# In-Browser LLM-Guided Fuzzing for Real-Time Prompt Injection Testing in Agentic AI Browsers

arXiv: https://arxiv.org/abs/2510.13543 · HTML: https://mlsanity.com/paper/2510.13543/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use.
- Builds on 2 of its 11 references that are in the map.
- First posted on arXiv 15 Oct 2025.

## Placed in

- LLM agents and tool use / [Agent threats, guardrails and governance](https://mlsanity.com/agents/agent-threats-guardrails-and-governance/)
- LLM safety and security / [Prompt injection and agent security](https://mlsanity.com/safety/prompt-injection-and-agent-security/)

## Builds on (most cited first, 2 of 2)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Universal Adversarial Triggers for Attacking and Analyzing NLP](https://mlsanity.com/paper/1908.07125/) (2019)

## Built on by (most cited first, 2 of 2)

- [Building Browser Agents: Architecture, Security, and Practical Solutions](https://mlsanity.com/paper/2511.19477/) (2025)
- [VATS: Exploiting Implicit Authority in Error-Path Injection via Systematic Mutation](https://mlsanity.com/paper/2606.07992/) (2026)

## Abstract (arXiv)

Large Language Model (LLM) based agents integrated into web browsers (often called agentic AI browsers) offer powerful automation of web tasks. However, they are vulnerable to indirect prompt injection attacks, where malicious instructions hidden in a webpage deceive the agent into unwanted actions. These attacks can bypass traditional web security boundaries, as the AI agent operates with the user privileges across sites. In this paper, we present a novel fuzzing framework that runs entirely in the browser and is guided by an LLM to automatically discover such prompt injection vulnerabilities in real time.