Supply Chain Exploitation of Secure ROS 2 Systems: A Proof-of-Concept on Autonomous Platform Compromise via Keystore Exfiltration
This paper presents a proof-of-concept supply chain attack against the Secure ROS 2 (SROS 2) framework, demonstrated on a Quanser QCar2 autonomous vehicle platform. A Trojan-infected Debian package modifies core ROS 2 security commands to exfiltrate newly generated keystore credentials via DNS in base64-encoded chunks to an attacker-controlled nameserver. Possession of these credentials enables the attacker to rejoin the SROS 2 network as an authenticated participant and publish spoofed control or perception messages without triggering authentication failures. We evaluate this capability on a secure ROS 2 Humble testbed configured for a four-stop-sign navigation routine using an Intel RealSense camera for perception. Experimental results show that control-topic injections can cause forced braking, sustained high-speed acceleration, and continuous turning loops, while perception-topic spoofing can induce phantom stop signs or suppress real detections. The attack generalizes to any data distribution service (DDS)-based robotic system using SROS 2, highlighting the need for both supply chain integrity controls and runtime semantic validation to safeguard autonomous systems against insider and impersonation threats.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Will bots take over the supply chain? Revisiting Agent-based supply chain automation
Agent-based systems have the capability to fuse information from many distributed sources and create better plans faster. This feature makes agent-based systems naturally suitable to address the challenges in Supply Chai…
ERPManagementA Survey on Blockchain-based Supply Chain Finance with Progress and Future directions
Supply Chain Finance is very important for supply chain competition, which is an important tool to activate the capital flow in the supply chain. Supply Chain Finance-related research can support multiple applications an…
Cloud ComputingManagementQuantum-Inspired Reinforcement Learning for Secure and Sustainable AIoT-Driven Supply Chain Systems
Modern supply chains must balance high-speed logistics with environmental impact and security constraints, prompting a surge of interest in AI-enabled Internet of Things (AIoT) solutions for global commerce. However, con…
Reinforcement LearningPrivacy Preserving Multi-Agent Reinforcement Learning in Supply Chains
This paper addresses privacy concerns in multi-agent reinforcement learning (MARL), specifically within the context of supply chains where individual strategic data must remain confidential. Organizations within the supp…
Multi-agent Reinforcement LearningPolicy Gradient MethodsPrivacy Preservingreinforcement-learning+1SEDULity: A Proof-of-Learning Framework for Distributed and Secure Blockchains with Efficient Useful Work
The security and decentralization of Proof-of-Work (PoW) have been well-tested in existing blockchain systems. However, its tremendous energy waste has raised concerns about sustainability. Proof-of-Useful-Work (PoUW) ai…