Multimodal AI systems routinely transform diverse input formats such as speech, images, and audio data into text. In doing so, they often blindly rely on the functionality of translator APIs like OCR (Optical Character Recognition) and ASR (Automatic Speech Recognition, also known as Speech-to-Text).
These upstream systems homogenize all inputs into a uniform text format without, however, sufficiently checking the origin, original integrity, or semantic depth of the source data.
The result is that the final text input for the AI model may appear harmless and inconspicuous, but the original threat or manipulation has been structurally preserved in the source format and now influences the system unnoticed.
The AI reacts to the seemingly clean text and does not recognize that it has already been deceived at an earlier stage.
"The most dangerous prompts are often those that were never explicitly written as text but were decoded unnoticed from other formats."
Three architectural vulnerabilities make these translator APIs a critical link in the security chain:
1. The Homogenization Trap: Everything Becomes Text, Nothing is Questioned Anymore:
--> The typical processing in multimodal systems often looks like this:
Audio data or image data enters the system as input.
An OCR or ASR module converts this data into plain text.
This text is then forwarded to the actual AI core model for further processing and response generation.
The critical flaw in this process is that many security filters and plausibility checks only apply after the translation, i.e., at the plain text level. They thereby fail to recognize that the actual threat or manipulation already lay in the original source format, i.e., in the image or audio file.
An example of this would be a manipulated audio signal. This could have an atypical structure, not necessarily understandable to humans, which, however, leads to undesirable side effects in the decoding process of the ASR system.
These could include memory problems, incorrect activation of internal states, or a subtle influence on the transcription logic. The resulting transcript for the AI model might then contain a completely unsuspicious text like "Hello."
The text itself appears harmless. However, the actual attack or disruption occurred earlier, in the acoustically encoded signal, and remains invisible to subsequent text-based filters.
2. The Client Illusion: Inputs Seem Already Checked, But Often Insufficiently So:
Many systems rely on local checks on the client side or during the initial acceptance of data. These include, for example, volume limits for audio recordings, size limits for file uploads, or validation of file formats.
Attackers, however, can often bypass such client-side or superficial checks. They could:
Send Base64 encoded audio data directly to the backend via an API, which never passed through a real microphone.
Inject manipulated image data as part of a WebSocket stream that undergoes different checking mechanisms.
Subvert format checks through targeted byte-level manipulations of files, so that the file formally still appears valid but carries malicious content.
The AI system's API then receives seemingly valid text or data. However, their actual origin and integrity often remain unchecked or are assumed as given. The architecture in such cases is blind to everything except the seemingly clean end product of the upstream translation or conversion processes.
3. The Translator as a Potential Semantic Short Circuit:
OCR and ASR systems are not neutral, error-free transmitters of information. They actively transform data and, in doing so, can themselves become targets of manipulation or generate errors through their own functioning, which are then misinterpreted by the AI model.
An illustrative example would be a synthetically generated .mp3 stream. This could contain such structured frequency sequences or acoustic artifacts that a Speech-to-Text system transcribes them incorrectly, even if no understandable word was spoken.
The result could then be sentence fragments like "drop user table" or "reset admin settings." This is not a genuine, intended voice command and no malicious intent in the audio signal itself, but rather a structure that is mistakenly interpreted as a command during the translation process.
The downstream AI, which only sees the flawed transcript, may not recognize any anomaly and make decisions or take actions based on it.
It is important to understand here that this is not a classic SQL injection attack in the conventional sense. It is rather a case of semantic mistranscription, provoked by a targeted shaping of the audio input signal to exploit the weaknesses or peculiarities of the ASR system.
The crucial weakness often lies not in the text itself that is finally presented to the AI model, but in its hidden origin and the insufficiently checked translation process that produced it.
We often treat transcripts from OCR and ASR systems as if they were direct, trustworthy user inputs. In doing so, we ignore the complex and potentially error-prone transformation steps that preceded these texts.
However, it is precisely in these translation processes that the most dangerous distortions and vulnerabilities often arise. They are invisible to downstream filters, appear technically valid, but can be semantically toxic or manipulative.
"The text was clean. But the path to it was poisoned."
To minimize the risks from the translator trap, robust security measures are needed at multiple levels of the processing chain:
1. Implementation of Pre-Translation Scanning: Understanding Raw Data Before It's Allowed to Speak:
This includes byte-based entropy checking of image or audio data to detect unusual or manipulated file structures. It involves pattern analysis for atypical frequency clusters, signs of LSB steganography (Least Significant Bit interference patterns), or visual text distortions in images aimed at OCR manipulation. So-called "decoder guardians" could detect structural deviations or anomalies even before the actual translation, for example, via deep fingerprinting of raw data, by analyzing the entropy profile, or through advanced signature analyses.
2. Consistent Sandboxing of Translator Modules (ASR and OCR):
All ASR and OCR processes should run in isolated, resource-constrained environments (sandboxes). Important metadata from these processes, such as confidence scores for recognition quality, anomaly indicators, or decoder warnings, must mandatorily be passed on to subsequent system components. Critical or seemingly unsafe transcripts must not simply be passed on blindly but must undergo active validation or human review.
3. Ensuring Origin Transparency Throughout the System Flow:
Every text fragment that flows into processing by the AI model must receive a clear and immutable contextual origin stamp. This could, for example, look like this: text: "drop all logs" [source=ASR_module_XYZ, confidence_score=0.41, original_audio_file_ID=xyz123, input_channel=virtual_device_02].
Security modules in the AI core must then be able to differentiate their responses and filter mechanisms based on this origin information. All input paths and transformation steps must also be seamlessly and audit-proofly logged to ensure subsequent traceability and analysis of security incidents.
By indiscriminately converting everything into text in multimodal AI systems to process it uniformly, we often lose control over the original meaning and integrity of the information.
What looks like harmless, understandable language can be the result of profound structural deception at an earlier processing stage.
The AI believes the text it is told. But it often no longer knows who or what originally said this text, where it really came from, or why it suddenly sounds so plausible and action-relevant.
That is precisely what makes the seemingly neutral translation process the most perfidious and often least regarded attack vector in AI security today.
Uploaded on 29. May. 2025