Still Camouflage, Moving Illusion: View-Induced Trajectory Manipulation in Autonomous Driving

Existing physical adversarial attacks on vision-based autonomous driving induce time-evolving perception errors, including biased object tracking or trajectory prediction, through i sophisticated physical patch inducing detection box drift when entering the view distance, or ii dynamically changi...

Prompt Injection Via Road Signs

Interesting research: "CHAI: Command Hijacking Against Embodied AI." Abstract: 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...

T2I-Based Physical-World Appearance Attack against Traffic Sign Recognition Systems in Autonomous Driving

Traffic Sign Recognition TSR systems play a critical role in Autonomous Driving AD systems, enabling real-time detection of road signs, such as STOP and speed limit signs. While these systems are increasingly integrated into commercial vehicles, recent research has exposed their vulnerability to...

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...

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...

One Patch to Rule Them All: Transforming Static Patches into Dynamic Attacks in the Physical World

Numerous methods have been proposed to generate physical adversarial patches PAPs against real-world machine learning systems. However, each existing PAP typically supports only a single, fixed attack goal, and switching to a different objective requires re-generating and re-deploying a new PAP...

Code Injection in apolloauto/apollo

Description Arbitrary Code Excecution in genprotofile.py in ApolloAuto/Apollo. An open autonomous driving platform. Technical Description This package was vulnerable to Arbitrary code execution due to a use of a known vulnerable function load in yaml. fix is to be done genprotofile.py Exploit cod...

Tesla Autopilot Duped By 'Phantom' Images

Researchers said that autopilot systems used by popular cars – including the Tesla Model X – can be fooled into detecting fake images, projected by drones on the road or on surrounding billboards, as real. Attackers could potentially leverage this design hole to trigger the systems to brake or...

The Dark Sides of Modern Cars: Hacking and Data Collection

Like an unstoppable incoming tide, connectivity has quietly inundated the automobiles we so love to drive. In less than a decade, amazing driver-assist mechanisms and must-have infotainment systems have swept into the dashboards of many popular car models for sale today. And we’re just at the sta...