Unverified50% confidenceFactExact time
For DICOM files, Aegis cleans identifiable metadata and checks for burned-in text in pixels that metadata rules cannot remove; OCR detects candidate text regions, NER helps classify sensitive entities, and policy rules determine which content needs to be masked, whitelisted or sent for manual review.
1
Sources
50%
Confidence
Long-term
Relevance
10/15/2026
First Seen
Sources
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Related Claims
UnverifiedA standard chunker will accept scanned documents wholesale, splitting pages, computing embeddings for the text output by the OCR layer and storing them in the vector database, so that embeddings of noisy text still land near the correct region of the vector space, chunk cosine similarity during retrieval looks normal, and the generator gives confident answers on a contaminated premise, with the error happening silently.64% similarUnverifiedWrapping retrieved text in clearly delimited, labeled blocks and explicitly telling the model that the content within those blocks is data rather than instructions can eliminate the majority of the prompt injection attack surface.62% similarUnverifiedDrawing a clear boundary between the proposal language and the evaluator cuts off the attribution contamination path through which an agent could inflate scores by relaxing evaluation criteria or adjusting data splits.61% similar
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