CVE-2026-84601 in macOS
Summary
by MITRE • 09/15/2026
A permissions issue was addressed with improved state management. This issue is fixed in macOS Golden Gate 27. An app may be able to bypass Apple Intelligence security prompts.
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Analysis
by VulDB Data Team • 09/15/2026
The vulnerability described involves a flaw in the permission handling and state management mechanisms within the Apple Intelligence framework on macOS, specifically prior to version 27 of the Golden Gate update. This issue allows an application to potentially circumvent the standard security prompts that are designed to notify users when sensitive data or system resources are being accessed by AI-related services. The core technical flaw lies in how the operating system tracks and validates the state of permission requests, creating a window where an app can exploit race conditions or improper state transitions to bypass these critical user consent mechanisms. This represents a significant deviation from the principle of least privilege and informed user consent, which are foundational to modern operating system security models.
From a technical perspective, this vulnerability aligns with CWE-284 Improper Access Control, as it involves an application gaining access to resources or data without proper authorization checks being enforced by the OS kernel or framework layer. Furthermore, the ability to bypass user prompts is indicative of CWE-798 Use of Hard-coded Credentials if the prompt bypass relies on hardcoded states, but more accurately fits CWE-200 Exposure of Sensitive Information through Discretionary Access Control flaws where the system fails to enforce its own security policies during specific operational sequences. The exploitation likely involves manipulating the internal state machine that governs when and how permission dialogs are presented or suppressed, allowing malicious software to operate under the guise of legitimate Apple Intelligence tasks without triggering user awareness alerts.
The operational impact of this vulnerability is severe due to the nature of Apple Intelligence services, which often process personal data including messages, emails, photos, and browsing history locally on the device. If an app can bypass these prompts, it may access sensitive private information without the user's knowledge or explicit consent. This undermines the trust model that macOS employs for AI-driven features, potentially leading to unauthorized surveillance, data exfiltration, or manipulation of personal digital records. Attackers could leverage this flaw in conjunction with other vulnerabilities to create a stealthy persistence mechanism or to harvest high-value intellectual property and personally identifiable information while appearing as benign background processes associated with system intelligence services.
In terms of threat modeling, this behavior is consistent with ATT&CK technique T1059 Command and Scripting Interpreter if the bypass allows for further script execution without detection, but more directly relates to T1213 Data from Information Repositories or T1005 Data from Local System where an adversary accesses data stored on the local device. The lack of user notification also aligns with ATT&CK tactic TA0006 Credential Access if sensitive credentials are processed by the AI service, and TA0007 Discovery for gathering system information. Defenders should view this as a critical integrity failure in the human-in-the-loop security model that macOS relies upon for privacy-preserving features.
Mitigation strategies primarily involve applying the official patch provided in macOS Golden Gate 27 or later versions, which addresses the underlying state management logic to ensure that permission prompts are consistently and correctly displayed before any sensitive data is accessed by Apple Intelligence components. Administrators should enforce strict software update policies across all endpoints running affected macOS versions. Additionally, organizations can implement monitoring solutions that detect anomalous behavior from applications claiming to use AI services but exhibiting patterns inconsistent with normal user interaction flows, such as rapid successive access requests or silent background processing of large data sets without corresponding UI events. Security awareness training should also emphasize the importance of reviewing permission grants for any application requesting access to sensitive areas of the system, particularly those leveraging new intelligence features.