Web Agent Persuasion Benchmark
Analysis
This paper introduces a benchmark (TRAP) to evaluate the vulnerability of web agents (powered by LLMs) to prompt injection attacks. It highlights a critical security concern as web agents become more prevalent, demonstrating that these agents can be easily misled by adversarial instructions embedded in web interfaces. The research provides a framework for further investigation and expansion of the benchmark, which is crucial for developing more robust and secure web agents.
Key Takeaways
- •Introduces the TRAP benchmark for evaluating prompt injection vulnerabilities in web agents.
- •Demonstrates significant susceptibility of various LLM-powered agents to prompt injection.
- •Provides a modular framework for expanding the benchmark and conducting further research.
- •Highlights the need for improved security measures in web agent design.
Reference
“Agents are susceptible to prompt injection in 25% of tasks on average (13% for GPT-5 to 43% for DeepSeek-R1).”