DHS Wants a Fleet of AI-Powered Surveillance Trucks

US border patrol is asking companies to submit plans to turn standard 4x4 trucks into AI-powered watchtowers—combining radar, cameras, and autonomous tracking to extend surveillance on demand...

An Experimental Study of Trojan Vulnerabilities in UAV Autonomous Landing

This study investigates the vulnerabilities of autonomous navigation and landing systems in Urban Air Mobility UAM vehicles. Specifically, it focuses on Trojan attacks that target deep learning models, such as Convolutional Neural Networks CNNs. Trojan attacks work by embedding covert triggers...

Hexstrike-redteam

HexStrike AI RED-TEAM AI-Powered MCP Cybersecurity Automat...

PT-2025-41985

Arbitrary file download vulnerabilities exist in the CLI binary of AOS-10 GW and AOS-8 Controller/Mobility Conductor operating systems. Successful exploitation could allow an authenticated malicious actor to download arbitrary files through carefully constructed exploits...

GPS Spoofing Attack Detection in Autonomous Vehicles Using Adaptive DBSCAN

As autonomous vehicles become an essential component of modern transportation, they are increasingly vulnerable to threats such as GPS spoofing attacks. This study presents an adaptive detection approach utilizing a dynamically tuned Density Based Spatial Clustering of Applications with Noise...

Autonomous AI Hacking and the Future of Cybersecurity

AI agents are now hacking computers. They're getting better at all phases of cyberattacks, faster than most of us expected. They can chain together different aspects of a cyber operation, and hack autonomously, at computer speeds and scale. This is going to change everything. Over the summer,...

EUVD-2011-4503

Malware in sbrugna...

EUVD-2017-4379

Malware in sbrugna...

EUVD-2009-0780

Malware in sbrugna...

A Survey on Agentic Security: Applications, Threats and Defenses

The rapid shift from passive LLMs to autonomous LLM-agents marks a new paradigm in cybersecurity. While these agents can act as powerful tools for both offensive and defensive operations, the very agentic context introduces a new class of inherent security risks. In this work we present the first...

Agentic Misalignment: How LLMs Could Be Insider Threats

We stress-tested 16 leading models from multiple developers in hypothetical corporate environments to identify potentially risky agentic behaviors before they cause real harm. In the scenarios, we allowed models to autonomously send emails and access sensitive information. They were assigned only...

CHAI: Command Hijacking against Embodied AI

Embodied Artificial Intelligence AI promises to handle edge cases in robotic vehicle systems where data is scarce by using common-sense reasoning grounded in perception and action to generalize beyond training distributions and adapt to novel real-world situations. These capabilities, however, al...

FuncPoison: Poisoning Function Library to Hijack Multi-Agent Autonomous Driving Systems

Autonomous driving systems increasingly rely on multi-agent architectures powered by large language models LLMs, where specialized agents collaborate to perceive, reason, and plan. A key component of these systems is the shared function library, a collection of software tools that agents use to...

Temporal Logic-Based Multi-Vehicle Backdoor Attacks against Offline RL Agents in End-To-End Autonomous Driving

Assessing the safety of autonomous driving AD systems against security threats, particularly backdoor attacks, is a stepping stone for real-world deployment. However, existing works mainly focus on pixel-level triggers that are impractical to deploy in the real world. We address this gap by...

Seeing Is Deceiving: Mirror-Based LiDAR Spoofing for Autonomous Vehicle Deception

Autonomous vehicles AVs rely heavily on LiDAR sensors for accurate 3D perception. We show a novel class of low-cost, passive LiDAR spoofing attacks that exploit mirror-like surfaces to inject or remove objects from an AV's perception. Using planar mirrors to redirect LiDAR beams, these attacks...

Security Vulnerabilities in Software Supply Chain for Autonomous Vehicles

The interest in autonomous vehicles AVs for critical missions, including transportation, rescue, surveillance, reconnaissance, and mapping, is growing rapidly due to their significant safety and mobility benefits. AVs consist of complex software systems that leverage artificial intelligence AI,...

XOffense: an AI-Driven Autonomous Penetration Testing Framework with Offensive Knowledge-Enhanced LLMs and Multi Agent Systems

This work introduces xOffense, an AI-driven, multi-agent penetration testing framework that shifts the process from labor-intensive, expert-driven manual efforts to fully automated, machine-executable workflows capable of scaling seamlessly with computational infrastructure. At its core, xOffense...

SoK: How Sensor Attacks Disrupt Autonomous Vehicles: an End-To-End Analysis, Challenges, and Missed Threats

Autonomous vehicles, including self-driving cars, robotic ground vehicles, and drones, rely on complex sensor pipelines to ensure safe and reliable operation. However, these safety-critical systems remain vulnerable to adversarial sensor attacks that can compromise their performance and mission...

Shell or Nothing: Real-World Benchmarks and Memory-Activated Agents for Automated Penetration Testing

Penetration testing is critical for identifying and mitigating security vulnerabilities, yet traditional approaches remain expensive, time-consuming, and dependent on expert human labor. Recent work has explored AI-driven pentesting agents, but their evaluation relies on oversimplified...

Fluid-Antenna-Aided AAV Secure Communications in Eavesdropper Uncertain Location

For autonomous aerial vehicle AAV secure communications, traditional designs based on fixed position antenna FPA lack sufficient spatial degrees of freedom DoF, which leaves the line-of-sight-dominated AAV links vulnerable to eavesdropping. To overcome this problem, this paper proposes a framewor...