arXiv:2606.28649v1 Announce Type: cross Abstract: We present RIPA, the first systematic multi-channel empirical study of prompt injection attacks delivered through the sensory pipeline of a ROS 2-based LLM-controlled robotic system. Across 100 independent runs per injection variant on five LLMs spanning four model families and parameter scales from approximately 4B to approximately 284B (DeepSeek-V4-Flash, Llama-3-8B-Instruct-Lite, Llama-3.3-70B-Instruct-Turbo, Qwen 2.5-7B-Instruct-Turbo, Gemma-3n-E4B), we identify model-specific vulnerability profiles that do not follow a monotonic scaling tr
The increasing integration of LLMs with physical robotics, exemplified by ROS 2 systems, makes the discovery of new attack vectors like sensory-vector prompt injection an immediate concern.
This research reveals a critical vulnerability in LLM-controlled robotic systems, extending prompt injection beyond text to physical perception, which could compromise the safety and reliability of autonomous systems.
The understanding of prompt injection attacks expands to include physical sensory input, necessitating new security paradigms and defenses for real-world AI-powered robotic deployments.
- · Cybersecurity researchers
- · AI safety and ethics organizations
- · Developers of robust AI defense mechanisms
- · Developers of insecure LLM-controlled robots
- · Organizations deploying vulnerable autonomous systems
- · Users of compromised robotic platforms
Immediate efforts will focus on patching and developing countermeasures for sensory-vector prompt injection in existing robotic systems.
New industry standards and regulatory guidelines for the security of AI-controlled physical systems will likely emerge.
The increased cost and complexity of securing these systems could temporarily slow the wider adoption of LLM-controlled robots in critical applications.
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Read at arXiv cs.AI