Sirens’Whisper injected near-ultrasonic prompts into speech-driven LLMs
A 2026 research framework used near-ultrasonic audio to deliver covert prompts and jailbreaks to speech-driven LLM interfaces under black-box conditions with commodity hardware.
Research demonstration of covert prompt injection into speech-driven LLMs. It establishes an input channel, not autonomous model escape.
Case kind
vulnerability
Claims
2
Reconstruction
Timeline
2026-03
Step
Claims & evidence
reported findingsupported
The authors report black-box effectiveness against commercial and open-source speech-driven LLMs and a user study in which the injected audio was perceptually indistinguishable from background-only playback.
Locator: Abstract, evaluation and user-study summary
reported findingsupported
Sirens’Whisper encodes target baseband audio into near-ultrasonic waveforms that can demodulate after acoustic transmission and microphone nonlinearity, enabling covert prompts to speech-driven LLMs.
Multimodal-agent security needs to authenticate and separate human speech from machine-readable acoustic instructions rather than assuming audible equivalence.
Controls & mitigations
Near-ultrasonic filtering and microphone hardening
Authenticate command provenance rather than treating all decoded speech as human-authorized
Adversarial testing across devices and environments
Require confirmation for high-impact actions
What remains unknown
Results are specific to tested devices, acoustic environments and model interfaces and should not be generalized to every microphone or speech model.
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