MAL-2025-47651 Malicious code in customer-preference (npm)

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CVE-2025-32321

In isSafeIntent of AccountTypePreferenceLoader.java, there is a possible way to bypass an intent type check due to a confused deputy. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation...

CVE-2025-32345

In updateState of ContentProtectionTogglePreferenceController.java, there is a possible way for a secondary user to disable the primary user's deceptive app scanning setting due to a logic error in the code. This could lead to local escalation of privilege with no additional execution privileges...

CVE-2025-32345

In updateState of ContentProtectionTogglePreferenceController.java, there is a possible way for a secondary user to disable the primary user's deceptive app scanning setting due to a logic error in the code. This could lead to local escalation of privilege with no additional execution privileges...

CVE-2025-26435

In updateState of ContentProtectionTogglePreferenceController.java, there is a possible way for a secondary user to disable the primary user's deceptive app scanning setting due to a logic error in the code. This could lead to local escalation of privilege with no additional execution privileges...

Linux Distros Unpatched Vulnerability : CVE-2018-7563

The Linux/Unix host has one or more packages installed that are impacted by a vulnerability without a vendor supplied patch available. - An issue was discovered in GLPI through 9.2.1. The application is affected by XSS in the query string to front/preference.php. An attacker is able to create a...

Prompt Injection Vulnerability of Consensus Generating Applications in Digital Democracy

Large Language Models LLMs are gaining traction as a method to generate consensus statements and aggregate preferences in digital democracy experiments. Yet, LLMs may introduce critical vulnerabilities in these systems. Here, we explore the impact of prompt-injection attacks targeting consensus...

Attack the Messages, Not the Agents: a Multi-Round Adaptive Stealthy Tampering Framework for LLM-MAS

Large language model-based multi-agent systems LLM-MAS effectively accomplish complex and dynamic tasks through inter-agent communication, but this reliance introduces substantial safety vulnerabilities. Existing attack methods targeting LLM-MAS either compromise agent internals or rely on direct...

Rational Miner Behaviour, Protocol Stability, and Time Preference: an Austrian and Game-Theoretic Analysis of Bitcoin'S Incentive Environment

This paper integrates Austrian capital theory with repeated game theory to examine strategic miner behaviour under different institutional conditions in blockchain systems. It shows that when protocol rules are mutable, effective time preference rises, undermining rational long-term planning and...

Smart-LLaMA-DPO: Reinforced Large Language Model for Explainable Smart Contract Vulnerability Detection

Smart contract vulnerability detection remains a major challenge in blockchain security. Existing vulnerability detection methods face two main issues: 1 Existing datasets lack comprehensive coverage and high-quality explanations for preference learning. 2 Large language models LLMs often struggl...

GHSA-JWR7-992G-68MH starcitizentools/citizen-skin allows stored XSS in preference menu heading messages

Summary Various preferences messages are inserted into raw HTML, allowing anybody who can edit those messages to insert arbitrary HTML into the DOM. Details The innerHtml of the label div is set to the textContent of the label, essentially unsanitizing the system messages:...

Cross-site Scripting (XSS)

Overview Affected versions of this package are vulnerable to Cross-site Scripting XSS via the addPortlet.polyfill.js process. An attacker can execute arbitrary HTML or JavaScript code in the context of a user's browser by editing preference menu heading messages that are rendered without proper...

starcitizentools/citizen-skin allows stored XSS in preference menu heading messages

Summary Various preferences messages are inserted into raw HTML, allowing anybody who can edit those messages to insert arbitrary HTML into the DOM. Details The innerHtml of the label div is set to the textContent of the label, essentially unsanitizing the system messages:...

CVE-2023-21016

In AccountTypePreference of AccountTypePreference.java, there is a possible way to mislead the user about accounts installed on the device due to improper input validation. This could lead to local denial of service with no additional execution privileges needed. User interaction is not needed fo...

CVE-2023-29733

The Lock Master app 2.2.4 for Android allows unauthorized apps to modify the values in its SharedPreference files. These files hold data that affects many app functions. Malicious modifications by unauthorized apps can cause security issues, such as functionality manipulation, resulting in a seve...

CVE-2021-30972

This issue was addressed with improved checks. This issue is fixed in Security Update 2022-001 Catalina, macOS Big Sur 11.6.3. A malicious application may be able to bypass certain Privacy preferences...

CVE-2011-1652

The default configuration of Microsoft Windows 7 immediately prefers a new IPv6 and DHCPv6 service over a currently used IPv4 and DHCPv4 service upon receipt of an IPv6 Router Advertisement RA, and does not provide an option to ignore an unexpected RA, which allows remote attackers to conduct...

MPMA: Preference Manipulation Attack against Model Context Protocol

Model Context Protocol MCP standardizes interface mapping for large language models LLMs to access external data and tools, which revolutionizes the paradigm of tool selection and facilitates the rapid expansion of the LLM agent tool ecosystem. However, as the MCP is increasingly adopted,...

Improved Algorithms for Differentially Private Language Model Alignment

Language model alignment is crucial for ensuring that large language models LLMs align with human preferences, yet it often involves sensitive user data, raising significant privacy concerns. While prior work has integrated differential privacy DP with alignment techniques, their performance...

Private Federated Learning Using Preference-Optimized Synthetic Data

In practical settings, differentially private Federated learning DP-FL is the dominant method for training models from private, on-device client data. Recent work has suggested that DP-FL may be enhanced or outperformed by methods that use DP synthetic data Wu et al., 2024; Hou et al., 2024. The...