MM-AttacKG: a Multimodal Approach to Attack Graph Construction with Large Language Models

Cyber Threat Intelligence CTI parsing aims to extract key threat information from massive data, transform it into actionable intelligence, enhance threat detection and defense efficiency, including attack graph construction, intelligence fusion and indicator extraction. Among these research topic...

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...

Five Uncomfortable Truths About LLMs in Production

Many tech professionals see integrating large language models LLMs as a simple process -just connect an API and let it run. At Wallarm, our experience has proved otherwise. Through rigorous testing and iteration, our engineering team uncovered several critical insights about deploying LLMs secure...

SecureFed: a Two-Phase Framework for Detecting Malicious Clients in Federated Learning

Federated Learning FL protects data privacy while providing a decentralized method for training models. However, because of the distributed schema, it is susceptible to adversarial clients that could alter results or sabotage model performance. This study presents SecureFed, a two-phase FL...

Exploring Traffic Simulation and Cybersecurity Strategies Using Large Language Models

Intelligent Transportation Systems ITS are increasingly vulnerable to sophisticated cyberattacks due to their complex, interconnected nature. Ensuring the cybersecurity of these systems is paramount to maintaining road safety and minimizing traffic disruptions. This study presents a novel...

The Hitchhiker'S Guide to Efficient, End-To-End, and Tight DP Auditing

This paper systematizes research on auditing Differential Privacy DP techniques, aiming to identify key insights into the current state of the art and open challenges. First, we introduce a comprehensive framework for reviewing work in the field and establish three cross-contextual desiderata tha...

Differentiation-Based Extraction of Proprietary Data from Fine-Tuned LLMs

The increasing demand for domain-specific and human-aligned Large Language Models LLMs has led to the widespread adoption of Supervised Fine-Tuning SFT techniques. SFT datasets often comprise valuable instruction-response pairs, making them highly valuable targets for potential extraction. This...

WormGPT Makes a Comeback Using Jailbroken Grok and Mixtral Models

Cato CTRL uncovers new WormGPT variants on Telegram powered by jailbroken Grok and Mixtral. Learn how cybercriminals jailbreak top LLMs for uncensored, illegal activities in this latest threat research...

Dynamic Risk Assessments for Offensive Cybersecurity Agents

Foundation models are increasingly becoming better autonomous programmers, raising the prospect that they could also automate dangerous offensive cyber-operations. Current frontier model audits probe the cybersecurity risks of such agents, but most fail to account for the degrees of freedom...

A Nested Watermark for Large Language Models

The rapid advancement of large language models LLMs has raised concerns regarding their potential misuse, particularly in generating fake news and misinformation. To address these risks, watermarking techniques for autoregressive language models have emerged as a promising means for detecting...

SHADE-Arena: Evaluating Sabotage and Monitoring in LLM Agents

As Large Language Models LLMs are increasingly deployed as autonomous agents in complex and long horizon settings, it is critical to evaluate their ability to sabotage users by pursuing hidden objectives. We study the ability of frontier LLMs to evade monitoring and achieve harmful hidden goals...

Specification and Evaluation of Multi-Agent LLM Systems -- Prototype and Cybersecurity Applications

Recent advancements in LLMs indicate potential for novel applications, e.g., through reasoning capabilities in the latest OpenAI and DeepSeek models. For applying these models in specific domains beyond text generation, LLM-based multi-agent approaches can be utilized that solve complex tasks by...

Theoretically Unmasking Inference Attacks against LDP-Protected Clients in Federated Vision Models

Federated Learning enables collaborative learning among clients via a coordinating server while avoiding direct data sharing, offering a perceived solution to preserve privacy. However, recent studies on Membership Inference Attacks MIAs have challenged this notion, showing high success rates...

CVE-2025-6030

Use of fixed learning codes, one code to lock the car and the other code to unlock it, in the Key Fob Transmitter in Cyclone Matrix TRF Smart Keyless Entry System, which allows a replay attack. Research was completed on the 2024 KIA Soluto. Attack confirmed on other KIA Models in Ecuador...

The Safety Reminder: a Soft Prompt to Reactivate Delayed Safety Awareness in Vision-Language Models

As Vision-Language Models VLMs demonstrate increasing capabilities across real-world applications such as code generation and chatbot assistance, ensuring their safety has become paramount. Unlike traditional Large Language Models LLMs, VLMs face unique vulnerabilities due to their multimodal...

Step-By-Step Reasoning Attack: Revealing 'Erased' Knowledge in Large Language Models

Whitepaper called Step-By-Step Reasoning Attack: Revealing 'Erased' Knowledge In Large Language Models...

CVE-2025-6030

Use of fixed learning codes, one code to lock the car and the other code to unlock it, in the Key Fob Transmitter in Cyclone Matrix TRF Smart Keyless Entry System, which allows a replay attack. Research was completed on the 2024 KIA Soluto. Attack confirmed on other KIA Models in Ecuador...

CVE-2025-6030 Autoeastern Smart Keyless Entry System Replay Attack

Use of fixed learning codes, one code to lock the car and the other code to unlock it, in the Key Fob Transmitter in Cyclone Matrix TRF Smart Keyless Entry System, which allows a replay attack. Research was completed on the 2024 KIA Soluto. Attack confirmed on other KIA Models in Ecuador...

CVE-2025-6030 Autoeastern Smart Keyless Entry System Replay Attack

Use of fixed learning codes, one code to lock the car and the other code to unlock it, in the Key Fob Transmitter in Cyclone Matrix TRF Smart Keyless Entry System, which allows a replay attack. Research was completed on the 2024 KIA Soluto. Attack confirmed on other KIA Models in Ecuador...

CVE-2025-6030

CVE-2025-6030 concerns the Cyclone Matrix TRF Smart Keyless Entry System’s Key Fob Transmitter, where the use of fixed learning codes enables a replay attack. The issue affects Cyclone Matrix TRF-based keyless systems and was demonstrated on a 2024 Kia Soluto, with reports of attacks on other Kia...