Attractive Metadata Attack: Inducing LLM Agents to Invoke Malicious Tools

Large language model LLM agents have demonstrated remarkable capabilities in complex reasoning and decision-making by leveraging external tools. However, this tool-centric paradigm introduces a previously underexplored attack surface: adversaries can manipulate tool metadata -- such as names,...

Secure MmWave Beamforming with Proactive-ISAC Defense against Beam-Stealing Attacks

Millimeter-wave mmWave communication systems face increasing susceptibility to advanced beam-stealing attacks, posing a significant physical layer security threat. This paper introduces a novel framework employing an advanced Deep Reinforcement Learning DRL agent for proactive and adaptive defens...

DINA: a Dual Defense Framework against Internal Noise and External Attacks in Natural Language Processing

As large language models LLMs and generative AI become increasingly integrated into customer service and moderation applications, adversarial threats emerge from both external manipulations and internal label corruption. In this work, we identify and systematically address these dual adversarial...

Coward: toward Practical Proactive Federated Backdoor Defense Via Collision-Based Watermark

Backdoor detection is currently the mainstream defense against backdoor attacks in federated learning FL, where malicious clients upload poisoned updates that compromise the global model and undermine the reliability of FL deployments. Existing backdoor detection techniques fall into two...

Leveraging Machine Learning for Botnet Attack Detection in Edge-Computing Assisted IoT Networks

The increase of IoT devices, driven by advancements in hardware technologies, has led to widespread deployment in large-scale networks that process massive amounts of data daily. However, the reliance on Edge Computing to manage these devices has introduced significant security vulnerabilities, a...

CVE-2025-53399

In Sipwise rtpengine before 13.4.1.1, an origin-validation error in the endpoint-learning logic of the media-relay core allows remote attackers to inject or intercept RTP/SRTP media streams via RTP packets except when the relay is configured for strict source and learning disabled. Version 13.4.1...

DEBIAN-CVE-2025-53399

In Sipwise rtpengine before 13.4.1.1, an origin-validation error in the endpoint-learning logic of the media-relay core allows remote attackers to inject or intercept RTP/SRTP media streams via RTP packets except when the relay is configured for strict source and learning disabled. Version 13.4.1...

CVE-2025-53399

In Sipwise rtpengine before 13.4.1.1, an origin-validation error in the endpoint-learning logic of the media-relay core allows remote attackers to inject or intercept RTP/SRTP media streams via RTP packets except when the relay is configured for strict source and learning disabled. Version 13.4.1...

rtpengine Access Control Vulnerability

rtpengine is a media proxy software from Sipwise Open Source. An access control error vulnerability exists in rtpengine versions prior to 13.4.1.1, which stems from a source validation error in the endpoint learning logic that could lead to the injection or interception of RTP/SRTP media streams...

CVE-2025-53399: Origin Validation Error

In Sipwise rtpengine before 13.4.1.1, an origin-validation error in the endpoint-learning logic of the media-relay core allows remote attackers to inject or intercept RTP/SRTP media streams via RTP packets except when the relay is configured for strict source and learning disabled. Version 13.4.1...

CVE-2025-53399

In Sipwise rtpengine before 13.4.1.1, an origin-validation error in the endpoint-learning logic of the media-relay core allows remote attackers to inject or intercept RTP/SRTP media streams via RTP packets except when the relay is configured for strict source and learning disabled. Version 13.4.1...

CVE-2025-54430

dedupe is a python library that uses machine learning to perform fuzzy matching, deduplication and entity resolution quickly on structured data. Before commit 3f61e79, a critical severity vulnerability has been identified within the .github/workflows/benchmark-bot.yml workflow, where a issuecomme...

Empirical Evaluation of Concept Drift in ML-Based Android Malware Detection

Despite outstanding results, machine learning-based Android malware detection models struggle with concept drift, where rapidly evolving malware characteristics degrade model effectiveness. This study examines the impact of concept drift on Android malware detection, evaluating two datasets and...

Breaking Obfuscation: Cluster-Aware Graph with LLM-Aided Recovery for Malicious JavaScript Detection

With the rapid expansion of web-based applications and cloud services, malicious JavaScript code continues to pose significant threats to user privacy, system integrity, and enterprise security. But, detecting such threats remains challenging due to sophisticated code obfuscation techniques and...

PT-2025-31382 · Dedupe · Dedupe

Name of the Vulnerable Software and Affected Versions: dedupe versions prior to commit 3f61e79 Description: dedupe is a Python library used for fuzzy matching, deduplication, and entity resolution on structured data. A critical severity issue exists in the .github/workflows/benchmark-bot.yml...

BentoML SSRF Vulnerability in File Upload Processing

Description There's an SSRF in the file upload processing system that allows remote attackers to make arbitrary HTTP requests from the server without authentication. The vulnerability exists in the serialization/deserialization handlers for multipart form data and JSON requests, which automatical...

Programmable Data Planes for Network Security

The emergence of programmable data planes, and particularly switches supporting the P4 language, has transformed network security by enabling customized, line-rate packet processing. These switches, originally intended for flexible forwarding, now play a broader role: detecting and mitigating...

Secure Tug-Of-War (SecTOW): Iterative Defense-Attack Training with Reinforcement Learning for Multimodal Model Security

The rapid advancement of multimodal large language models MLLMs has led to breakthroughs in various applications, yet their security remains a critical challenge. One pressing issue involves unsafe image-query pairs--jailbreak inputs specifically designed to bypass security constraints and elicit...

GUARD-CAN: Graph-Understanding and Recurrent Architecture for CAN Anomaly Detection

Modern in-vehicle networks face various cyber threats due to the lack of encryption and authentication in the Controller Area Network CAN. To address this security issue, this paper presents GUARD-CAN, an anomaly detection framework that combines graph-based representation learning with time-seri...

Next-Generation Quantum Neural Networks: Enhancing Efficiency, Security, and Privacy

This paper provides an integrated perspective on addressing key challenges in developing reliable and secure Quantum Neural Networks QNNs in the Noisy Intermediate-Scale Quantum NISQ era. In this paper, we present an integrated framework that leverages and combines existing approaches to enhance...