MalLoc: toward Fine-Grained Android Malicious Payload Localization Via LLMs

The rapid evolution of Android malware poses significant challenges to the maintenance and security of mobile applications apps. Traditional detection techniques often struggle to keep pace with emerging malware variants that employ advanced tactics such as code obfuscation and dynamic behavior...

Tricking LLM-Based NPCs into Spilling Secrets

Large Language Models LLMs are increasingly used to generate dynamic dialogue for game NPCs. However, their integration raises new security concerns. In this study, we examine whether adversarial prompt injection can cause LLM-based NPCs to reveal hidden background secrets that are meant to remai...

A Systematic Approach to Predict the Impact of Cybersecurity Vulnerabilities Using LLMs

Vulnerability databases, such as the National Vulnerability Database NVD, offer detailed descriptions of Common Vulnerabilities and Exposures CVEs, but often lack information on their real-world impact, such as the tactics, techniques, and procedures TTPs that adversaries may use to exploit the...

Comprehensive MCP Security Checklist: Protecting Your AI-Powered Infrastructure

With innovation comes risk. As organizations race to build AI-first infrastructure, security is struggling to keep pace. Multi-Agentic Systems – those built on Large Language Models LLMs and Multi-Component Protocols MCP - bring immense potential, but also novel vulnerabilities that traditional...

Towards Scalable and Interpretable Mobile App Risk Analysis Via Large Language Models

Mobile application marketplaces are responsible for vetting apps to identify and mitigate security risks. Current vetting processes are labor-intensive, relying on manual analysis by security professionals aided by semi-automated tools. To address this inefficiency, we propose Mars, a system that...

MoEcho: Exploiting Side-Channel Attacks to Compromise User Privacy in Mixture-Of-Experts LLMs

The transformer architecture has become a cornerstone of modern AI, fueling remarkable progress across applications in natural language processing, computer vision, and multimodal learning. As these models continue to scale explosively for performance, implementation efficiency remains a critical...

Aura-CAPTCHA: a Reinforcement Learning and GAN-Enhanced Multi-Modal CAPTCHA System

Aura-CAPTCHA was developed as a multi-modal CAPTCHA system to address vulnerabilities in traditional methods that are increasingly bypassed by AI technologies, such as Optical Character Recognition OCR and adversarial image processing. The design integrated Generative Adversarial Networks GANs fo...

CCFC: Core and Core-Full-Core Dual-Track Defense for LLM Jailbreak Protection

Jailbreak attacks pose a serious challenge to the safe deployment of large language models LLMs. We introduce CCFC Core & Core-Full-Core, a dual-track, prompt-level defense framework designed to mitigate LLMs' vulnerabilities from prompt injection and structure-aware jailbreak attacks. CCFC...

Enhancing Targeted Adversarial Attacks on Large Vision-Language Models through Intermediate Projector Guidance

Targeted adversarial attacks are essential for proactively identifying security flaws in Vision-Language Models before real-world deployment. However, current methods perturb images to maximize global similarity with the target text or reference image at the encoder level, collapsing rich visual...

Consiglieres in the Shadow: Understanding the Use of Uncensored Large Language Models in Cybercrimes

The advancement of AI technologies, particularly Large Language Models LLMs, has transformed computing while introducing new security and privacy risks. Prior research shows that cybercriminals are increasingly leveraging uncensored LLMs ULLMs as backends for malicious services. Understanding the...

VerilogLAVD: LLM-Aided Rule Generation for Vulnerability Detection in Verilog

Timely detection of hardware vulnerabilities during the early design stage is critical for reducing remediation costs. Existing early detection techniques often require specialized security expertise, limiting their usability. Recent efforts have explored the use of large language models LLMs for...

Unlearning at Scale: Implementing the Right to Be Forgotten in Large Language Models

We study the right to be forgotten GDPR Art. 17 for large language models and frame unlearning as a reproducible systems problem. Our approach treats training as a deterministic program and logs a minimal per-microbatch record ordered ID hash, RNG seed, learning-rate value, optimizer-step counter...

CryptoScope: Utilizing Large Language Models for Automated Cryptographic Logic Vulnerability Detection

Cryptographic algorithms are fundamental to modern security, yet their implementations frequently harbor subtle logic flaws that are hard to detect. We introduce CryptoScope, a novel framework for automated cryptographic vulnerability detection powered by Large Language Models LLMs. CryptoScope...

REFN: a Reinforcement-Learning-From-Network Framework against 1-Day/N-Day Exploitations

The exploitation of 1 day or n day vulnerabilities poses severe threats to networked devices due to massive deployment scales and delayed patching average Mean Time To Patch exceeds 60 days. Existing defenses, including host based patching and network based filtering, are inadequate due to limite...

Can Multi-Modal (Reasoning) LLMs Detect Document Manipulation?

Document fraud poses a significant threat to industries reliant on secure and verifiable documentation, necessitating robust detection mechanisms. This study investigates the efficacy of state-of-the-art multi-modal large language models LLMs-including OpenAI O1, OpenAI 4o, Gemini Flash thinking,...

Advancing Autonomous Incident Response: Leveraging LLMs and Cyber Threat Intelligence

Effective incident response IR is critical for mitigating cyber threats, yet security teams are overwhelmed by alert fatigue, high false-positive rates, and the vast volume of unstructured Cyber Threat Intelligence CTI documents. While CTI holds immense potential for enriching security operations...

Enhancing GraphQL Security by Detecting Malicious Queries Using Large Language Models, Sentence Transformers, and Convolutional Neural Networks

GraphQL's flexibility, while beneficial for efficient data fetching, introduces unique security vulnerabilities that traditional API security mechanisms often fail to address. Malicious GraphQL queries can exploit the language's dynamic nature, leading to denial-of-service attacks, data...

Amazon Nova AI Challenge -- Trusted AI: Advancing Secure, AI-Assisted Software Development

AI systems for software development are rapidly gaining prominence, yet significant challenges remain in ensuring their safety. To address this, Amazon launched the Trusted AI track of the Amazon Nova AI Challenge, a global competition among 10 university teams to drive advances in secure AI. In...

PRvL: Quantifying the Capabilities and Risks of Large Language Models for PII Redaction

Redacting Personally Identifiable Information PII from unstructured text is critical for ensuring data privacy in regulated domains. While earlier approaches have relied on rule-based systems and domain-specific Named Entity Recognition NER models, these methods fail to generalize across formats...

Prompt Injection Vulnerability of Consensus Generating Applications in Digital Democracy

Large Language Models LLMs are gaining traction as a method to generate consensus statements and aggregate preferences in digital democracy experiments. Yet, LLMs may introduce critical vulnerabilities in these systems. Here, we explore the impact of prompt-injection attacks targeting consensus...