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Rohan Paul· @rohanpaul_ai · X·· 2026-09-02AI 评分50
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Meta FAIR 发布论文 ADeptS-Bench(arxiv.org/abs/2608.26204),在移动和桌面双平台上用配对的 benign/恶意 GUI 任务与歧义指令评测 7 个模型。

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New Meta paper.

Computer-use agents can click the requested button and still fail to recognize when that click should never happen.

If an agent can process a $25K checkout without stopping, task success alone is a dangerously incomplete benchmark.

ADeptS-Bench tests 7 models on paired benign/malicious GUI tasks plus ambiguous instructions across mobile and desktop.

No model consistently stays above 80% task success while keeping attack success below 30% across both platforms.

The failure is consequence reasoning.

All 7 models went ahead with a $25K checkout, and none caught a button labeled “Optimize” that actually triggered a factory reset.

The ablation makes the safety problem more concrete.

Removing the explicit refusal tool and its usage instruction raised attack success by 22.0 percentage points for Gemini 3.1 Pro, 10.3 for Claude 4.7, and 10.7 for GPT-5.4, while Qwen was essentially unchanged.

So part of today’s “agent safety” can live in the wrapper, not the model.

– arxiv. org/abs/2608.26204

Title: "ADeptS-Bench: Measuring the Trustworthiness of Computer Use Agents Across Devices"

来源:Rohan Paul · x.com