Rethinking MCP Security: A Large-Scale Study of Runtime MCP Servers and Security Scanner Reliability

The Model Context Protocol MCP has rapidly established itself as a standard interface for enabling LLM-based agents to interact with external tools and services. As MCP servers are increasingly entrusted with security-sensitive operations, understanding their real-world risks has become critical...

Uncovering Similar but Different Packages in PyPI and Potential Security Threats

In this study, we present a large-scale, in-depth study of package replication in PyPI. As a vital platform, PyPI streamlines Python package distribution for developers. However, beyond small-scale code cloning, we observe that many replicated packages exist on PyPI, which duplicate most of the...

Understanding Binary Code Similarity for Real-World Vulnerability Detection: A Large-Scale Empirical Study

Firmware lies at the heart of IoT devices. Its development depends heavily on third-party libraries TPLs, which greatly accelerate the process but simultaneously introduce associated vulnerabilities. Binary Code Similarity Detection BCSD is an effective technique for identifying vulnerabilities i...

Evaluating LLMs for Obfuscation Detection and Classification in Android Apps

Android applications apps developers increasingly rely on code obfuscation techniques to hinder reverse engineering and protect intellectual property. However, obfuscation also reduces the effectiveness of static analysis and vulnerability detection tools, creating challenges for Android security...

The Fault in Our Drafts: Vulnerabilities in RPKI Specification and Software

The Resource Public Key Infrastructure RPKI secures the Internet's routing system by defining a complex trust and validation framework for certificates, Route Origin Authorizations ROAs, manifests, and Certificate Revocation Lists CRLs. These mechanisms are specified across dozens of RFCs. This...

Indirect Prompt Injection in the Wild: An Empirical Study of Prevalence, Techniques, and Objectives

As LLMs are increasingly integrated into systems that browse, retrieve, summarize, and act on web content, webpages have become an untrusted input vector for downstream model behavior. This enables site owners, contributors, and adversaries to embed instructions directly in web resources, i.e.,...

Hackers or Hallucinators? A Comprehensive Analysis of LLM-Based Automated Penetration Testing

The rapid advancement of Large Language Models LLMs has created new opportunities for Automated Penetration Testing AutoPT, spawning numerous frameworks aimed at achieving end-to-end autonomous attacks. However, despite the proliferation of related studies, existing research generally lacks...

Debt behind the AI Boom: A Large-Scale Empirical Study of AI-Generated Code in the Wild

AI coding assistants are now widely used in software development. Software developers increasingly integrate AI-generated code into their codebases to improve productivity. Prior studies have shown that AI-generated code may contain code quality issues under controlled settings. However, we still...

Internet-Scale Measurement of React2Shell Exploitation Using an Active Network Telescope

The increasing adoption of server-side component-based web frameworks has introduced new application-layer attack surfaces that remain insufficiently understood at Internet scale. On 3 December 2025, a critical remote code execution vulnerability CVE-2025-55182 in React Server Components, referre...

DuoLungo: Usability Study of Duo 2FA

Multi-Factor Authentication MFA enhances login security by requiring multiple authentication factors. Its adoption has increased in response to more frequent and sophisticated attacks. Duo is widely used by organizations including Fortune 500 companies and major educational institutions, yet its...

A Large Scale Study of AI-Based Binary Function Similarity Detection Techniques for Security Researchers and Practitioners

Binary Function Similarity Detection BFSD is a foundational technique in software security, underpinning a wide range of applications including vulnerability detection, malware analysis. Recent advances in AI-based BFSD tools have led to significant performance improvements. However, existing...

Benchmarking Fake Voice Detection in the Fake Voice Generation Arms Race

As advances in synthetic voice generation accelerate, an increasing variety of fake voice generators have emerged, producing audio that is often indistinguishable from real human speech. This evolution poses new and serious threats across sectors where audio recordings serve as critical evidence...

KeyDroid: a Large-Scale Analysis of Secure Key Storage in Android Apps

Most contemporary mobile devices offer hardware-backed storage for cryptographic keys, user data, and other sensitive credentials. Such hardware protects credentials from extraction by an adversary who has compromised the main operating system, such as a malicious third-party app. Since 2011,...

Unveiling the Landscape of LLM Deployment in the Wild: an Empirical Study

Background: Large language models LLMs are increasingly deployed via open-source and commercial frameworks, enabling individuals and organizations to self-host advanced AI capabilities. However, insecure defaults and misconfigurations often expose LLM services to the public Internet, posing...

Snorkeling in Dark Waters: a Longitudinal Surface Exploration of Unique Tor Hidden Services (Extended Version)

The Onion Router Tor is a controversial network whose utility is constantly under scrutiny. On the one hand, it allows for anonymous interaction and cooperation of users seeking untraceable navigation on the Internet. This freedom also attracts criminals who aim to thwart law enforcement...