611e0d45c5793c7066110af37c8514e6a6c54 and will release a patch release for all affected versions.

We recommend users to upgrade to TensorFlow 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.

For more information Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.

Attribution This vulnerability is a variant of GHSA-63xm-rx5p-xvqr

Impact If a user passes a list of strings to dlpack.todlpack there is a memory leak following an expected validation failure: https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/c/eager/dlpack.cc#L100-L104

The allocated memory is from https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/c/eager/dlpack.cc#L256

The issue occurs because the status argument during validation failures is not properly checked: https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/c/eager/dlpack.cc#L265-L267

Since each of the above methods can return an error status, the status value must be checked before continuing.

Patches We have patched the issue in 22e07fb204386768e5bcbea563641ea11f96ceb8 and will release a patch release for all affected versions.

We recommend users to upgrade to TensorFlow 2.2.1 or 2.3.1.

For more information Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.

Attribution This vulnerability has been discovered during variant analysis of GHSA-rjjg-hgv6-h69v.

Impact The tf.rawops.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: https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/core/commonruntime/eager/kernelanddevice.cc#L308-L313

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

Patches We have patched the issue in da8558533d925694483d2c136a9220d6d49d843c and will release a patch release for all affected versions.

We recommend users to upgrade to TensorFlow 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.

For more information Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.

Attribution This vulnerability has been reported by members of the Aivul Team from Qihoo 360.

Pagure before 5.6 allows XSS via the templates/blame.html blame view.

An issue was discovered in Xen through 4.14.x. x86 PV guest kernels can experience denial of service via SYSENTER. The SYSENTER instruction leaves various state sanitization activities to software. One of Xen's sanitization paths injects a #GP fault, and incorrectly delivers it twice to the guest. This causes the guest kernel to observe a kernel-privilege #GP fault (typically fatal) rather than a user-privilege #GP fault (usually converted into SIGSEGV/etc.). Malicious or buggy userspace can crash the guest kernel, resulting in a VM Denial of Service. All versions of Xen from 3.2 onwards are vulnerable. Only x86 systems are vulnerable. ARM platforms are not vulnerable. Only x86 systems that support the SYSENTER instruction in 64bit mode are vulnerable. This is believed to be Intel, Centaur, and Shanghai CPUs. AMD and Hygon CPUs are not believed to be vulnerable. Only x86 PV guests can exploit the vulnerability. x86 PVH / HVM guests cannot exploit the vulnerability.

An issue was discovered in Xen through 4.14.x. There is a race condition when migrating timers between x86 HVM vCPUs. When migrating timers of x86 HVM guests between its vCPUs, the locking model used allows for a second vCPU of the same guest (also operating on the timers) to release a lock that it didn't acquire. The most likely effect of the issue is a hang or crash of the hypervisor, i.e., a Denial of Service (DoS). All versions of Xen are affected. Only x86 systems are vulnerable. Arm systems are not vulnerable. Only x86 HVM guests can leverage the vulnerability. x86 PV and PVH cannot leverage the vulnerability. Only guests with more than one vCPU can exploit the vulnerability.

An issue was discovered in Xen through 4.14.x. An x86 PV guest can trigger a host OS crash when handling guest access to MSRMISCENABLE. When a guest accesses certain Model Specific Registers, Xen first reads the value from hardware to use as the basis for auditing the guest access. For the MISCENABLE MSR, which is an Intel specific MSR, this MSR read is performed without error handling for a #GP fault, which is the consequence of trying to read this MSR on non-Intel hardware. A buggy or malicious PV guest administrator can crash Xen, resulting in a host Denial of Service. Only x86 systems are vulnerable. ARM systems are not vulnerable. Only Xen versions 4.11 and onwards are vulnerable. 4.10 and earlier are not vulnerable. Only x86 systems that do not implement the MISCENABLE MSR (0x1a0) are vulnerable. AMD and Hygon systems do not implement this MSR and are vulnerable. Intel systems do implement this MSR and are not vulnerable. Other manufacturers have not been checked. Only x86 PV guests can exploit the vulnerability. x86 HVM/PVH guests cannot exploit the vulnerability.

An issue was discovered in Xen through 4.14.x. There is a lack of preemption in evtchnreset() / evtchndestroy(). In particular, the FIFO event channel model allows guests to have a large number of event channels active at a time. Closing all of these (when resetting all event channels or when cleaning up after the guest) may take extended periods of time. So far, there was no arrangement for preemption at suitable intervals, allowing a CPU to spend an almost unbounded amount of time in the processing of these operations. Malicious or buggy guest kernels can mount a Denial of Service (DoS) attack affecting the entire system. All Xen versions are vulnerable in principle. Whether versions 4.3 and older are vulnerable depends on underlying hardware characteristics.

An issue was discovered in Xen through 4.14.x. Out of bounds event channels are available to 32-bit x86 domains. The so called 2-level event channel model imposes different limits on the number of usable event channels for 32-bit x86 domains vs 64-bit or Arm (either bitness) ones. 32-bit x86 domains can use only 1023 channels, due to limited space in their shared (between guest and Xen) information structure, whereas all other domains can use up to 4095 in this model. The recording of the respective limit during domain initialization, however, has occurred at a time where domains are still deemed to be 64-bit ones, prior to actually honoring respective domain properties. At the point domains get recognized as 32-bit ones, the limit didn't get updated accordingly. Due to this misbehavior in Xen, 32-bit domains (including Domain 0) servicing other domains may observe event channel allocations to succeed when they should really fail. Subsequent use of such event channels would then possibly lead to corruption of other parts of the shared info structure. An unprivileged guest may cause another domain, in particular Domain 0, to misbehave. This may lead to a Denial of Service (DoS) for the entire system. All Xen versions from 4.4 onwards are vulnerable. Xen versions 4.3 and earlier are not vulnerable. Only x86 32-bit domains servicing other domains are vulnerable. Arm systems, as well as x86 64-bit domains, are not vulnerable.