TensorFlow is an end-to-end open source platform for machine learning. Sending invalid argument for `row_partition_types` of `tf.raw_ops.RaggedTensorToTensor` API results in a null pointer dereference and undefined behavior. The [implementation](https://github.com/tensorflow/tensorflow/blob/47a06f40411a69c99f381495f490536972152ac0/tensorflow/core/kernels/ragged_tensor_to_tensor_op.cc#L328) accesses the first element of a user supplied list of values without validating that the provided list is not empty. We have patched the issue in GitHub commit 301ae88b331d37a2a16159b65b255f4f9eb39314. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.
TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can craft a TFLite model that would trigger a null pointer dereference, which would result in a crash and denial of service. This is caused by the MLIR optimization of `L2NormalizeReduceAxis` operator. The [implementation](https://github.com/tensorflow/tensorflow/blob/149562d49faa709ea80df1d99fc41d005b81082a/tensorflow/compiler/mlir/lite/transforms/optimize.cc#L67-L70) unconditionally dereferences a pointer to an iterator to a vector without checking that the vector has elements. We have patched the issue in GitHub commit d6b57f461b39fd1aa8c1b870f1b974aac3554955. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.
in OpenHarmony v4.0.0 and prior versions allow a local attacker arbitrary code execution in pre-installed apps through use after free or cause DOS through NULL pointer dereference.
A null pointer dereference flaw was found in the hugetlbfs_fill_super function in the Linux kernel hugetlbfs (HugeTLB pages) functionality. This issue may allow a local user to crash the system or potentially escalate their privileges on the system.
In onNullBinding of TileLifecycleManager.java, there is a possible way to launch an activity from the background due to a missing null check. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.
In the Linux kernel, the following vulnerability has been resolved: xfs: avoid dereferencing log items after push callbacks After xfsaild_push_item() calls iop_push(), the log item may have been freed if the AIL lock was dropped during the push. Background inode reclaim or the dquot shrinker can free the log item while the AIL lock is not held, and the tracepoints in the switch statement dereference the log item after iop_push() returns. Fix this by capturing the log item type, flags, and LSN before calling xfsaild_push_item(), and introducing a new xfs_ail_push_class trace event class that takes these pre-captured values and the ailp pointer instead of the log item pointer.
TensorFlow is an end-to-end open source platform for machine learning. The implementation of `MatrixDiag*` operations(https://github.com/tensorflow/tensorflow/blob/4c4f420e68f1cfaf8f4b6e8e3eb857e9e4c3ff33/tensorflow/core/kernels/linalg/matrix_diag_op.cc#L195-L197) does not validate that the tensor arguments are non-empty. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.
TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger undefined behavior by binding to null pointer in `tf.raw_ops.ParameterizedTruncatedNormal`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/3f6fe4dfef6f57e768260b48166c27d148f3015f/tensorflow/core/kernels/parameterized_truncated_normal_op.cc#L630) does not validate input arguments before accessing the first element of `shape`. If `shape` argument is empty, then `shape_tensor.flat<T>()` is an empty array. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.
TensorFlow is an end-to-end open source platform for machine learning. The fix for CVE-2020-15209(https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15209) missed the case when the target shape of `Reshape` operator is given by the elements of a 1-D tensor. As such, the fix for the vulnerability(https://github.com/tensorflow/tensorflow/blob/9c1dc920d8ffb4893d6c9d27d1f039607b326743/tensorflow/lite/core/subgraph.cc#L1062-L1074) allowed passing a null-buffer-backed tensor with a 1D shape. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.
The regset (aka register set) feature in the Linux kernel before 3.2.10 does not properly handle the absence of .get and .set methods, which allows local users to cause a denial of service (NULL pointer dereference) or possibly have unspecified other impact via a (1) PTRACE_GETREGSET or (2) PTRACE_SETREGSET ptrace call.
Memory corruption while invoking the SubmitCommands call on Gfx engine during the graphics render.
Null pointer dereference in Windows Performance Counters allows an authorized attacker to elevate privileges locally.
Null pointer dereference in Windows Ancillary Function Driver for WinSock allows an authorized attacker to elevate privileges locally.
In the Linux kernel, the following vulnerability has been resolved: KVM: Don't clobber irqfd routing type when deassigning irqfd When deassigning a KVM_IRQFD, don't clobber the irqfd's copy of the IRQ's routing entry as doing so breaks kvm_arch_irq_bypass_del_producer() on x86 and arm64, which explicitly look for KVM_IRQ_ROUTING_MSI. Instead, to handle a concurrent routing update, verify that the irqfd is still active before consuming the routing information. As evidenced by the x86 and arm64 bugs, and another bug in kvm_arch_update_irqfd_routing() (see below), clobbering the entry type without notifying arch code is surprising and error prone. As a bonus, checking that the irqfd is active provides a convenient location for documenting _why_ KVM must not consume the routing entry for an irqfd that is in the process of being deassigned: once the irqfd is deleted from the list (which happens *before* the eventfd is detached), it will no longer receive updates via kvm_irq_routing_update(), and so KVM could deliver an event using stale routing information (relative to KVM_SET_GSI_ROUTING returning to userspace). As an even better bonus, explicitly checking for the irqfd being active fixes a similar bug to the one the clobbering is trying to prevent: if an irqfd is deactivated, and then its routing is changed, kvm_irq_routing_update() won't invoke kvm_arch_update_irqfd_routing() (because the irqfd isn't in the list). And so if the irqfd is in bypass mode, IRQs will continue to be posted using the old routing information. As for kvm_arch_irq_bypass_del_producer(), clobbering the routing type results in KVM incorrectly keeping the IRQ in bypass mode, which is especially problematic on AMD as KVM tracks IRQs that are being posted to a vCPU in a list whose lifetime is tied to the irqfd. Without the help of KASAN to detect use-after-free, the most common sympton on AMD is a NULL pointer deref in amd_iommu_update_ga() due to the memory for irqfd structure being re-allocated and zeroed, resulting in irqfd->irq_bypass_data being NULL when read by avic_update_iommu_vcpu_affinity(): BUG: kernel NULL pointer dereference, address: 0000000000000018 #PF: supervisor read access in kernel mode #PF: error_code(0x0000) - not-present page PGD 40cf2b9067 P4D 40cf2b9067 PUD 408362a067 PMD 0 Oops: Oops: 0000 [#1] SMP CPU: 6 UID: 0 PID: 40383 Comm: vfio_irq_test Tainted: G U W O 6.19.0-smp--5dddc257e6b2-irqfd #31 NONE Tainted: [U]=USER, [W]=WARN, [O]=OOT_MODULE Hardware name: Google, Inc. Arcadia_IT_80/Arcadia_IT_80, BIOS 34.78.2-0 09/05/2025 RIP: 0010:amd_iommu_update_ga+0x19/0xe0 Call Trace: <TASK> avic_update_iommu_vcpu_affinity+0x3d/0x90 [kvm_amd] __avic_vcpu_load+0xf4/0x130 [kvm_amd] kvm_arch_vcpu_load+0x89/0x210 [kvm] vcpu_load+0x30/0x40 [kvm] kvm_arch_vcpu_ioctl_run+0x45/0x620 [kvm] kvm_vcpu_ioctl+0x571/0x6a0 [kvm] __se_sys_ioctl+0x6d/0xb0 do_syscall_64+0x6f/0x9d0 entry_SYSCALL_64_after_hwframe+0x4b/0x53 RIP: 0033:0x46893b </TASK> ---[ end trace 0000000000000000 ]--- If AVIC is inhibited when the irfd is deassigned, the bug will manifest as list corruption, e.g. on the next irqfd assignment. list_add corruption. next->prev should be prev (ffff8d474d5cd588), but was 0000000000000000. (next=ffff8d8658f86530). ------------[ cut here ]------------ kernel BUG at lib/list_debug.c:31! Oops: invalid opcode: 0000 [#1] SMP CPU: 128 UID: 0 PID: 80818 Comm: vfio_irq_test Tainted: G U W O 6.19.0-smp--f19dc4d680ba-irqfd #28 NONE Tainted: [U]=USER, [W]=WARN, [O]=OOT_MODULE Hardware name: Google, Inc. Arcadia_IT_80/Arcadia_IT_80, BIOS 34.78.2-0 09/05/2025 RIP: 0010:__list_add_valid_or_report+0x97/0xc0 Call Trace: <TASK> avic_pi_update_irte+0x28e/0x2b0 [kvm_amd] kvm_pi_update_irte+0xbf/0x190 [kvm] kvm_arch_irq_bypass_add_producer+0x72/0x90 [kvm] irq_bypass_register_consumer+0xcd/0x170 [irqbypa ---truncated---
In the Linux kernel, the following vulnerability has been resolved: crypto: sun8i-ce - Fix use after free in unprepare sun8i_ce_cipher_unprepare should be called before crypto_finalize_skcipher_request, because client callbacks may immediately free memory, that isn't needed anymore. But it will be used by unprepare after free. Before removing prepare/unprepare callbacks it was handled by crypto engine in crypto_finalize_request. Usually that results in a pointer dereference problem during a in crypto selftest. Unable to handle kernel NULL pointer dereference at virtual address 0000000000000030 Mem abort info: ESR = 0x0000000096000004 EC = 0x25: DABT (current EL), IL = 32 bits SET = 0, FnV = 0 EA = 0, S1PTW = 0 FSC = 0x04: level 0 translation fault Data abort info: ISV = 0, ISS = 0x00000004, ISS2 = 0x00000000 CM = 0, WnR = 0, TnD = 0, TagAccess = 0 GCS = 0, Overlay = 0, DirtyBit = 0, Xs = 0 user pgtable: 4k pages, 48-bit VAs, pgdp=000000004716d000 [0000000000000030] pgd=0000000000000000, p4d=0000000000000000 Internal error: Oops: 0000000096000004 [#1] SMP This problem is detected by KASAN as well. ================================================================== BUG: KASAN: slab-use-after-free in sun8i_ce_cipher_do_one+0x6e8/0xf80 [sun8i_ce] Read of size 8 at addr ffff00000dcdc040 by task 1c15000.crypto-/373 Hardware name: Pine64 PinePhone (1.2) (DT) Call trace: dump_backtrace+0x9c/0x128 show_stack+0x20/0x38 dump_stack_lvl+0x48/0x60 print_report+0xf8/0x5d8 kasan_report+0x90/0xd0 __asan_load8+0x9c/0xc0 sun8i_ce_cipher_do_one+0x6e8/0xf80 [sun8i_ce] crypto_pump_work+0x354/0x620 [crypto_engine] kthread_worker_fn+0x244/0x498 kthread+0x168/0x178 ret_from_fork+0x10/0x20 Allocated by task 379: kasan_save_stack+0x3c/0x68 kasan_set_track+0x2c/0x40 kasan_save_alloc_info+0x24/0x38 __kasan_kmalloc+0xd4/0xd8 __kmalloc+0x74/0x1d0 alg_test_skcipher+0x90/0x1f0 alg_test+0x24c/0x830 cryptomgr_test+0x38/0x60 kthread+0x168/0x178 ret_from_fork+0x10/0x20 Freed by task 379: kasan_save_stack+0x3c/0x68 kasan_set_track+0x2c/0x40 kasan_save_free_info+0x38/0x60 __kasan_slab_free+0x100/0x170 slab_free_freelist_hook+0xd4/0x1e8 __kmem_cache_free+0x15c/0x290 kfree+0x74/0x100 kfree_sensitive+0x80/0xb0 alg_test_skcipher+0x12c/0x1f0 alg_test+0x24c/0x830 cryptomgr_test+0x38/0x60 kthread+0x168/0x178 ret_from_fork+0x10/0x20 The buggy address belongs to the object at ffff00000dcdc000 which belongs to the cache kmalloc-256 of size 256 The buggy address is located 64 bytes inside of freed 256-byte region [ffff00000dcdc000, ffff00000dcdc100)
In the Linux kernel, the following vulnerability has been resolved: scsi: mpt3sas: Fix kernel panic during drive powercycle test While looping over shost's sdev list it is possible that one of the drives is getting removed and its sas_target object is freed but its sdev object remains intact. Consequently, a kernel panic can occur while the driver is trying to access the sas_address field of sas_target object without also checking the sas_target object for NULL.
In the Linux kernel, the following vulnerability has been resolved: hwmon: (w83791d) Fix NULL pointer dereference by removing unnecessary structure field If driver read val value sufficient for (val & 0x08) && (!(val & 0x80)) && ((val & 0x7) == ((val >> 4) & 0x7)) from device then Null pointer dereference occurs. (It is possible if tmp = 0b0xyz1xyz, where same literals mean same numbers) Also lm75[] does not serve a purpose anymore after switching to devm_i2c_new_dummy_device() in w83791d_detect_subclients(). The patch fixes possible NULL pointer dereference by removing lm75[]. Found by Linux Driver Verification project (linuxtesting.org). [groeck: Dropped unnecessary continuation lines, fixed multi-line alignment]
Memory Corruption when running a memory copy operation due to invalid writes caused by a null pointer.
TensorFlow is an open source platform for machine learning. In affected versions the code for boosted trees in TensorFlow is still missing validation. As a result, attackers can trigger denial of service (via dereferencing `nullptr`s or via `CHECK`-failures) as well as abuse undefined behavior (binding references to `nullptr`s). An attacker can also read and write from heap buffers, depending on the API that gets used and the arguments that are passed to the call. Given that the boosted trees implementation in TensorFlow is unmaintained, it is recommend to no longer use these APIs. We will deprecate TensorFlow's boosted trees APIs in subsequent releases. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.
A crafted NTFS image can cause a NULL pointer dereference in ntfs_extent_inode_open in NTFS-3G < 2021.8.22.
Memory Corruption when writing to invalid memory locations occurs due to heap memory exhaustion during secure data initialization.
win32k.sys in the kernel-mode drivers in Microsoft Windows Vista SP1 and SP2, Windows Server 2008 Gold, SP2, R2, and R2 SP1, and Windows 7 Gold and SP1 allows local users to gain privileges via a crafted application that triggers a NULL pointer dereference, a different vulnerability than other CVEs listed in MS11-054, aka "Win32k Null Pointer De-reference Vulnerability."
The cifs_close function in fs/cifs/file.c in the Linux kernel before 2.6.39 allows local users to cause a denial of service (NULL pointer dereference and BUG) or possibly have unspecified other impact by setting the O_DIRECT flag during an attempt to open a file on a CIFS filesystem.
TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation of SVDF in TFLite is [vulnerable to a null pointer error](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/lite/kernels/svdf.cc#L300-L313). The [`GetVariableInput` function](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/lite/kernels/kernel_util.cc#L115-L119) can return a null pointer but `GetTensorData` assumes that the argument is always a valid tensor. Furthermore, because `GetVariableInput` calls [`GetMutableInput`](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/lite/kernels/kernel_util.cc#L82-L90) which might return `nullptr`, the `tensor->is_variable` expression can also trigger a null pointer exception. We have patched the issue in GitHub commit 5b048e87e4e55990dae6b547add4dae59f4e1c76. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.
TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can cause undefined behavior via binding a reference to null pointer in all binary cwise operations that don't require broadcasting (e.g., gradients of binary cwise operations). The [implementation](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/cwise_ops_common.h#L264) assumes that the two inputs have exactly the same number of elements but does not check that. Hence, when the eigen functor executes it triggers heap OOB reads and undefined behavior due to binding to nullptr. We have patched the issue in GitHub commit 93f428fd1768df147171ed674fee1fc5ab8309ec. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.
TensorFlow is an end-to-end open source platform for machine learning. In affected versions the code for `tf.raw_ops.SaveV2` does not properly validate the inputs and an attacker can trigger a null pointer dereference. The [implementation](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/save_restore_v2_ops.cc) uses `ValidateInputs` to check that the input arguments are valid. This validation would have caught the illegal state represented by the reproducer above. However, the validation uses `OP_REQUIRES` which translates to setting the `Status` object of the current `OpKernelContext` to an error status, followed by an empty `return` statement which just terminates the execution of the function it is present in. However, this does not mean that the kernel execution is finalized: instead, execution continues from the next line in `Compute` that follows the call to `ValidateInputs`. This is equivalent to lacking the validation. We have patched the issue in GitHub commit 9728c60e136912a12d99ca56e106b7cce7af5986. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.
TensorFlow is an end-to-end open source platform for machine learning. When restoring tensors via raw APIs, if the tensor name is not provided, TensorFlow can be tricked into dereferencing a null pointer. Alternatively, attackers can read memory outside the bounds of heap allocated data by providing some tensor names but not enough for a successful restoration. The [implementation](https://github.com/tensorflow/tensorflow/blob/47a06f40411a69c99f381495f490536972152ac0/tensorflow/core/kernels/save_restore_tensor.cc#L158-L159) retrieves the tensor list corresponding to the `tensor_name` user controlled input and immediately retrieves the tensor at the restoration index (controlled via `preferred_shard` argument). This occurs without validating that the provided list has enough values. If the list is empty this results in dereferencing a null pointer (undefined behavior). If, however, the list has some elements, if the restoration index is outside the bounds this results in heap OOB read. We have patched the issue in GitHub commit 9e82dce6e6bd1f36a57e08fa85af213e2b2f2622. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.
TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can craft a TFLite model that would trigger a null pointer dereference, which would result in a crash and denial of service. The [implementation](https://github.com/tensorflow/tensorflow/blob/149562d49faa709ea80df1d99fc41d005b81082a/tensorflow/lite/kernels/internal/optimized/optimized_ops.h#L268-L285) unconditionally dereferences a pointer. We have patched the issue in GitHub commit 15691e456c7dc9bd6be203b09765b063bf4a380c. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.
Possible null pointer dereference due to lack of WDOG structure validation during registration in Snapdragon Auto, Snapdragon Connectivity, Snapdragon Industrial IOT, Snapdragon Mobile
Null pointer dereference in Tianocore EDK2 may allow an authenticated user to potentially enable escalation of privilege via local access.
The keyctl_session_to_parent function in security/keys/keyctl.c in the Linux kernel 2.6.35.4 and earlier expects that a certain parent session keyring exists, which allows local users to cause a denial of service (NULL pointer dereference and system crash) or possibly have unspecified other impact via a KEYCTL_SESSION_TO_PARENT argument to the keyctl function.
Null pointer dereference in Microsoft Brokering File System allows an authorized attacker to elevate privileges locally.
TensorFlow is an end-to-end open source platform for machine learning. Incomplete validation in `SparseAdd` results in allowing attackers to exploit undefined behavior (dereferencing null pointers) as well as write outside of bounds of heap allocated data. The implementation(https://github.com/tensorflow/tensorflow/blob/656e7673b14acd7835dc778867f84916c6d1cac2/tensorflow/core/kernels/sparse_add_op.cc) has a large set of validation for the two sparse tensor inputs (6 tensors in total), but does not validate that the tensors are not empty or that the second dimension of `*_indices` matches the size of corresponding `*_shape`. This allows attackers to send tensor triples that represent invalid sparse tensors to abuse code assumptions that are not protected by validation. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.
TensorFlow is an end-to-end open source platform for machine learning. The implementation of TrySimplify(https://github.com/tensorflow/tensorflow/blob/c22d88d6ff33031aa113e48aa3fc9aa74ed79595/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc#L390-L401) has undefined behavior due to dereferencing a null pointer in corner cases that result in optimizing a node with no inputs. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.
Possible null pointer dereference due to lack of TLB validation for user provided address in Snapdragon Auto, Snapdragon Compute, Snapdragon Connectivity, Snapdragon Consumer Electronics Connectivity, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon Voice & Music, Snapdragon Wired Infrastructure and Networking
Possible null pointer dereference in thread cache operation handler due to lack of validation of user provided input in Snapdragon Auto, Snapdragon Compute, Snapdragon Connectivity, Snapdragon Consumer Electronics Connectivity, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon Voice & Music, Snapdragon Wearables, Snapdragon Wired Infrastructure and Networking
Possible null pointer dereference in thread profile trap handler due to lack of thread ID validation before dereferencing it in Snapdragon Auto, Snapdragon Compute, Snapdragon Connectivity, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon Voice & Music, Snapdragon Wearables, Snapdragon Wired Infrastructure and Networking
Possible null pointer dereference in trap handler due to lack of thread ID validation before dereferencing it in Snapdragon Auto, Snapdragon Compute, Snapdragon Connectivity, Snapdragon Consumer Electronics Connectivity, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon Voice & Music, Snapdragon Wearables, Snapdragon Wired Infrastructure and Networking
TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a null pointer dereference by providing an invalid `permutation` to `tf.raw_ops.SparseMatrixSparseCholesky`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/080f1d9e257589f78b3ffb75debf584168aa6062/tensorflow/core/kernels/sparse/sparse_cholesky_op.cc#L85-L86) fails to properly validate the input arguments. Although `ValidateInputs` is called and there are checks in the body of this function, the code proceeds to the next line in `ValidateInputs` since `OP_REQUIRES`(https://github.com/tensorflow/tensorflow/blob/080f1d9e257589f78b3ffb75debf584168aa6062/tensorflow/core/framework/op_requires.h#L41-L48) is a macro that only exits the current function. Thus, the first validation condition that fails in `ValidateInputs` will cause an early return from that function. However, the caller will continue execution from the next line. The fix is to either explicitly check `context->status()` or to convert `ValidateInputs` to return a `Status`. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.
TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.MaxPool3DGradGrad` exhibits undefined behavior by dereferencing null pointers backing attacker-supplied empty tensors. The implementation(https://github.com/tensorflow/tensorflow/blob/72fe792967e7fd25234342068806707bbc116618/tensorflow/core/kernels/pooling_ops_3d.cc#L679-L703) fails to validate that the 3 tensor inputs are not empty. If any of them is empty, then accessing the elements in the tensor results in dereferencing a null pointer. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.
TensorFlow is an end-to-end open source platform for machine learning. Calling TF operations with tensors of non-numeric types when the operations expect numeric tensors result in null pointer dereferences. The conversion from Python array to C++ array(https://github.com/tensorflow/tensorflow/blob/ff70c47a396ef1e3cb73c90513da4f5cb71bebba/tensorflow/python/lib/core/ndarray_tensor.cc#L113-L169) is vulnerable to a type confusion. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.
TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.FusedBatchNorm` is vulnerable to a heap buffer overflow. If the tensors are empty, the same implementation can trigger undefined behavior by dereferencing null pointers. The implementation(https://github.com/tensorflow/tensorflow/blob/57d86e0db5d1365f19adcce848dfc1bf89fdd4c7/tensorflow/core/kernels/fused_batch_norm_op.cc) fails to validate that `scale`, `offset`, `mean` and `variance` (the last two only when required) all have the same number of elements as the number of channels of `x`. This results in heap out of bounds reads when the buffers backing these tensors are indexed past their boundary. If the tensors are empty, the validation mentioned in the above paragraph would also trigger and prevent the undefined behavior. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.
TensorFlow is an end-to-end open source platform for machine learning. In eager mode (default in TF 2.0 and later), session operations are invalid. However, users could still call the raw ops associated with them and trigger a null pointer dereference. The implementation(https://github.com/tensorflow/tensorflow/blob/eebb96c2830d48597d055d247c0e9aebaea94cd5/tensorflow/core/kernels/session_ops.cc#L104) dereferences the session state pointer without checking if it is valid. Thus, in eager mode, `ctx->session_state()` is nullptr and the call of the member function is undefined behavior. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.
In the Linux kernel, the following vulnerability has been resolved: devlink: rate: Unset parent pointer in devl_rate_nodes_destroy The function devl_rate_nodes_destroy is documented to "Unset parent for all rate objects". However, it was only calling the driver-specific `rate_leaf_parent_set` or `rate_node_parent_set` ops and decrementing the parent's refcount, without actually setting the `devlink_rate->parent` pointer to NULL. This leaves a dangling pointer in the `devlink_rate` struct, which cause refcount error in netdevsim[1] and mlx5[2]. In addition, this is inconsistent with the behavior of `devlink_nl_rate_parent_node_set`, where the parent pointer is correctly cleared. This patch fixes the issue by explicitly setting `devlink_rate->parent` to NULL after notifying the driver, thus fulfilling the function's documented behavior for all rate objects. [1] repro steps: echo 1 > /sys/bus/netdevsim/new_device devlink dev eswitch set netdevsim/netdevsim1 mode switchdev echo 1 > /sys/bus/netdevsim/devices/netdevsim1/sriov_numvfs devlink port function rate add netdevsim/netdevsim1/test_node devlink port function rate set netdevsim/netdevsim1/128 parent test_node echo 1 > /sys/bus/netdevsim/del_device dmesg: refcount_t: decrement hit 0; leaking memory. WARNING: CPU: 8 PID: 1530 at lib/refcount.c:31 refcount_warn_saturate+0x42/0xe0 CPU: 8 UID: 0 PID: 1530 Comm: bash Not tainted 6.18.0-rc4+ #1 NONE Hardware name: QEMU Standard PC (Q35 + ICH9, 2009), BIOS rel-1.16.0-0-gd239552ce722-prebuilt.qemu.org 04/01/2014 RIP: 0010:refcount_warn_saturate+0x42/0xe0 Call Trace: <TASK> devl_rate_leaf_destroy+0x8d/0x90 __nsim_dev_port_del+0x6c/0x70 [netdevsim] nsim_dev_reload_destroy+0x11c/0x140 [netdevsim] nsim_drv_remove+0x2b/0xb0 [netdevsim] device_release_driver_internal+0x194/0x1f0 bus_remove_device+0xc6/0x130 device_del+0x159/0x3c0 device_unregister+0x1a/0x60 del_device_store+0x111/0x170 [netdevsim] kernfs_fop_write_iter+0x12e/0x1e0 vfs_write+0x215/0x3d0 ksys_write+0x5f/0xd0 do_syscall_64+0x55/0x10f0 entry_SYSCALL_64_after_hwframe+0x4b/0x53 [2] devlink dev eswitch set pci/0000:08:00.0 mode switchdev devlink port add pci/0000:08:00.0 flavour pcisf pfnum 0 sfnum 1000 devlink port function rate add pci/0000:08:00.0/group1 devlink port function rate set pci/0000:08:00.0/32768 parent group1 modprobe -r mlx5_ib mlx5_fwctl mlx5_core dmesg: refcount_t: decrement hit 0; leaking memory. WARNING: CPU: 7 PID: 16151 at lib/refcount.c:31 refcount_warn_saturate+0x42/0xe0 CPU: 7 UID: 0 PID: 16151 Comm: bash Not tainted 6.17.0-rc7_for_upstream_min_debug_2025_10_02_12_44 #1 NONE Hardware name: QEMU Standard PC (Q35 + ICH9, 2009), BIOS rel-1.16.3-0-ga6ed6b701f0a-prebuilt.qemu.org 04/01/2014 RIP: 0010:refcount_warn_saturate+0x42/0xe0 Call Trace: <TASK> devl_rate_leaf_destroy+0x8d/0x90 mlx5_esw_offloads_devlink_port_unregister+0x33/0x60 [mlx5_core] mlx5_esw_offloads_unload_rep+0x3f/0x50 [mlx5_core] mlx5_eswitch_unload_sf_vport+0x40/0x90 [mlx5_core] mlx5_sf_esw_event+0xc4/0x120 [mlx5_core] notifier_call_chain+0x33/0xa0 blocking_notifier_call_chain+0x3b/0x50 mlx5_eswitch_disable_locked+0x50/0x110 [mlx5_core] mlx5_eswitch_disable+0x63/0x90 [mlx5_core] mlx5_unload+0x1d/0x170 [mlx5_core] mlx5_uninit_one+0xa2/0x130 [mlx5_core] remove_one+0x78/0xd0 [mlx5_core] pci_device_remove+0x39/0xa0 device_release_driver_internal+0x194/0x1f0 unbind_store+0x99/0xa0 kernfs_fop_write_iter+0x12e/0x1e0 vfs_write+0x215/0x3d0 ksys_write+0x5f/0xd0 do_syscall_64+0x53/0x1f0 entry_SYSCALL_64_after_hwframe+0x4b/0x53
Null pointer dereference in Windows Ancillary Function Driver for WinSock allows an authorized attacker to elevate privileges locally.
Null pointer dereference in Windows Ancillary Function Driver for WinSock allows an authorized attacker to elevate privileges locally.
i915_gem_userptr_get_pages in drivers/gpu/drm/i915/i915_gem_userptr.c in the Linux kernel 4.15.0 on Ubuntu 18.04.2 allows local users to cause a denial of service (NULL pointer dereference and BUG) or possibly have unspecified other impact via crafted ioctl calls to /dev/dri/card0.
In the Linux kernel, the following vulnerability has been resolved: s390/mm: Do not map lowcore with identity mapping Since the identity mapping is pinned to address zero the lowcore is always also mapped to address zero, this happens regardless of the relocate_lowcore command line option. If the option is specified the lowcore is mapped twice, instead of only once. This means that NULL pointer accesses will succeed instead of causing an exception (low address protection still applies, but covers only parts). To fix this never map the first two pages of physical memory with the identity mapping.
In the Linux kernel, the following vulnerability has been resolved: gfs2: No more self recovery When a node withdraws and it turns out that it is the only node that has the filesystem mounted, gfs2 currently tries to replay the local journal to bring the filesystem back into a consistent state. Not only is that a very bad idea, it has also never worked because gfs2_recover_func() will refuse to do anything during a withdraw. However, before even getting to this point, gfs2_recover_func() dereferences sdp->sd_jdesc->jd_inode. This was a use-after-free before commit 04133b607a78 ("gfs2: Prevent double iput for journal on error") and is a NULL pointer dereference since then. Simply get rid of self recovery to fix that.
In the Linux kernel, the following vulnerability has been resolved: drm/amd/display: remove oem i2c adapter on finish Fixes a bug where unbinding of the GPU would leave the oem i2c adapter registered resulting in a null pointer dereference when applications try to access the invalid device. (cherry picked from commit 89923fb7ead4fdd37b78dd49962d9bb5892403e6)