DEBIAN-CVE-2024-11584

cloud-init through 25.1.2 includes the systemd socket unit cloud-init-hotplugd.socket with default SocketMode that grants 0666 permissions, making it world-writable. This is used for the "/run/cloud-init/hook-hotplug-cmd" FIFO. An unprivileged user could trigger hotplug-hook commands...

UBUNTU-CVE-2024-11584

cloud-init through 25.1.2 includes the systemd socket unit cloud-init-hotplugd.socket with default SocketMode that grants 0666 permissions, making it world-writable. This is used for the "/run/cloud-init/hook-hotplug-cmd" FIFO. An unprivileged user could trigger hotplug-hook commands...

CVE-2024-11584

CVE-2024-11584 affects cloud-init up to 25.1.2 where the systemd socket unit cloud-init-hotplugd.socket uses 0666 permissions, making the /run/cloud-init/hook-hotplug-cmd FIFO world-writable. This enables an unprivileged user to trigger hotplug-hook commands. The connected Nessus advisories confi...

CVE-2024-11584

cloud-init through 25.1.2 includes the systemd socket unit cloud-init-hotplugd.socket with default SocketMode that grants 0666 permissions, making it world-writable. This is used for the "/run/cloud-init/hook-hotplug-cmd" FIFO. An unprivileged user could trigger hotplug-hook commands...

CVE-2024-11584

cloud-init through 25.1.2 includes the systemd socket unit cloud-init-hotplugd.socket with default SocketMode that grants 0666 permissions, making it world-writable. This is used for the "/run/cloud-init/hook-hotplug-cmd" FIFO. An unprivileged user could trigger hotplug-hook commands...

Balancing Privacy and Utility in Correlated Data: a Study of Bayesian Differential Privacy

Privacy risks in differentially private DP systems increase significantly when data is correlated, as standard DP metrics often underestimate the resulting privacy leakage, leaving sensitive information vulnerable. Given the ubiquity of dependencies in real-world databases, this oversight poses a...

Vulnerability Disclosure through Adaptive Black-Box Adversarial Attacks on NIDS

Adversarial attacks, wherein slight inputs are carefully crafted to mislead intelligent models, have attracted increasing attention. However, a critical gap persists between theoretical advancements and practical application, particularly in structured data like network traffic, where...

SUSE CVE-2025-52991

The Nix, Lix, and Guix package managers default to using temporary build directories in a world-readable and world-writable location. This allows standard users to deceive the package manager into using directories with pre-existing content, potentially leading to unauthorized actions or data...

Anti-Phishing Training Does Not Work: a Large-Scale Empirical Assessment of Multi-Modal Training Grounded in the NIST Phish Scale

Social engineering attacks using email, commonly known as phishing, are a critical cybersecurity threat. Phishing attacks often lead to operational incidents and data breaches. As a result, many organizations allocate a substantial portion of their cybersecurity budgets to phishing awareness...

Differential Privacy in Machine Learning: from Symbolic AI to LLMs

Machine learning models should not reveal particular information that is not otherwise accessible. Differential privacy provides a formal framework to mitigate privacy risks by ensuring that the inclusion or exclusion of any single data point does not significantly alter the output of an algorith...

Understanding Content Moderation Policies and User Experiences in Generative AI Products

While recent research has focused on developing safeguards for generative AI GAI model-level content safety, little is known about how content moderation to prevent malicious content performs for end-users in real-world GAI products. To bridge this gap, we investigated content moderation policies...

Position: Certified Robustness Does Not (Yet) Imply Model Security

While certified robustness is widely promoted as a solution to adversarial examples in Artificial Intelligence systems, significant challenges remain before these techniques can be meaningfully deployed in real-world applications. We identify critical gaps in current research, including the parad...

Domain Adaptation for Image Classification of Defects in Semiconductor Manufacturing

In the semiconductor sector, due to high demand but also strong and increasing competition, time to market and quality are key factors in securing significant market share in various application areas. Thanks to the success of deep learning methods in recent years in the computer vision domain,...

RAS-Eval: a Comprehensive Benchmark for Security Evaluation of LLM Agents in Real-World Environments

The rapid deployment of Large language model LLM agents in critical domains like healthcare and finance necessitates robust security frameworks. To address the absence of standardized evaluation benchmarks for these agents in dynamic environments, we introduce RAS-Eval, a comprehensive security...

Unlearning-Enhanced Website Fingerprinting Attack: against Backdoor Poisoning in Anonymous Networks

Website Fingerprinting WF is an effective tool for regulating and governing the dark web. However, its performance can be significantly degraded by backdoor poisoning attacks in practical deployments. This paper aims to address the problem of hidden backdoor poisoning attacks faced by Website...

From Thinking to Output: Chain-Of-Thought and Text Generation Characteristics in Reasoning Language Models

Recently, there have been notable advancements in large language models LLMs, demonstrating their growing abilities in complex reasoning. However, existing research largely overlooks a thorough and systematic comparison of these models' reasoning processes and outputs, particularly regarding thei...

Towards Effective Complementary Security Analysis Using Large Language Models

A key challenge in security analysis is the manual evaluation of potential security weaknesses generated by static application security testing SAST tools. Numerous false positives FPs in these reports reduce the effectiveness of security analysis. We propose using Large Language Models LLMs to...

SAFER-D: a Self-Adaptive Security Framework for Distributed Computing Architectures

The rise of the Internet of Things and Cyber-Physical Systems has introduced new challenges on ensuring secure and robust communication. The growing number of connected devices increases network complexity, leading to higher latency and traffic. Distributed computing architectures DCAs have gaine...

Malicious code in world-id-js (npm)

--- -= Per source details. Do not edit below this line.=-...

AgentVigil: Generic Black-Box Red-Teaming for Indirect Prompt Injection against LLM Agents

The strong planning and reasoning capabilities of Large Language Models LLMs have fostered the development of agent-based systems capable of leveraging external tools and interacting with increasingly complex environments. However, these powerful features also introduce a critical security risk:...