CVE-2026-86082

n8n is an open source workflow automation platform. Prior to 1.123.76, 2.37.7, and 2.38.2, the OpenAI Chat Model node enforced credential allowed-domain restrictions for normal calls but not for the model-search dropdown. A workflow editor could set options.baseURL to an arbitrary host and make t...

CVE-2026-86082 n8n: Domain-Restriction Bypass via Unguarded Model-Search Endpoint in OpenAI Chat Model Node

n8n is an open source workflow automation platform. Prior to 1.123.76, 2.37.7, and 2.38.2, the OpenAI Chat Model node enforced credential allowed-domain restrictions for normal calls but not for the model-search dropdown. A workflow editor could set options.baseURL to an arbitrary host and make t...

CVE-2026-86082

n8n , an open source workflow automation platform, is affected by a domain-restriction bypass in the OpenAI Chat Model node . The root cause is that the searchModels code path in loadModels.ts omitted the assertOpenAiCredentialAllowsUrl check, meaning the credential allowed-domain restrictions en...

CVE-2026-86082 n8n: Domain-Restriction Bypass via Unguarded Model-Search Endpoint in OpenAI Chat Model Node

n8n is an open source workflow automation platform. Prior to 1.123.76, 2.37.7, and 2.38.2, the OpenAI Chat Model node enforced credential allowed-domain restrictions for normal calls but not for the model-search dropdown. A workflow editor could set options.baseURL to an arbitrary host and make t...

CVE-2026-86082 n8n: Domain-Restriction Bypass via Unguarded Model-Search Endpoint in OpenAI Chat Model Node

n8n is an open source workflow automation platform. Prior to 1.123.76, 2.37.7, and 2.38.2, the OpenAI Chat Model node enforced credential allowed-domain restrictions for normal calls but not for the model-search dropdown. A workflow editor could set options.baseURL to an arbitrary host and make t...

CVE-2026-79721

Code execution can occur in versions of the MLflow platform running version 0.0.1 or newer, enabling a maliciously crafted model artifact to execute arbitrary code on an end user's system when loaded by the project...

Unsafe Dependency Resolution

Overview mlflow is a platform to streamline machine learning development, including tracking experiments, packaging code into reproducible runs, and sharing and deploying models. Affected versions of this package are vulnerable to Unsafe Dependency Resolution through the model artifact loading...

CVE-2026-79721

MLflow platform (version 0.0.1 or newer ) is vulnerable to arbitrary code execution via a maliciously crafted model artifact . When an end user loads a tampered artifact into a project, it can execute attacker-controlled code on their system. The CNA-assigned CVSS 4.0 score is 8.6 (HIGH) , reflec...

CVE-2026-79721

Code execution can occur in versions of the MLflow platform running version 0.0.1 or newer, enabling a maliciously crafted model artifact to execute arbitrary code on an end user's system when loaded by the project...

EUVD-2026-74038

Code execution can occur in versions of the MLflow platform running version 0.0.1 or newer, enabling a maliciously crafted model artifact to execute arbitrary code on an end user's system when loaded by the project...

CVE-2026-79721

Code execution can occur in versions of the MLflow platform running version 0.0.1 or newer, enabling a maliciously crafted model artifact to execute arbitrary code on an end user's system when loaded by the project...

EEF-CVE-2026-78230 AshAi aggregate tool can read field-policy-protected fields

Summary AshAi exposes Ash read actions to language-model tool calls. The read tool accepts an aggregate result type min, max, sum, avg that builds an ad-hoc Ash.Query.Aggregate over a named field and returns its raw value. Ash field policies redact forbidden fields on returned records replacing...

EUVD-2026-72924

AshAi exposes Ash read actions to language-model tool calls. The read tool accepts an aggregate result type min, max, sum, avg that builds an ad-hoc Ash.Query.Aggregate over a named field and returns its raw value. Ash field policies redact forbidden fields on returned records replacing them with...

EUVD-2026-65229

NLTK: Unsafe Pickle Deserialization in TransitionParser Allows Remote Code Execution...

GHSA-RHP5-R9X4-F5G2 NLTK: Unsafe Pickle Deserialization in TransitionParser Allows Remote Code Execution

Summary The NLTK library's TransitionParser.parse method deserializes model files using pickleload with the default restricted=False parameter, allowing arbitrary Python code execution when loading a malicious model file. The library provides a RestrictedUnpickler class for safe deserialization,...

CVE-2026-62437

A flaw in Xen allows a guest virtual machine's device model (DM) to re-establish interrupt request (IRQ) tracking structures after the initial cleanup process during guest termination. This causes certain structures to remain unallocated, resulting in a memory leak . A local attacker with control...

EUVD-2026-72719

When guests are terminated, various pieces of cleanup need carrying out. The cleaning up of PCI devices which were assigned to guests, and the associated removal of tracking structures for IRQs used by the devices occurs relatively early in the process. Unfortunately after that point the guest...

CVE-2026-62437 x86: DMs may cause mem leak by IRQ binding

When guests are terminated, various pieces of cleanup need carrying out. The cleaning up of PCI devices which were assigned to guests, and the associated removal of tracking structures for IRQs used by the devices occurs relatively early in the process. Unfortunately after that point the guest...

CVE-2026-62437 x86: DMs may cause mem leak by IRQ binding

When guests are terminated, various pieces of cleanup need carrying out. The cleaning up of PCI devices which were assigned to guests, and the associated removal of tracking structures for IRQs used by the devices occurs relatively early in the process. Unfortunately after that point the guest...

Stealing AI Reasoning Traces

Interesting research: "Stealing Reasoning Traces from Proprietary LLM APIs": Abstract: Leading large language model providers now conceal their models' step-by-step reasoning, or chain-of-thought, to protect intellectual property and limit information leakage. Rather than storing these traces...