Rethinking On-Device LLM Reasoning: Why Analogical Mapping Outperforms Abstract Thinking for IoT DDoS Detection

The rapid expansion of IoT deployments has intensified cybersecurity threats, notably Distributed Denial of Service DDoS attacks, characterized by increasingly sophisticated patterns. Leveraging Generative AI through On-Device Large Language Models ODLLMs provides a viable solution for real-time...

Many Hands Make Light Work: An LLM-Based Multi-Agent System for Detecting Malicious PyPI Packages

Malicious code in open-source repositories such as PyPI poses a growing threat to software supply chains. Traditional rule-based tools often overlook the semantic patterns in source code that are crucial for identifying adversarial components. Large language models LLMs show promise for software...

KryptoPilot: An Open-World Knowledge-Augmented LLM Agent for Automated Cryptographic Exploitation

Capture-the-Flag CTF competitions play a central role in modern cybersecurity as a platform for training practitioners and evaluating offensive and defensive techniques derived from real-world vulnerabilities. Despite recent advances in large language models LLMs, existing LLM-based agents remain...

Cracking IoT Security: Can LLMs Outsmart Static Analysis Tools?

Smart home IoT platforms such as openHAB rely on Trigger Action Condition TAC rules to automate device behavior, but the interplay among these rules can give rise to interaction threats, unintended or unsafe behaviors emerging from implicit dependencies, conflicting triggers, or overlapping...

Agentic AI for Cyber Resilience: A New Security Paradigm and Its System-Theoretic Foundations

Cybersecurity is being fundamentally reshaped by foundation-model-based artificial intelligence. Large language models now enable autonomous planning, tool orchestration, and strategic adaptation at scale, challenging security architectures built on static rules, perimeter defenses, and...

Assessing the Software Security Comprehension of Large Language Models

Large language models LLMs are increasingly used in software development, but their level of software security expertise remains unclear. This work systematically evaluates the security comprehension of five leading LLMs: GPT-4o-Mini, GPT-5-Mini, Gemini-2.5-Flash, Llama-3.1, and Qwen-2.5, using...

AgenticCyber: A GenAI-Powered Multi-Agent System for Multimodal Threat Detection and Adaptive Response in Cybersecurity

The increasing complexity of cyber threats in distributed environments demands advanced frameworks for real-time detection and response across multimodal data streams. This paper introduces AgenticCyber, a generative AI powered multi-agent system that orchestrates specialized agents to monitor...

COGNITION: From Evaluation to Defense against Multimodal LLM CAPTCHA Solvers

This paper studies how multimodal large language models MLLMs undermine the security guarantees of visual CAPTCHA. We identify the attack surface where an adversary can cheaply automate CAPTCHA solving using off-the-shelf models. We evaluate 7 leading commercial and open-source MLLMs across 18...

Red Teaming Large Reasoning Models

Large Reasoning Models LRMs have emerged as a powerful advancement in multi-step reasoning tasks, offering enhanced transparency and logical consistency through explicit chains of thought CoT. However, these models introduce novel safety and reliability risks, such as CoT-hijacking and...

Think Fast: Real-Time IoT Intrusion Reasoning Using IDS and LLMs at the Edge Gateway

As the number of connected IoT devices continues to grow, securing these systems against cyber threats remains a major challenge, especially in environments with limited computational and energy resources. This paper presents an edge-centric Intrusion Detection System IDS framework that integrate...

EASE: Practical and Efficient Safety Alignment for Small Language Models

Small language models SLMs are increasingly deployed on edge devices, making their safety alignment crucial yet challenging. Current shallow alignment methods that rely on direct refusal of malicious queries fail to provide robust protection, particularly against adversarial jailbreaks. While...

Large Language Models for Explainable Threat Intelligence

As cyber threats continue to grow in complexity, traditional security mechanisms struggle to keep up. Large language models LLMs offer significant potential in cybersecurity due to their advanced capabilities in text processing and generation. This paper explores the use of LLMs with...

Aether - Adaptive Exploit and Threat Hunting Engine for EVM-based Repositories

Aether is a Python-based framework for analyzing Solidity smart contracts, generating vulnerability findings, producing Foundry-based proof-of-concept PoC tests, and optionally validating those tests on mainnet forks. It combines static analysis, prompt-driven LLM analysis, and AI-ensemble...

Adapting Large Language Models to Emerging Cybersecurity Using Retrieval Augmented Generation

Security applications are increasingly relying on large language models LLMs for cyber threat detection; however, their opaque reasoning often limits trust, particularly in decisions that require domain-specific cybersecurity knowledge. Because security threats evolve rapidly, LLMs must not only...

Breaking Agent Backbones: Evaluating the Security of Backbone LLMs in AI Agents

AI agents powered by large language models LLMs are being deployed at scale, yet we lack a systematic understanding of how the choice of backbone LLM affects agent security. The non-deterministic sequential nature of AI agents complicates security modeling, while the integration of traditional...

The Trojan Example: Jailbreaking LLMs through Template Filling and Unsafety Reasoning

Large Language Models LLMs have advanced rapidly and now encode extensive world knowledge. Despite safety fine-tuning, however, they remain susceptible to adversarial prompts that elicit harmful content. Existing jailbreak techniques fall into two categories: white-box methods e.g., gradient-base...

TITAN: Graph-Executable Reasoning for Cyber Threat Intelligence

TITAN Threat Intelligence Through Automated Navigation is a framework that connects natural-language cyber threat queries with executable reasoning over a structured knowledge graph. It integrates a path planner model, which predicts logical relation chains from text, and a graph executor that...

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...

Real-VulLLM: An LLM Based Assessment Framework in the Wild

Artificial Intelligence AI and more specifically Large Language Models LLMs have demonstrated exceptional progress in multiple areas including software engineering, however, their capability for vulnerability detection in the wild scenario and its corresponding reasoning remains underexplored...

EUVD-2025-31171

Malicious code in bioql PyPI...