EUVD-2017-4379

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EUVD-2011-4503

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EUVD-2009-0780

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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...

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...

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...

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...

Asymmetry Vulnerability and Physical Attacks on Online Map Construction for Autonomous Driving

High-definition maps provide precise environmental information essential for prediction and planning in autonomous driving systems. Due to the high cost of labeling and maintenance, recent research has turned to online HD map construction using onboard sensor data, offering wider coverage and mor...

Integrated Simulation Framework for Adversarial Attacks on Autonomous Vehicles

Autonomous vehicles AVs rely on complex perception and communication systems, making them vulnerable to adversarial attacks that can compromise safety. While simulation offers a scalable and safe environment for robustness testing, existing frameworks typically lack comprehensive supportfor...

CyberSleuth: Autonomous Blue-Team LLM Agent for Web Attack Forensics

Large Language Model LLM agents are powerful tools for automating complex tasks. In cybersecurity, researchers have primarily explored their use in red-team operations such as vulnerability discovery and penetration tests. Defensive uses for incident response and forensics have received...

Securing and governing the rise of autonomous agents​​

In this blog, you will hear directly from Corporate Vice President and Deputy Chief Information Security Officer CISO for Identity, Igor Sakhnov, about how to secure and govern autonomous agents. This blog is part of a new ongoing series where our Deputy CISOs share their thoughts on what is most...

Securing and governing the rise of autonomous agents​​

In this blog, you will hear directly from Corporate Vice President and Deputy Chief Information Security Officer CISO for Identity, Igor Sakhnov, about how to secure and govern autonomous agents. This blog is part of a new ongoing series where our Deputy CISOs share their thoughts on what is most...

FALCON: Autonomous Cyber Threat Intelligence Mining with LLMs for IDS Rule Generation

Signature-based Intrusion Detection Systems IDS detect malicious activities by matching network or host activity against predefined rules. These rules are derived from extensive Cyber Threat Intelligence CTI, which includes attack signatures and behavioral patterns obtained through automated tool...