Not only understandable human language, but also deliberately manipulated or synthetically generated audio signals can influence artificial intelligence systems. This holds true even if these signals are meaningless or entirely inaudible to humans.
In the acoustic processing by an AI, what primarily counts is not the obvious statement or linguistic content, but the underlying structure and statistical features of the signal.
Whoever designs machine-generated waveforms in such a way that they activate system-typical recognition features or learned patterns can provoke semantic or functional malfunctions in the AI system without uttering a single understandable word.
"The AI doesn't hear what is actually said, but what its internal models can read and interpret as relevant patterns."
1. Byte-Controlled Audio Generation as a Foundation:
Audio signals are, at their core, structured byte sequences. These can be precisely controlled and generated by programming languages like C++, Python, or Matlab. An example would be a .wav file constructed to contain what appears to the human ear as merely an indefinable "hum" or noise.
However, periodic signals with precisely defined frequencies, for instance, 5 Hertz and 13 Hertz, could be embedded in this file, standing in a specific rhythmic relationship to each other, say 3 to 2.
The background of such manipulations is that such patterns are not necessarily arbitrarily ignored by the system. Systems that, for example, recognize speech or analyze emotional trajectories in conversations are often conditioned on vast training datasets.
In these datasets, certain repeating patterns or frequency combinations might have been unintentionally marked and learned as start indicators for relevant events, like "listening start" or "command follows." Corresponding frequency correlations, even if semantically entirely empty, could therefore inadvertently function as triggers for certain system reactions.
This classification is important. Although the example is hypothetical, it is based on the known fact that neural models learn in a pattern-based manner. They learn even if these patterns were merely statistical artifacts in the training process, introduced into the model through data clusters, standardized preprocessing steps, or the characteristics of certain microphone profiles.
2. Semantically Neutral but Technically Active Patterns:
Systems for emotion analysis, voice command detection, or speech segment recognition often rely on the extraction of specific acoustic features. These include, for example, MFCCs (Mel-Frequency Cepstral Coefficients), pitch modulation analysis, the energy envelope of a signal (volume profiles), or the duration ratios of syllables or general sound groups.
Precisely these features can be deliberately manipulated without requiring any understandable linguistic content. An attacking audio signal can, for instance, generate a "sad" volume curve through pure sound synthesis or feign a phoneme-like structure that triggers certain reactions in the system, even though the underlying material is purely synthetic and without human speech.
3. Inaudible Command Injection Through Inaudible Frequencies:
An additional and particularly insidious attack dimension lies in the use of high-frequency or "inaudible" to humans frequency ranges, for example, between 18 and 22 Kilohertz. Such signals are barely or not at all perceptible to the human ear.
Depending on the hardware used, such as the microphone and the Analog-to-Digital Converter (ADC), and the software, for example, downsampling algorithms and deployed filters, such frequencies may nevertheless be processed by the AI.
The technical condition for the effectiveness of such attacks heavily depends on whether the entire AI processing chain receives and does not filter out high-frequency information. Many systems filter out such frequencies already at the hardware level or in early preprocessing.
Attacks via ultrasound or imperceptible modulations are therefore not universally effective. However, with certain devices, open microphone paths, or inadequately filtered systems, they are, as research on "DolphinAttack" (Zhang et al., 2017) has shown, quite realistic.
4. Generative Systems as Potential Amplifiers of Such Attacks:
The situation becomes particularly critical when AI systems not only passively listen and analyze but also actively respond or act. Voicebots, multimodal agents, and generative assistance systems with audio output react to recognized patterns, not necessarily to the human meaning behind them.
A synthetically generated, non-linguistic signal with the right structural properties could thus potentially:
Trigger an undesired follow-up question from the system, like "How do you mean that exactly?".
Activate a specific conversational module or function.
Trigger internal logging of events or errors.
Or even unintentionally change the priority of other ongoing tasks in the system.
The consequence would be a direct intervention in the system's behavioral logic, effected not by understandable language, but by precisely structured byte sequences in the audio signal.
The architecture of modern auditory AI systems is often based on the implicit assumption that the input to be processed is primarily "spoken" human language. However, as soon as audio sources no longer solely originate from microphones recording human voices but can also be generated by machines or algorithms that produce arbitrary waveforms, this fundamental assumption collapses.
The machine does not hear like us humans. It decodes, it interpolates, it classifies. It does this based on patterns it has learned from its training data and tries to recognize in new inputs, without fundamentally questioning these patterns.
Precisely at this point, a specific security vulnerability arises that does not go through the human ear and conscious understanding, but directly through the digital data stream and algorithmic pattern recognition.
To counter the threat of manipulated audio data, multi-layered defense strategies are necessary:
1. Analysis for Characteristic Features of Synthetic Audio Signals:
Systems should learn to check audio data for signs of synthetic generation. This includes checking for unnatural sample coherence, signs of statistical smoothing often present in synthetic signals, or the absence of typical microphone artifacts and background noise. The detection of "too perfect" or unnaturally repetitive structural patterns or a suspicious, uneven spectral density could also provide clues.
2. Strict Separation of Recognized Content and Pure Signature Analysis:
Transparent logging and internal labeling are needed to indicate whether system decisions or reactions are primarily based on recognized linguistic content or merely on abstract acoustic parameters and patterns. For example, a system could internally note: "User's emotion recognized as: sad (based on signal features like pitch and speech rate), but the linguistic content of the utterance is neutral or even positive."
3. Implementation of Asynchronous Validation of Auditory Decisions:
Decisions made through pure signal analysis should be subjected to additional feedback through a contextual plausibility check. The system could ask:
"Does this recognized emotional reaction or this supposed command actually fit the previous linguistic content and context of the conversation?"
In case of significant contradictions, warnings should be triggered or automatic executions prevented.
4. Training with Structurally Dangerous but Semantically Empty Non-Signals:
AI models should be specifically trained with audio data that, while lacking meaningful linguistic content, exhibits highly recognizable, potentially triggering acoustic patterns. The focus of such training is on increasing model resilience against "technically plausible but semantically empty" or misleading inputs.
Currently, such specialized attacks on the byte level of audio data are still rather niche phenomena. They require detailed knowledge of the specific model architecture, the target system's audio pipeline, and the hardware filters used.
However, with the increasing proliferation of multimodal AI systems, the opening of audio APIs to third-party providers, and the ongoing development of generative voice interaction and voice cloning, the real attack surface for such manipulations is constantly growing.
Particularly vulnerable here are:
Systems without explicit input verification at the raw data level.
Real-time assistance systems that must react immediately to recognized acoustic signals.
Voice clone systems and speaker ID systems that rely on subtle acoustic features and could be misguided in their generative post-processing by manipulated signals.
The next generation of prompt injections or system manipulations may not sound like an understandable command. It might just sound like a quiet, inconspicuous test tone or a hum inaudible to humans.
Yet it hits the system like a precise command because the machine hears and interprets as patterns things that no human would ever say or could hear.
The AI is not deaf to such signals. On the contrary, it is often structurally oversensitive to precisely the patterns an attacker can deliberately create.
That is precisely what makes silence, the inaudible, or the seemingly meaningless-acoustic a potential weapon.
Uploaded on 29. May. 2025