Abstract
The rapid proliferation of Multimodal Large Language Models (MLLMs) has ushered in the era of the “Agentic Economy,” where Mobile Agents autonomously execute high-stakes financial transactions. While these agents demonstrate impressive operational capabilities, their adversarial robustness remains a glaring blind spot. In this paper, we identify a systemic vulnerability termed Visual Dominance Hallucination (VDH), where imperceptible adversarial visual cues can act as a “super-stimulus,” overriding textual price evidence in our evaluated screenshot-based price-constrained settings and forcing the agent into irrational economic decisions. We propose PriceBlind, a stealthy, white-box adversarial attack framework for controlled screenshot-based evaluation. Unlike prior works that rely on conspicuous artifacts like pop-ups, PriceBlind exploits the modality gap in CLIP-based encoders via a novel Semantic-Decoupling Loss. Rather than literally making a luxury item “look cheap,” this regularizer weakens the consistency between high-price text and visual value cues by aligning the image embedding with a low-cost/value-associated anchor region while preserving pixel-level fidelity. On our main E-ShopBench benchmark with clear price constraints, screenshot-based white-box evaluation yields ASRs around 80% on the evaluated agents. Under the evaluated single-turn coordinate-selection protocol in a simplified layout-aware setting, our Ensemble-DI-FGSM strategy also yields non-trivial black-box transfer, with ASR roughly 35–41% across GPT-4o, Gemini-1.5-Pro, and Claude-3.5-Sonnet. In the same screenshot-based setting, standard robust encoders reduce ASR only partially, while a Verify-then-Act stack with robust encoders lowers ASR to below 10% at some clean-accuracy cost.- Anthology ID:
- 2026.findings-acl.788
- Volume:
[Findings of the Association for Computational Linguistics: ACL 2026](/volumes/2026.findings-acl/)- Month:
- July
- Year:
- 2026
- Address:
- San Diego, California, United States
- Editors:
[Maria Liakata](/people/maria-liakata/),[Viviane P. Moreira](/people/viviane-p-moreira/unverified/),[Jiajun Zhang](/people/jiajun-zhang/unverified/),[David Jurgens](/people/david-jurgens/)- Venue:
[Findings](/venues/findings/)- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 16059–16073
- Language:
- URL:
[https://aclanthology.org/2026.findings-acl.788/](https://aclanthology.org/2026.findings-acl.788/)- DOI:
- Cite (ACL):
- Jiachen Qian and Zhaolu Kang. 2026. "Penny Wise, Pixel Foolish": Bypassing Price Constraints in Multimodal Agents via Visual Adversarial Perturbations. InFindings of the Association for Computational Linguistics: ACL 2026, pages 16059–16073, San Diego, California, United States. Association for Computational Linguistics. - Cite (Informal):
[“Penny Wise, Pixel Foolish”: Bypassing Price Constraints in Multimodal Agents via Visual Adversarial Perturbations](https://aclanthology.org/2026.findings-acl.788/)(Qian & Kang, Findings 2026)- PDF:
[https://aclanthology.org/2026.findings-acl.788.pdf](https://aclanthology.org/2026.findings-acl.788.pdf)