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New framework MUZZLE finds 44 novel attacks on web agents

By PulseAugur Editorial · [1 sources] ·

Researchers have developed MUZZLE, an automated framework designed to test the security of web agents against indirect prompt injection attacks. This system adaptively identifies vulnerable injection points and crafts context-aware malicious instructions to compromise confidentiality, integrity, and availability. MUZZLE's evaluations have uncovered numerous new attacks across various web applications and LLMs, demonstrating its effectiveness in discovering vulnerabilities with minimal human oversight. AI

IMPACT This research highlights critical security vulnerabilities in web agents, potentially influencing future development and security practices for LLM-based applications.

RANK_REASON The cluster contains an academic paper detailing a new research framework and its findings. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New framework MUZZLE finds 44 novel attacks on web agents

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The cluster contains an academic paper detailing a new research framework and its findings. [lever_c_demoted from research: ic=1 ai=1.0]
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High
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102 days old
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Georgios Syros, Evan Rose, Brian Grinstead, Christoph Kerschbaumer, William Robertson, Cristina Nita-Rotaru, Alina Oprea ·

    MUZZLE: Adaptive Agentic Red-Teaming of Web Agents Against Indirect Prompt Injection Attacks

    arXiv:2602.09222v2 Announce Type: replace-cross Abstract: Large language model (LLM) based web agents are increasingly deployed to automate complex online tasks by directly interacting with web sites and performing actions on users' behalf. While these agents offer powerful capab…

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