LLMalMorph: on the Feasibility of Generating Variant Malware Using Large-Language-Models

Large Language Models LLMs have transformed software development and automated code generation. Motivated by these advancements, this paper explores the feasibility of LLMs in modifying malware source code to generate variants. We introduce LLMalMorph, a semi-automated framework that leverages...

Favicon Trojans: Executable Steganography Via Ico Alpha Channel Exploitation

This paper presents a novel method of executable steganography using the alpha transparency layer of ICO image files to embed and deliver self-decompressing JavaScript payloads within web browsers. By targeting the least significant bit LSB of non-transparent alpha layer image values, the propose...

DATABench: Evaluating Dataset Auditing in Deep Learning from an Adversarial Perspective

The widespread application of Deep Learning across diverse domains hinges critically on the quality and composition of training datasets. However, the common lack of disclosure regarding their usage raises significant privacy and copyright concerns. Dataset auditing techniques, which aim to...

False Alarms, Real Damage: Adversarial Attacks Using LLM-Based Models on Text-Based Cyber Threat Intelligence Systems

Cyber Threat Intelligence CTI has emerged as a vital complementary approach that operates in the early phases of the cyber threat lifecycle. CTI involves collecting, processing, and analyzing threat data to provide a more accurate and rapid understanding of cyber threats. Due to the large volume ...

Microsoft, PayPal, DocuSign, and Geek Squad faked in callback phishing scams

Microsoft, DocuSign, Adobe, McAfee, NortonLifeLock, PayPal, and Best Buy’s Geek Squad are being impersonated online through malicious emails that contain fake telephone support numbers and dangerous QR codes that can ensnare victims into phishing scams. The brands and their products are frequentl...

OneClik: A ClickOnce-Based APT Campaign Targeting Energy, Oil and Gas Infrastructure

OneClik: A ClickOnce-Based Red Team Campaign Simulating APT Tactics in Energy Infrastructure By Nico Paulo Yturriaga and Pham Duy Phuc · Updated : June 30, 2025 The Trellix Advanced Research Center previously uncovered what appeared to be a sophisticated APT malware campaign, which we dubbed...

New Stealthy Remcos Malware Campaigns Target Businesses and Schools

Forcepoint's X-Labs reveals Remcos malware using new tricky phishing emails from compromised accounts and advanced evasion techniques like…...

OneClik: A ClickOnce-Based APT Campaign Targeting Energy, Oil and Gas Infrastructure

OneClik: A ClickOnce-Based APT Campaign Targeting Energy, Oil and Gas Infrastructure By Nico Paulo Yturriaga and Pham Duy Phuc · June 24, 2025 The Trellix Advanced Research Center has uncovered a sophisticated APT malware campaign that we’ve dubbed OneClik. It specifically targets the energy, oil...

Enhancing Security in LLM Applications: a Performance Evaluation of Early Detection Systems

Prompt injection threatens novel applications that emerge from adapting LLMs for various user tasks. The newly developed LLM-based software applications become more ubiquitous and diverse. However, the threat of prompt injection attacks undermines the security of these systems as the mitigation a...

InfoFlood: Jailbreaking Large Language Models with Information Overload

Large Language Models LLMs have demonstrated remarkable capabilities across various domains. However, their potential to generate harmful responses has raised significant societal and regulatory concerns, especially when manipulated by adversarial techniques known as "jailbreak" attacks. Existing...

Assessing the Resilience of Automotive Intrusion Detection Systems to Adversarial Manipulation

The security of modern vehicles has become increasingly important, with the controller area network CAN bus serving as a critical communication backbone for various Electronic Control Units ECUs. The absence of robust security measures in CAN, coupled with the increasing connectivity of vehicles,...

GenBreak: Red Teaming Text-To-Image Generators Using Large Language Models

Text-to-image T2I models such as Stable Diffusion have advanced rapidly and are now widely used in content creation. However, these models can be misused to generate harmful content, including nudity or violence, posing significant safety risks. While most platforms employ content moderation...

GradEscape: a Gradient-Based Evader against AI-Generated Text Detectors

In this paper, we introduce GradEscape, the first gradient-based evader designed to attack AI-generated text AIGT detectors. GradEscape overcomes the undifferentiable computation problem, caused by the discrete nature of text, by introducing a novel approach to construct weighted embeddings for t...

MalGEN: a Generative Agent Framework for Modeling Malicious Software in Cybersecurity

The dual use nature of Large Language Models LLMs presents a growing challenge in cybersecurity. While LLM enhances automation and reasoning for defenders, they also introduce new risks, particularly their potential to be misused for generating evasive, AI crafted malware. Despite this emerging...

Malicious Package

Overview Colorizator is a malicious package. This package contains payloads with Windows and Linux variants that access and exfiltrate sensitive configuration information, establish remote control / remote access for the attacker, establish persistence and “command and control” C2 mechanisms...

Malicious Package

Overview coloramapkgsw is a malicious package. This package contains payloads with Windows and Linux variants that access and exfiltrate sensitive configuration information, establish remote control / remote access for the attacker, establish persistence and “command and control” C2 mechanisms...

Merge Hijacking: Backdoor Attacks to Model Merging of Large Language Models

Model merging for Large Language Models LLMs directly fuses the parameters of different models finetuned on various tasks, creating a unified model for multi-domain tasks. However, due to potential vulnerabilities in models available on open-source platforms, model merging is susceptible to...

Fooling the Watchers: Breaking AIGC Detectors Via Semantic Prompt Attacks

The rise of text-to-image T2I models has enabled the synthesis of photorealistic human portraits, raising serious concerns about identity misuse and the robustness of AIGC detectors. In this work, we propose an automated adversarial prompt generation framework that leverages a grammar tree...

The Feasibility of Topic-Based Watermarking on Academic Peer Reviews

Large language models LLMs are increasingly integrated into academic workflows, with many conferences and journals permitting their use for tasks such as language refinement and literature summarization. However, their use in peer review remains prohibited due to concerns around confidentiality...

Adapting Novelty Towards Generating Antigens for Antivirus Systems

It is well known that anti-malware scanners depend on malware signatures to identify malware. However, even minor modifications to malware code structure results in a change in the malware signature thus enabling the variant to evade detection by scanners. Therefore, there exists the need for a...