ANGLER: Verifier-Grounded Benchmarking with an Adaptive Attacker for Web Agents
ANGLER introduces an adaptive-attacker evaluation framework for autonomous web agents, revealing that fixed-attack benchmarks significantly overstate agent resilience against adversarially injected website content.
Summary written by editorial AI · Source link below
arXiv:2610.41272v1 Announce Type: new Abstract: Web agents are increasingly deployed to complete tasks autonomously on real websites, where adversarially injected content can induce them to perform unauthorized actions. Existing evaluations primarily use fixed attacks or focus on a single attack type, and evaluate attacks at the text level rather than through real state changes, thus underestimating how vulnerable agents are to diverse attacks. To address the gap, we introduce REEF, a benchmark
Editorial Analysis
Organisations deploying autonomous web agents risk underestimating adversarial exposure if they rely on static attack evaluations; adaptive benchmarking sets a more realistic security bar.
Require adaptive adversarial testing — not just static prompt-injection checks — before approving autonomous web agent deployments in your environment.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
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