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🚨 CVE-2026-64438
In the Linux kernel, the following vulnerability has been resolved:

crypto: qat - fix VF2PF work teardown race in adf_disable_sriov()

The VF2PF interrupt handler queues PF-side response work that stores a
raw pointer to per-VF state (struct adf_accel_vf_info). Currently,
adf_disable_sriov() destroys per-VF mutexes and frees vf_info without
stopping new VF2PF work or waiting for in-flight workers to complete. A
concurrently scheduled or already queued worker can then dereference
freed memory.

This manifests as a use-after-free when KASAN is enabled:

BUG: KASAN: null-ptr-deref in mutex_lock+0x76/0xe0
Write of size 8 at addr 0000000000000260 by task kworker/24:2/...
Workqueue: qat_pf2vf_resp_wq adf_iov_send_resp [intel_qat]
Call Trace:
kasan_report+0x119/0x140
mutex_lock+0x76/0xe0
adf_gen4_pfvf_send+0xd4/0x1f0 [intel_qat]
adf_recv_and_handle_vf2pf_msg+0x290/0x360 [intel_qat]
adf_iov_send_resp+0x8c/0xe0 [intel_qat]
process_one_work+0x6ac/0xfd0
worker_thread+0x4dd/0xd30
kthread+0x326/0x410
ret_from_fork+0x33b/0x670

Add a PF-local flag, vf2pf_disabled, that gates work queueing, worker
processing, and interrupt re-enabling during teardown. Set this flag
atomically with the hardware interrupt mask inside
adf_disable_all_vf2pf_interrupts(). After masking, synchronize the AE
cluster MSI-X interrupt and flush the PF response workqueue before
tearing down per-VF locks and state so all in-flight work completes
before vf_info is destroyed.

Introduce adf_enable_all_vf2pf_interrupts() to clear the flag and
unmask all VF2PF interrupts under the same lock when SR-IOV is
re-enabled. This ensures the software flag and hardware state transition
atomically on both the enable and disable paths.

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🚨 CVE-2026-63559
An integer overflow in the UA_Variant arrayDimensions product
computation in open62541 may allow a remote attacker to read
out-of-bounds heap memory, potentially disclosing sensitive information.

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🚨 CVE-2026-63035
A heap use-after-free vulnerability in the TransferSubscriptions service
in open62541 may allow an authenticated attacker to cause a denial of
service or potentially execute arbitrary code.

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🚨 CVE-2026-63362
An unsigned integer underflow in the PubSub signature verification path
in open62541 may allow a remote attacker to cause a denial of service
via a crafted UDP packet.

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🚨 CVE-2026-63550
The MMS BER decoder contains a boundary-handling flaw in the processing
of certain fields within confirmed-request messages. When a crafted
BER-encoded element is received over an established MMS session (TCP
port 102), the decoder may advance its internal read position
incorrectly, leading to a heap out-of-bounds read. This condition causes
the MMS handling process to terminate unexpectedly, resulting in a
denial-of-service.

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🚨 CVE-2026-65421
The MMS BER decoder contains a flaw in decoding fixed-width BER fields
(boolean/integer): an attacker-supplied length value is not validated,
causing a read past the end of a heap buffer. This leads to termination
of the MMS service process and a denial-of-service condition.

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🚨 CVE-2026-65423
An integer overflow in the UA_Variant arrayDimensions product
computation in open62541 may allow a remote attacker to trigger an
out-of-bounds write.

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🚨 CVE-2026-66364
The GOOSE payload parser contains a boundary handling flaw that can be
triggered by a single unauthenticated Layer 2 multicast frame on the
process bus. When processing specific payload fields, an attacker
controlled inner element length may exceed its enclosing length, causing
the parser to over read by one byte. This out-of-bounds read reliably
terminates the subscriber process, resulting in a denial-of-service
condition.

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🚨 CVE-2026-66369
The GOOSE parser contains an off-by-one boundary-handling flaw that can
be triggered by a single unauthenticated Layer-2 multicast frame on the
process bus. When specific GOOSE message fields are processed, the
parser advances its internal buffer position incorrectly, resulting in a
heap out-of-bounds read. On affected platforms, this condition reliably
terminates the subscriber process and causes a denial-of-service.

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🚨 CVE-2026-66720
The GOOSE subscriber component improperly validates the UTC timestamp
field in unauthenticated IEC 61850 GOOSE (EtherType 0x88B8) Layer-2
multicast messages. A specially crafted GOOSE frame containing an
undersized timestamp field can trigger a heap out-of-bounds read during
message processing, causing the process to crash and resulting in a
denial-of-service condition.

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🚨 CVE-2026-9856
A vulnerability in huggingface/transformers versions <=5.8.0.dev0 allows an attacker to perform arbitrary file writes via path traversal. The issue resides in the `save_pretrained()` methods of `PreTrainedTokenizerBase` and `ProcessorMixin`, where keys from the `chat_template` dictionary are used directly as filenames without proper validation. An attacker can exploit this by publishing a malicious Hugging Face Hub repository with a crafted `tokenizer_config.json` file. When a victim downloads and saves the tokenizer or processor, the attacker-controlled keys can escape the intended save directory, enabling arbitrary file writes with attacker-controlled content. This vulnerability affects multiple processors inheriting from `ProcessorMixin`, including Idefics, Florence, Gemma, Phi, and Qwen-VL.

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🚨 CVE-2026-16315
OMICRON StationGuard before version 4.10 contains a cryptographic timing side-channel vulnerability in the backend authentication mechanism that may allow an unauthenticated attacker to forge valid authentication credentials, bypass authentication and authorization, and impersonate legitimate clients.
An attacker can gain full access to the system configuration, allowing modification, reset, or unauthorized alteration of system parameters.

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🚨 CVE-2026-16316
OMICRON StationGuard 4.00 contains an improper input validation vulnerability in its IEC 61850 Sampled Values (SV) frame processing. A specially crafted SV frame can cause the affected process to terminate, disrupting alert processing for Sampled Values traffic. The vulnerability does not affect overall system availability or the processing of other traffic types, and the process is automatically restarted, and the failure is immediately reported to the user.

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🚨 CVE-2026-15732
A Server-Side Request Forgery (SSFR) vulnerability exist in WGDashboard version 4.2.3 and earlier. The webhook functionality allows authenticated attackers to make arbitrary HTTP requests and retrieve responses.

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🚨 CVE-2026-15733
A Remote Code Execution (RCE) vulnerability exist in WGDashboard version 4.2.3 and earlier. Multiple OS command injection allows authenticated attackers to execute arbitrary commands as root.

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🚨 CVE-2026-15734
A Server-Side Template Injection (SSTI) vulnerability in WGDashboard version 4.3.2 and earlier, allows authenticated attackers to execute arbitrary code as root.

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🚨 CVE-2026-10595
A path traversal vulnerability exists in parisneo/lollms version 2.1.0, specifically in the SPA catch-all route implemented in `backend/routers/ui.py`. The vulnerability arises from the improper handling of user-controlled path input, which is directly joined into a filesystem path without sanitization or containment checks. URL-encoded dot-dot sequences (`%2e%2e`) bypass Starlette's built-in path normalization and are resolved by Python's `pathlib`, allowing an unauthenticated attacker to read arbitrary files on the server. This issue has been resolved in version 3.

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🚨 CVE-2026-12570
A vulnerability in keras-team/keras versions <= 3.15.0 allows for a denial of service (DoS) attack when loading malicious .keras model files via the keras.models.load_model() function. The H5IOStore.__getitem__ method in keras/src/saving/saving_lib.py does not validate the shape or size of datasets, leading to unbounded memory allocation. A specially crafted .keras file can exploit this flaw to trigger an out-of-memory (OOM) condition, causing the process to be terminated (exit code 137). This issue bypasses the fix for CVE-2026-0897, which only addressed a similar vulnerability in KerasFileEditor. The attack vector includes poisoned models from public repositories or malicious model registries, posing a risk to machine learning pipelines that process untrusted models.

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