MAD-Spear: a Conformity-Driven Prompt Injection Attack on Multi-Agent Debate Systems

Multi-agent debate MAD systems leverage collaborative interactions among large language models LLMs agents to improve reasoning capabilities. While recent studies have focused on increasing the accuracy and scalability of MAD systems, their security vulnerabilities have received limited attention...

Game Theory Meets LLM and Agentic AI: Reimagining Cybersecurity for the Age of Intelligent Threats

Protecting cyberspace requires not only advanced tools but also a shift in how we reason about threats, trust, and autonomy. Traditional cybersecurity methods rely on manual responses and brittle heuristics. To build proactive and intelligent defense systems, we need integrated theoretical...

SAMEP: a Secure Protocol for Persistent Context Sharing across AI Agents

Current AI agent architectures suffer from ephemeral memory limitations, preventing effective collaboration and knowledge sharing across sessions and agent boundaries. We introduce SAMEP Secure Agent Memory Exchange Protocol, a novel framework that enables persistent, secure, and semantically...

The Rise of Agentic AI: From Chatbots to Web Agents

Disclaimer: This post isn’t our usual security-focused content – today we’re taking a quick detour to explore the fascinating world of AI agents with the focus of AI web agents. Enjoy this educational dive as a warm-up before we get into the juicy details of AI web agents in our follow-up post...

SV-LLM: an Agentic Approach for SoC Security Verification Using Large Language Models

Ensuring the security of complex system-on-chips SoCs designs is a critical imperative, yet traditional verification techniques struggle to keep pace due to significant challenges in automation, scalability, comprehensiveness, and adaptability. The advent of large language models LLMs, with their...

PhishDebate: an LLM-Based Multi-Agent Framework for Phishing Website Detection

Phishing websites continue to pose a significant cybersecurity threat, often leveraging deceptive structures, brand impersonation, and social engineering tactics to evade detection. While recent advances in large language models LLMs have enabled improved phishing detection through contextual...

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...

IP Leakage Attacks Targeting LLM-Based Multi-Agent Systems

The rapid advancement of Large Language Models LLMs has led to the emergence of Multi-Agent Systems MAS to perform complex tasks through collaboration. However, the intricate nature of MAS, including their architecture and agent interactions, raises significant concerns regarding intellectual...

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...

AURA: a Multi-Agent Intelligence Framework for Knowledge-Enhanced Cyber Threat Attribution

Effective attribution of Advanced Persistent Threats APTs increasingly hinges on the ability to correlate behavioral patterns and reason over complex, varied threat intelligence artifacts. We present AURA Attribution Using Retrieval-Augmented Agents, a multi-agent, knowledge-enhanced framework fo...

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...

MARVEL: Multi-Agent RTL Vulnerability Extraction Using Large Language Models

Hardware security verification is a challenging and time-consuming task. For this purpose, design engineers may utilize tools such as formal verification, linters, and functional simulation tests, coupled with analysis and a deep understanding of the hardware design being inspected. Large Languag...

From Static to Adaptive Defense: Federated Multi-Agent Deep Reinforcement Learning-Driven Moving Target Defense against DoS Attacks in UAV Swarm Networks

The proliferation of unmanned aerial vehicle UAV swarms has enabled a wide range of mission-critical applications, but also exposes UAV networks to severe Denial-of-Service DoS threats due to their open wireless environment, dynamic topology, and resource constraints. Traditional static or...

Comprehensive Vulnerability Analysis Is Necessary for Trustworthy LLM-MAS

This paper argues that a comprehensive vulnerability analysis is essential for building trustworthy Large Language Model-based Multi-Agent Systems LLM-MAS. These systems, which consist of multiple LLM-powered agents working collaboratively, are increasingly deployed in high-stakes applications bu...

Langflow Chatbot Detected

This is an informational plugin to inform the user that the scanner has detected a publicly accessible Langflow chatbot on the target application. Langflow is an open-source visual framework for building multi-agent and RAG. This detection is included in the AI and LLM category. No source data...

ATAG: AI-Agent Application Threat Assessment with Attack Graphs

Evaluating the security of multi-agent systems MASs powered by large language models LLMs is challenging, primarily because of the systems' complex internal dynamics and the evolving nature of LLM vulnerabilities. Traditional attack graph AG methods often lack the specific capabilities to model...

A Novel Zero-Trust Identity Framework for Agentic AI: Decentralized Authentication and Fine-Grained Access Control

Traditional Identity and Access Management IAM systems, primarily designed for human users or static machine identities via protocols such as OAuth, OpenID Connect OIDC, and SAML, prove fundamentally inadequate for the dynamic, interdependent, and often ephemeral nature of AI agents operating at...

MultiPhishGuard: an LLM-Based Multi-Agent System for Phishing Email Detection

Phishing email detection faces critical challenges from evolving adversarial tactics and heterogeneous attack patterns. Traditional detection methods, such as rule-based filters and denylists, often struggle to keep pace with these evolving tactics, leading to false negatives and compromised...

PandaGuard: Systematic Evaluation of LLM Safety against Jailbreaking Attacks

Large language models LLMs have achieved remarkable capabilities but remain vulnerable to adversarial prompts known as jailbreaks, which can bypass safety alignment and elicit harmful outputs. Despite growing efforts in LLM safety research, existing evaluations are often fragmented, focused on...

CoTGuard: Using Chain-Of-Thought Triggering for Copyright Protection in Multi-Agent LLM Systems

As large language models LLMs evolve into autonomous agents capable of collaborative reasoning and task execution, multi-agent LLM systems have emerged as a powerful paradigm for solving complex problems. However, these systems pose new challenges for copyright protection, particularly when...