HarmNet: A Framework for Adaptive Multi-Turn Jailbreak Attacks on Large Language Models

Large Language Models LLMs remain vulnerable to multi-turn jailbreak attacks. We introduce HarmNet, a modular framework comprising ThoughtNet, a hierarchical semantic network; a feedback-driven Simulator for iterative query refinement; and a Network Traverser for real-time adaptive attack...

CLASP: Cost-Optimized LLM-Based Agentic System for Phishing Detection

Phishing websites remain a significant cybersecurity threat, necessitating accurate and cost-effective detection mechanisms. In this paper, we present CLASP, a novel system that effectively identifies phishing websites by leveraging multiple intelligent agents, built using large language models...

Prompting the Priorities: A First Look at Evaluating LLMs for Vulnerability Triage and Prioritization

Security analysts face increasing pressure to triage large and complex vulnerability backlogs. Large Language Models LLMs offer a potential aid by automating parts of the interpretation process. We evaluate four models ChatGPT, Claude, Gemini, and DeepSeek across twelve prompting techniques to...

Can Transformer Memory Be Corrupted? Investigating Cache-Side Vulnerabilities in Large Language Models

Even when prompts and parameters are secured, transformer language models remain vulnerable because their key-value KV cache during inference constitutes an overlooked attack surface. This paper introduces Malicious Token Injection MTI, a modular framework that systematically perturbs cached key...

Structuring Security: A Survey of Cybersecurity Ontologies, Semantic Log Processing, and LLMs Application

This survey investigates how ontologies, semantic log processing, and Large Language Models LLMs enhance cybersecurity. Ontologies structure domain knowledge, enabling interoperability, data integration, and advanced threat analysis. Security logs, though critical, are often unstructured and...

SoK: Taxonomy and Evaluation of Prompt Security in Large Language Models

Large Language Models LLMs have rapidly become integral to real-world applications, powering services across diverse sectors. However, their widespread deployment has exposed critical security risks, particularly through jailbreak prompts that can bypass model alignment and induce harmful outputs...

MalCVE: Malware Detection and CVE Association Using Large Language Models

Malicious software attacks are having an increasingly significant economic impact. Commercial malware detection software can be costly, and tools that attribute malware to the specific software vulnerabilities it exploits are largely lacking. Understanding the connection between malware and the...

When Intelligence Fails: An Empirical Study on Why LLMs Struggle with Password Cracking

The remarkable capabilities of Large Language Models LLMs in natural language understanding and generation have sparked interest in their potential for cybersecurity applications, including password guessing. In this study, we conduct an empirical investigation into the efficacy of pre-trained LL...

Bringing the Power of Agentic AI for Identity Risk, Adaptive Threat Prioritization, and Exposure Exploitability Validation

Qualys Enterprise TruRisk Management ETM extends the power of risk operations with agentic AI — Introducing ETM Identity, TruLens for industry-based threat prioritization, and TruConfirm exposure exploitability validation to accelerate your remediation. Every year at our yearly conference, now...

Toward Cybersecurity-Expert Small Language Models

Large language models LLMs are transforming everyday applications, yet deployment in cybersecurity lags due to a lack of high-quality, domain-specific models and training datasets. To address this gap, we present CyberPal 2.0, a family of cybersecurity-expert small language models SLMs ranging fr...

CTIArena: Benchmarking LLM Knowledge and Reasoning across Heterogeneous Cyber Threat Intelligence

Cyber threat intelligence CTI is central to modern cybersecurity, providing critical insights for detecting and mitigating evolving threats. With the natural language understanding and reasoning capabilities of large language models LLMs, there is increasing interest in applying them to CTI, whic...

A Systematic Study on Generating Web Vulnerability Proof-Of-Concepts Using Large Language Models

Recent advances in Large Language Models LLMs have brought remarkable progress in code understanding and reasoning, creating new opportunities and raising new concerns for software security. Among many downstream tasks, generating Proof-of-Concept PoC exploits plays a central role in vulnerabilit...

EUVD-2025-33778

Cherry Studio is a desktop client that supports for multiple LLM providers. Cherry Studio registers a custom protocol called cherrystudio://. When handling the MCP installation URL, it parses the base64-encoded configuration data and directly executes the command within it. In the files...

Distilling Lightweight Language Models for C/C++ Vulnerabilities

The increasing complexity of modern software systems exacerbates the prevalence of security vulnerabilities, posing risks of severe breaches and substantial economic loss. Consequently, robust code vulnerability detection is essential for software security. While Large Language Models LLMs have...

RedTWIZ: Diverse LLM Red Teaming Via Adaptive Attack Planning

This paper presents the vision, scientific contributions, and technical details of RedTWIZ: an adaptive and diverse multi-turn red teaming framework, to audit the robustness of Large Language Models LLMs in AI-assisted software development. Our work is driven by three major research streams: 1...

A Survey on Agentic Security: Applications, Threats and Defenses

The rapid shift from passive LLMs to autonomous LLM-agents marks a new paradigm in cybersecurity. While these agents can act as powerful tools for both offensive and defensive operations, the very agentic context introduces a new class of inherent security risks. In this work we present the first...

Leveraging Large Language Models for Cybersecurity Risk Assessment -- a Case from Forestry Cyber-Physical Systems

In safety-critical software systems, cybersecurity activities become essential, with risk assessment being one of the most critical. In many software teams, cybersecurity experts are either entirely absent or represented by only a small number of specialists. As a result, the workload for these...

P2P: A Poison-To-Poison Remedy for Reliable Backdoor Defense in LLMs

During fine-tuning, large language models LLMs are increasingly vulnerable to data-poisoning backdoor attacks, which compromise their reliability and trustworthiness. However, existing defense strategies suffer from limited generalization: they only work on specific attack types or task settings...

Imperceptible Jailbreaking against Large Language Models

Jailbreaking attacks on the vision modality typically rely on imperceptible adversarial perturbations, whereas attacks on the textual modality are generally assumed to require visible modifications e.g., non-semantic suffixes. In this paper, we introduce imperceptible jailbreaks that exploit a...

Selecting Cybersecurity Requirements: Effects of LLM Use and Professional Software Development Experience

This study investigates how access to Large Language Models LLMs and varying levels of professional software development experience affect the prioritization of cybersecurity requirements for web applications. Twenty-three postgraduate students participated in a research study to prioritize...