Adversarial Attack on Large Language Models Using Exponentiated Gradient Descent

As Large Language Models LLMs are widely used, understanding them systematically is key to improving their safety and realizing their full potential. Although many models are aligned using techniques such as reinforcement learning from human feedback RLHF, they are still vulnerable to jailbreakin...

The vulnerability of the Firewall Service component in D-Link’s router microprogramming systems, DIR-618 and DIR-605L, allows a hacker to execute arbitrary code.

The vulnerability of the Firewall Service component in D-Link’s DIR-618 and DIR-605L router microprogramming systems is related to access control errors. Exploiting this vulnerability allows a remote attacker to execute arbitrary code...

Palo Alto Networks PAN-OS 10.1.x < 10.1.14-h14 / 10.2.x < 10.2.11 / 11.0.x < 11.0.7 / 11.1.x < 11.1.5 Vulnerability

The version of Palo Alto Networks PAN-OS running on the remote host is 10.1.x prior to 10.1.14-h14, 10.2.x prior to 10.2.11, 11.0.x prior to 11.0.7, or 11.1.x prior to 11.1.5. It is, therefore, affected by a vulnerability. Using the AES-128-CCM algorithm for IPSec on certain Palo Alto Networks...

The vulnerability of the traceroute utility in the microprogramming system of the RUGGEDCOM ROX routing and switching platform for models MX (MX5000, MX5000RE) and RX (RX1400, RX1501, RX1510, RX1511, RX1512, RX1524, RX1536, and RX5000) allows a hacker to execute arbitrary code.

The vulnerability of the traceroute utility in the microprogramming-based routing and switching platform RUGGEDCOM ROX for series MX MX5000, MX5000RE and RX RX1400, RX1501, RX1510, RX1511, RX1512, RX1524, RX1536, and RX5000 lies in the absence of a mechanism to verify input data on the server sid...

CVE-2025-31929

A vulnerability has been identified in IEC 1Ph 7.4kW Child socket 8EM1310-2EH04-0GA0 All versions, IEC 1Ph 7.4kW Child socket/ shutter 8EM1310-2EN04-0GA0 All versions, IEC 1Ph 7.4kW Parent cable 7m 8EM1310-2EJ04-3GA1 All versions, IEC 1Ph 7.4kW Parent cable 7m incl. SIM 8EM1310-2EJ04-3GA2 All...

Gaussian Shading++: Rethinking the Realistic Deployment Challenge of Performance-Lossless Image Watermark for Diffusion Models

Ethical concerns surrounding copyright protection and inappropriate content generation pose challenges for the practical implementation of diffusion models. One effective solution involves watermarking the generated images. Existing methods primarily focus on ensuring that watermark embedding doe...

Red Teaming the Mind of the Machine: a Systematic Evaluation of Prompt Injection and Jailbreak Vulnerabilities in LLMs

Large Language Models LLMs are increasingly integrated into consumer and enterprise applications. Despite their capabilities, they remain susceptible to adversarial attacks such as prompt injection and jailbreaks that override alignment safeguards. This paper provides a systematic investigation o...

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...

Federated Large Language Models: Feasibility, Robustness, Security and Future Directions

The integration of Large Language Models LLMs and Federated Learning FL presents a promising solution for joint training on distributed data while preserving privacy and addressing data silo issues. However, this emerging field, known as Federated Large Language Models FLLM, faces significant...

LM-Scout: Analyzing the Security of Language Model Integration in Android Apps

Developers are increasingly integrating Language Models LMs into their mobile apps to provide features such as chat-based assistants. To prevent LM misuse, they impose various restrictions, including limits on the number of queries, input length, and allowed topics. However, if the LM integration...

LLM-Based Threat Detection and Prevention Framework for IoT Ecosystems

The increasing complexity and scale of the Internet of Things IoT have made security a critical concern. This paper presents a novel Large Language Model LLM-based framework for comprehensive threat detection and prevention in IoT environments. The system integrates lightweight LLMs fine-tuned on...

LiteLMGuard: Seamless and Lightweight On-Device Prompt Filtering for Safeguarding Small Language Models against Quantization-Induced Risks and Vulnerabilities

The growing adoption of Large Language Models LLMs has influenced the development of their lighter counterparts-Small Language Models SLMs-to enable on-device deployment across smartphones and edge devices. These SLMs offer enhanced privacy, reduced latency, server-free functionality, and improve...

POISONCRAFT: Practical Poisoning of Retrieval-Augmented Generation for Large Language Models

Large language models LLMs have achieved remarkable success in various domains, primarily due to their strong capabilities in reasoning and generating human-like text. Despite their impressive performance, LLMs are susceptible to hallucinations, which can lead to incorrect or misleading outputs...

On the Price of Differential Privacy for Spectral Clustering over Stochastic Block Models

We investigate privacy-preserving spectral clustering for community detection within stochastic block models SBMs. Specifically, we focus on edge differential privacy DP and propose private algorithms for community recovery. Our work explores the fundamental trade-offs between the privacy budget...

System Prompt Poisoning: Persistent Attacks on Large Language Models beyond User Injection

Large language models LLMs have gained widespread adoption across diverse applications due to their impressive generative capabilities. Their plug-and-play nature enables both developers and end users to interact with these models through simple prompts. However, as LLMs become more integrated in...

CVE-2025-30165

A flaw was found in vLLM's multi-node configuration, which is vulnerable to remote code execution due to unsafe deserialization using pickle over a ZeroMQ SUB socket. If the primary vLLM host is compromised, attackers can escalate privileges and execute arbitrary code on connected secondary hosts...

Building Trustworthy Multimodal AI: a Review of Fairness, Transparency, and Ethics in Vision-Language Tasks

Objective: This review explores the trustworthiness of multimodal artificial intelligence AI systems, specifically focusing on vision-language tasks. It addresses critical challenges related to fairness, transparency, and ethical implications in these systems, providing a comparative analysis of...

FedTDP: a Privacy-Preserving and Unified Framework for Trajectory Data Preparation Via Federated Learning

Trajectory data, which capture the movement patterns of people and vehicles over time and space, are crucial for applications like traffic optimization and urban planning. However, issues such as noise and incompleteness often compromise data quality, leading to inaccurate trajectory analyses and...

MTL-UE: Learning to Learn Nothing for Multi-Task Learning

Most existing unlearnable strategies focus on preventing unauthorized users from training single-task learning STL models with personal data. Nevertheless, the paradigm has recently shifted towards multi-task data and multi-task learning MTL, targeting generalist and foundation models that can...

CVE-2025-20137

A vulnerability in the access control list ACL programming of Cisco IOS Software that is running on Cisco Catalyst 1000 Switches and Cisco Catalyst 2960L Switches could allow an unauthenticated, remote attacker to bypass a configured ACL. This vulnerability is due to the use of both an IPv4 ACL a...