User-Centric Phishing Detection: A RAG and LLM-Based Approach

The escalating sophistication of phishing emails necessitates a shift beyond traditional rule-based and conventional machine-learning-based detectors. Although large language models LLMs offer strong natural language understanding, using them as standalone classifiers often yields elevated...

Lightweight LLMs for Network Attack Detection in IoT Networks

The rapid growth of Internet of Things IoT devices has increased the scale and diversity of cyberattacks, exposing limitations in traditional intrusion detection systems. Classical machine learning ML models such as Random Forest and Support Vector Machine perform well on known attacks but requir...

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...

An Empirical Evaluation of LLM-Based Approaches for Code Vulnerability Detection: RAG, SFT, and Dual-Agent Systems

The rapid advancement of Large Language Models LLMs presents new opportunities for automated software vulnerability detection, a crucial task in securing modern codebases. This paper presents a comparative study on the effectiveness of LLM-based techniques for detecting software vulnerabilities...

Exploring the Security Threats of Retriever Backdoors in Retrieval-Augmented Code Generation

Retrieval-Augmented Code Generation RACG is increasingly adopted to enhance Large Language Models for software development, yet its security implications remain dangerously underexplored. This paper conducts the first systematic exploration of a critical and stealthy threat: backdoor attacks...

LLM-Driven Feature-Level Adversarial Attacks on Android Malware Detectors

The rapid growth in both the scale and complexity of Android malware has driven the widespread adoption of machine learning ML techniques for scalable and accurate malware detection. Despite their effectiveness, these models remain vulnerable to adversarial attacks that introduce carefully crafte...

ReGAIN: Retrieval-Grounded AI Framework for Network Traffic Analysis

Modern networks generate vast, heterogeneous traffic that must be continuously analyzed for security and performance. Traditional network traffic analysis systems, whether rule-based or machine learning-driven, often suffer from high false positives and lack interpretability, limiting analyst...

I Built a RAG Bot to Decode Airline Bureaucracy (So You Don't Have To)

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Agentic AI for 6G: A New Paradigm for Autonomous RAN Security Compliance

Agentic AI systems are emerging as powerful tools for automating complex, multi-step tasks across various industries. One such industry is telecommunications, where the growing complexity of next-generation radio access networks RANs opens up numerous opportunities for applying these systems...

Llama-Based Source Code Vulnerability Detection: Prompt Engineering Vs Fine Tuning

The significant increase in software production, driven by the acceleration of development cycles over the past two decades, has led to a steady rise in software vulnerabilities, as shown by statistics published yearly by the CVE program. The automation of the source code vulnerability detection...

Retrieval-Augmented Few-Shot Prompting Versus Fine-Tuning for Code Vulnerability Detection

Few-shot prompting has emerged as a practical alternative to fine-tuning for leveraging the capabilities of large language models LLMs in specialized tasks. However, its effectiveness depends heavily on the selection and quality of in-context examples, particularly in complex domains. In this wor...

From Reviewers' Lens: Understanding Bug Bounty Report Invalid Reasons with LLMs

Bug bounty platforms e.g., HackerOne, BugCrowd leverage crowd-sourced vulnerability discovery to improve continuous coverage, reduce the cost of discovery, and serve as an integral complement to internal red teams. With the rise of AI-generated bug reports, little work exists to help bug hunters...

ReVul-CoT: Towards Effective Software Vulnerability Assessment with Retrieval-Augmented Generation and Chain-Of-Thought Prompting

Context: Software Vulnerability Assessment SVA plays a vital role in evaluating and ranking vulnerabilities in software systems to ensure their security and reliability. Objective: Although Large Language Models LLMs have recently shown remarkable potential in SVA, they still face two major...

Securing AI Agents against Prompt Injection Attacks

Retrieval-augmented generation RAG systems have become widely used for enhancing large language model capabilities, but they introduce significant security vulnerabilities through prompt injection attacks. We present a comprehensive benchmark for evaluating prompt injection risks in RAG-enabled A...

MalRAG: A Retrieval-Augmented LLM Framework for Open-Set Malicious Traffic Identification

Fine-grained identification of IDS-flagged suspicious traffic is crucial in cybersecurity. In practice, cyber threats evolve continuously, making the discovery of novel malicious traffic a critical necessity as well as the identification of known classes. Recent studies have advanced this goal wi...

RAG-Targeted Adversarial Attack on LLM-Based Threat Detection and Mitigation Framework

The rapid expansion of the Internet of Things IoT is reshaping communication and operational practices across industries, but it also broadens the attack surface and increases susceptibility to security breaches. Artificial Intelligence has become a valuable solution in securing IoT networks, wit...

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...

Create Self-Improving AI Agents Using Spring AI Recursive Advisors

The Spring AI ChatClient offers a fluent API for communicating with an AI model. The fluent API provides methods for building the constituent parts of a prompt that gets passed to the AI model as input. Advisors are a key part of the fluent API that intercept, modify, and enhance AI-driven...

LLM-Based Multi-Class Attack Analysis and Mitigation Framework in IoT/IIoT Networks

The Internet of Things has expanded rapidly, transforming communication and operations across industries but also increasing the attack surface and security breaches. Artificial Intelligence plays a key role in securing IoT, enabling attack detection, attack behavior analysis, and mitigation...

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...