Stealthy Concurrent Audio Prompt Injections Against Multimodal LLM Agents
While this paradigm enhances interaction naturalness, it introduces a critical yet under-explored attack surface, as audio inputs inevitably contain environmental noise beyond user control. In this paper, we investigate concurrent audio prompt injection attacks targeting multimodal agents. Distinct from traditional acoustic attacks on voice devices, we propose novel techniques for instruction augmentation and scenario concealment.
- ▪While this paradigm enhances interaction naturalness, it introduces a critical yet under-explored attack surface, as audio inputs inevitably contain environmental noise beyond user control.
- ▪In this paper, we investigate concurrent audio prompt injection attacks targeting multimodal agents.
- ▪Distinct from traditional acoustic attacks on voice devices, we propose novel techniques for instruction augmentation and scenario concealment.
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| Original publisher | arXiv.org |
| Canonical URL | https://arxiv.org/abs/2607.28165 |
| Publication time | Fri, 31 Jul 2026 14:02:19 +0000 |
| Retrieval time | 2026-07-31T14:23:02.758Z |
| Last seen | 2026-07-31T14:23:02.758Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | gzkQw_T7v4HR · 1 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
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| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
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Computer Science > Cryptography and Security arXiv:2607.28165 (cs) [Submitted on 30 Jul 2026] Title:Piggybacking on Perception: Stealthy Concurrent Audio Prompt Injections against Multimodal LLM Agents Authors:Mingxiao Liu (1), Yitong Li (1), Haoren Zhao (1), Yaoxiang Bian (1), Jianan Ma (1 and 2), Jian Zhang (1), Jialuo Chen (3 and 2), Xinhao Deng (4 and 2), Zhen Wang (1) ((1) Hangzhou Dianzi University, (2) Ant Group, (3) Zhejiang University, (4) Tsinghua University) View a PDF of the paper titled Piggybacking on Perception: Stealthy Concurrent Audio Prompt Injections against Multimodal LLM Agents, by Mingxiao Liu (1) and 11 other authors View PDF HTML (experimental) Abstract:Large Language Model (LLM)-driven multimodal agents are increasingly deployed to execute autonomous tasks via…
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