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. 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:

@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
• Issues and questions: please open a discussion on the dataset page.