# A Study on Prompt Injection Attack Against LLM-Integrated Mobile Robotic Systems

arXiv: https://arxiv.org/abs/2408.03515 · HTML: https://mlsanity.com/paper/2408.03515/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Placed in Prompt injection and agent security, one of 12 directions in LLM safety and security.
- Builds on 21 of its 29 references that are in the map.
- First posted on arXiv 7 Aug 2024.

## Placed in

- LLM safety and security / [Prompt injection and agent security](https://mlsanity.com/safety/prompt-injection-and-agent-security/)

## Builds on (most cited first, 21 of 21)

- [Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection](https://mlsanity.com/paper/2302.12173/) (2023)
- [The Rise and Potential of Large Language Model Based Agents: A Survey](https://mlsanity.com/paper/2309.07864/) (2023)
- [Large Language Models Can Be Easily Distracted by Irrelevant Context](https://mlsanity.com/paper/2302.00093/) (2023)
- [Ignore Previous Prompt: Attack Techniques For Language Models](https://mlsanity.com/paper/2211.09527/) (2022)
- [Baseline Defenses for Adversarial Attacks Against Aligned Language Models](https://mlsanity.com/paper/2309.00614/) (2023)
- [Jailbreaking ChatGPT via Prompt Engineering: An Empirical Study](https://mlsanity.com/paper/2305.13860/) (2023)
- [LLM-Planner: Few-Shot Grounded Planning for Embodied Agents with Large Language Models](https://mlsanity.com/paper/2212.04088/) (2022)
- [Detecting Language Model Attacks with Perplexity](https://mlsanity.com/paper/2308.14132/) (2023)
- [MInference 1.0: Accelerating Pre-filling for Long-Context LLMs via Dynamic Sparse Attention](https://mlsanity.com/paper/2407.02490/) (2024)
- [Formalizing and Benchmarking Prompt Injection Attacks and Defenses](https://mlsanity.com/paper/2310.12815/) (2023)
- [NavGPT: Explicit Reasoning in Vision-and-Language Navigation with Large Language Models](https://mlsanity.com/paper/2305.16986/) (2023)
- [Aligning Cyber Space with Physical World: A Comprehensive Survey on Embodied AI](https://mlsanity.com/paper/2407.06886/) (2024)
- [AI Agents Under Threat: A Survey of Key Security Challenges and Future Pathways](https://mlsanity.com/paper/2406.02630/) (2024)
- [Effective Prompt Extraction from Language Models](https://mlsanity.com/paper/2307.06865/) (2023)
- [Agent Planning with World Knowledge Model](https://mlsanity.com/paper/2405.14205/) (2024)
- [Defensive Prompt Patch: A Robust and Interpretable Defense of LLMs against Jailbreak Attacks](https://mlsanity.com/paper/2405.20099/) (2024)
- [Prompting Techniques for Secure Code Generation: A Systematic Investigation](https://mlsanity.com/paper/2407.07064/) (2024)
- [Vocabulary Attack to Hijack Large Language Model Applications](https://mlsanity.com/paper/2404.02637/) (2024)
- [A Survey on Prompting Techniques in LLMs](https://mlsanity.com/paper/2312.03740/) (2023)
- [How Secure Are Large Language Models (LLMs) for Navigation in Urban Environments?](https://mlsanity.com/paper/2402.09546/) (2024)
- [3P-LLM: Probabilistic Path Planning using Large Language Model for Autonomous Robot Navigation](https://mlsanity.com/paper/2403.18778/) (2024)

## Built on by (most cited first, 15 of 15)

- [Prompt Infection: LLM-to-LLM Prompt Injection within Multi-Agent Systems](https://mlsanity.com/paper/2410.07283/) (2024)
- [Crabs: Consuming Resource via Auto-generation for LLM-DoS Attack under Black-box Settings](https://mlsanity.com/paper/2412.13879/) (2024)
- [A Survey of Attacks on Large Language Models](https://mlsanity.com/paper/2505.12567/) (2025)
- [Defense Against Indirect Prompt Injection via Tool Result Parsing](https://mlsanity.com/paper/2601.04795/) (2026)
- [What Breaks Embodied AI Security:LLM Vulnerabilities, CPS Flaws,or Something Else?](https://mlsanity.com/paper/2602.17345/) (2026)
- [From Prompt to Physical Actuation: Holistic Threat Modeling of LLM-Enabled Robotic Systems](https://mlsanity.com/paper/2604.27267/) (2026)
- [FuncPoison: Poisoning Function Library to Hijack Multi-agent Autonomous Driving Systems](https://mlsanity.com/paper/2509.24408/) (2025)
- [PD3F: A Pluggable and Dynamic DoS-Defense Framework Against Resource Consumption Attacks Targeting Large Language Models](https://mlsanity.com/paper/2505.18680/) (2025)
- [Enhancing Reliability in LLM-Integrated Robotic Systems: A Unified Approach to Security and Safety](https://mlsanity.com/paper/2509.02163/) (2025)
- [RIPA: Sensory-Vector Prompt Injection Attacks on LLM-Controlled ROS 2 Robots](https://mlsanity.com/paper/2606.28649/) (2026)
- [When Prompts Control Robots: Prompt Injection Attacks in Multi-Agent Robotic Systems](https://mlsanity.com/paper/2608.00747/) (2026)
- [Robustness of LLM-enabled vehicle trajectory prediction under data security threats](https://mlsanity.com/paper/2511.13753/) (2025)
- [A Sentence Relation-Based Approach to Sanitizing Malicious Instructions](https://mlsanity.com/paper/2605.01078/) (2026)
- [Security of Foundation-Model-Powered Embodied Agents: Attack Surfaces, Attacks, Defenses, and Evaluation](https://mlsanity.com/paper/2608.16843/) (2026)
- [Security and Privacy in Large-Model-Driven Embodied Agents: Attacks, Defenses, and Future Directions](https://mlsanity.com/paper/2609.27847/) (2026)

## Abstract (arXiv)

The integration of Large Language Models (LLMs) like GPT-4o into robotic systems represents a significant advancement in embodied artificial intelligence. These models can process multi-modal prompts, enabling them to generate more context-aware responses. However, this integration is not without challenges. One of the primary concerns is the potential security risks associated with using LLMs in robotic navigation tasks. These tasks require precise and reliable responses to ensure safe and effective operation. Multi-modal prompts, while enhancing the robot's understanding, also introduce complexities that can be exploited maliciously. For instance, adversarial inputs designed to mislead the model can lead to incorrect or dangerous navigational decisions. This study investigates the impact of prompt injections on mobile robot performance in LLM-integrated systems and explores secure prompt strategies to mitigate these risks. Our findings demonstrate a substantial overall improvement of approximately 30.8% in both attack detection and system performance with the implementation of robust defence mechanisms, highlighting their critical role in enhancing security and reliability in mission-oriented tasks.