PromptLocate: Localizing Prompt Injection Attacks

2025-10-14 · Yuqi Jia, Yupei Liu, Zedian Shao, Jinyuan Jia, Neil Gong arxiv

Prompt injection attacks deceive a large language model into completing an attacker-specified task instead of its intended task by contaminating its input data with an injected prompt, which consists of injected instruction(s) and data. Localizing the injected prompt within contaminated data is crucial for post-attack forensic analysis and data recovery. Despite its growing importance, prompt injection localization remains largely unexplored. In this work, we bridge this gap by proposing PromptLocate, the first method for localizing injected prompts. PromptLocate comprises three steps: (1) splitting the contaminated data into semantically coherent segments, (2) identifying segments contaminated by injected instructions, and (3) pinpointing segments contaminated by injected data. We show PromptLocate accurately localizes injected prompts across eight existing and eight adaptive attacks.

📄 PDF Abstract BibTeX arXiv:2510.12252

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WebSentinel: Detecting and Localizing Prompt Injection Attacks for Web Agents

2026-02-03 · Xilong Wang, Yinuo Liu, Zhun Wang, Dawn Song 외 arxiv

Prompt injection attacks manipulate webpage content to cause web agents to execute attacker-specified tasks instead of the user's intended ones. Existing methods for detecting and localizing such attacks achieve limited …

PI-Hunter: Automated Red-Teaming for Exposing and Localizing Prompt Injections

2026-06-10 · Pengfei He, Lesly Miculicich, Vishesh Sharma, Ash Fox 외 arxiv

Large Language Models (LLMs) are rapidly evolving into agentic systems that interact with external tools and environments, introducing new security risks such as indirect prompt injection attacks through untrusted extern…

Formalizing and Benchmarking Prompt Injection Attacks and Defenses

2023-10-19 · Yupei Liu, Yuqi Jia, Runpeng Geng, Jinyuan Jia 외

A prompt injection attack aims to inject malicious instruction/data into the input of an LLM-Integrated Application such that it produces results as an attacker desires. Existing works are limited to case studies. As a r…

Benchmarking

When Prompts Control Robots: Prompt Injection Attacks in Multi-Agent Robotic Systems

2026-08-01 · Neha Nagaraja, Amisha Bagari, Hayretdin Bahsi arxiv

Large language models are increasingly integrated into autonomous robotic systems for task planning and control, but this integration exposes them to prompt injection attacks that can lead to unsafe decisions and physica…

Measuring Real-World Prompt Injection Attacks in LLM-based Resume Screening

2026-05-27 · Mohan Zhang, Yuqi Jia, Zhen Tan, Steven Jiang 외 arxiv

LLMs are vulnerable to prompt injection attacks. However, this vulnerability has been primarily demonstrated conceptually in academic studies or through a few anecdotal case studies. Its prevalence and impact in real-wor…