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新论文 · v1arXiv:2611.06039

Securing Web Agents with the Compile-Then-Execute Paradigm

Annabella Chow, Jinhao Zhu, Sylvie Venuto, Abdulaziz Alharbi, Basel Alomair, Raluca Popa, David Wagner, Julien Piet

原文摘要

Web agents commonly follow the ReAct paradigm, selecting each action from runtime observations that inherently mix trusted and untrusted content. This design makes their control flow vulnerable to prompt injections embedded in webpages. We introduce Baya, a compile-then-execute web agent designed to prevent prompt-injection-driven control-flow hijacking by design. Before accessing the live website, Baya compiles the user's request into an executable Python program that calls a Trusted Website API. The planning model does not observe live website content; only the compiled program processes it. Untrusted data may determine program values or select among branches defined during compilation, but it cannot redefine the user task or cause the model to synthesize new website operations at runtime. The Trusted Website API gives the compiler a unified view of the website's action space, enabling ahead-of-time planning without the stepwise action discovery required by reactive agents. Our evaluation across 581 valid WebArena Verified tasks shows that Baya achieves 77.8% aggregate utility, outperforming browser-only ReAct (55.1%) and API-enabled ReAct (66.7%). On WASP, a security benchmark for web agents, Baya achieves 0% attack success rate, compared with 16.2% for API-enabled ReAct and 25.5% for browser-only ReAct.

cs.CR

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1134ACL Anthology · ACL 2026

BadScientist: Can a Research Agent Write Convincing but Unsound Papers that Fool LLM Reviewers?

Fengqing Jiang Apple and University of Washington, Yichen Feng University of Washington, Yuetai Li, Luyao Niu University of Washington, Basel Alomair University of Washington, Radha Poovendran University of Washington, Seattle

原文摘要

The convergence of LLM-powered research assistants and AI-based peer review systems creates a critical vulnerability: fully automated publication loops where AI-generated research is evaluated by AI reviewers without human oversight. We investigate this through BadScientist, a framework that evaluates whether fabrication-oriented paper generation agents can deceive multi-model LLM review systems. Our generator employs presentation-manipulation strategies requiring no real experiments. We develop a rigorous evaluation framework with formal error guarantees (concentration bounds and calibration analysis), calibrated on real data. Our results reveal systematic vulnerabilities: fabricated papers achieve acceptance rates up to 82%. Critically, we identify concern-acceptance conflict—reviewers frequently flag integrity issues yet assign acceptance-level scores. Our mitigation strategies show only marginal improvements, with detection accuracy barely exceeding random chance. Despite provably sound aggregation mathematics, integrity checking systematically fails, exposing fundamental limitations in current AI-driven review systems and underscoring the urgent need for defense-in-depth safeguards in scientific publishing.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表