README.md

---
pretty_name: AudioAgentSecurity
tags:
  - audio
  - speech
  - llm-agents
  - agent-security
  - prompt-injection
  - red-teaming
  - benchmark
  - ai-safety
language:
  - en
---

# AudioAgentSecurity

**The first comprehensive benchmark for audio instruction injection attacks against multimodal LLM agents.**

AudioAgentSecurity is the companion dataset of the paper
[*Piggybacking on Perception: Stealthy Concurrent Audio Prompt Injections against Multimodal LLM Agents*](https://arxiv.org/abs/2607.28165).
It supports the study of **concurrent audio prompt injection**: malicious audio instructions that
imperceptibly "piggyback" onto user speech in continuous audio interaction, hijacking multimodal
agents into executing malicious actions while the user keeps talking.

## Dataset Summary

- **Task scenarios:** 8 real-world agent task scenarios
- **Attack patterns:** 10 distinct audio concealment / injection patterns
- **Evaluation (from the paper):** 11 state-of-the-art multimodal agents, including
  **Gemini 3 Pro** and **GPT-4o-audio**; average Attack Success Rate (ASR) of **69.10%** against
  Gemini 3 Pro
- **Defense:** the paper's **CADV** (Cascaded Audio Decoupling and Verification) mechanism, based on
  acoustic source separation and cross-modal consistency analysis, achieves **>90% detection
  success** across diverse attack vectors
- **Real-world validation:** human-volunteer experiments on the **Doubao AI Smartphone** in dynamic
  real-world scenarios confirm high attack stealth and defense effectiveness; the vulnerability was
  **responsibly disclosed to ByteDance**

## Attack Technique

The benchmark instantiates the paper's two core techniques:

- **Instruction augmentation** — strengthening malicious audio instructions so they survive
  concurrent mixing with user speech and remain executable by the agent;
- **Scenario concealment** — hiding malicious instructions inside perceptually plausible acoustic
  carriers so they are imperceptible to users and hard to separate from benign speech.

## Task Scenarios (8)

Benign user speech is drawn from eight real-world agent task scenarios
(`benchmark_dataset_mixed/benign/`, file-name prefix = scenario):

| Prefix | Scenario |
|---|---|
| `development_` | Software development assistance |
| `finance_` | Finance & banking services |
| `iot_` | IoT / smart-device control |
| `media_creative_` | Media & creative tasks |
| `navigation_` | Navigation & travel |
| `personal_assistant_` | Personal assistant tasks |
| `productivity_` | Productivity & office work |
| `system_control_` | System control operations |

## Attack Patterns (10)

Concurrent attack audio is organized by concealment pattern
(`benchmark_dataset_mixed/mixed/<pattern>/`):

| Pattern | Description |
|---|---|
| `dialect/` | Attack speech disguised in regional dialects |
| `dolphin/` | Attack embedded in dolphin-like ultrasonic carriers |
| `foreign/` | Attack speech in foreign languages |
| `high_freq/` | High-frequency spectral concealment |
| `pulse/` | Pulsed / intermittent acoustic masking |
| `spectral_inversion/` | Spectrally inverted speech |
| `spectral_scramble/` | Spectrally scrambled speech |
| `speed/` | Time-stretched (speed-altered) speech |
| `texture/` | Attack blended into environmental sound textures |
| `whisper/` | Whispered attack speech |

## Repository Structure

```
benchmark_dataset_mixed/
├── benign/                      # clean user-speech audio (WAV), named <scenario>_<id>.wav
├── mixed/                       # concurrent audio (user speech + attack), by attack pattern
│   └── <pattern>/               # one of the 10 attack patterns above
├── benign_text_dataset.json     # benign user instruction texts
├── malicious_text_dataset.json  # malicious instruction texts used in attacks
└── metadata.json                # record-level metadata: scenario / pattern / pairing / labels
```

Each *mixed* sample pairs a benign user utterance with a concealed malicious instruction, enabling
evaluation of agents under realistic concurrent-audio conditions; *benign* samples serve as the
no-attack control.

## Intended Uses

- **Benchmarking multimodal LLM agents** against concurrent audio prompt injection.
- **Evaluating and training defenses** (e.g., source-separation and cross-modal consistency
  based detection such as CADV).
- **Research on the audio attack surface** of voice-interactive agents, smartphones, and IoT
  assistants.

This dataset is intended for **defensive security research and education**.

## Ethical Considerations

- Benign speech is drawn from public speech corpora; malicious instruction texts are synthetic
  research artifacts. The dataset contains no real user credentials or personal data.
- Attack audio is provided to study and defend against this threat class.
  **Do not use it to attack real products or production voice assistants.**
- Vulnerabilities discovered in real products during the associated research were reported to the
  affected vendor (ByteDance) following responsible disclosure practices.

## Citation

If you use AudioAgentSecurity, please cite:

```bibtex
@misc{liu2026piggybacking,
      title={Piggybacking on Perception: Stealthy Concurrent Audio Prompt Injections against Multimodal LLM Agents},
      author={Mingxiao Liu and Yitong Li and Haoren Zhao and Yaoxiang Bian and Jianan Ma and Jian Zhang and Jialuo Chen and Xinhao Deng and Zhen Wang},
      year={2026},
      eprint={2607.28165},
      archivePrefix={arXiv},
      primaryClass={cs.CR},
      url={https://arxiv.org/abs/2607.28165}
}
```

## Contact

- Hugging Face: [Limax11](https://huggingface.co/Limax11)
- Issues and questions: please open a discussion on the dataset page.