Unverified50% confidenceFactExact time
Traditional distillation usually requires access to the teacher model's full output probabilities (logits), but in black-box API scenarios attackers can only obtain the final text, so it has evolved into "black-box distillation"—instruction fine-tuning on large numbers of input-output pairs.
1
Sources
50%
Confidence
Long-term
Relevance
10/24/2026
First Seen
Sources
OpenCode Hit by Large-Scale Attack: Distillation Attacks and the New AI Security Tax
youtubeThe PrimeTime10/24/2026
Related Entities
Related Claims
UnverifiedIn traditional white-box distillation, the student model can directly access the complete probability distribution (logits) of the teacher model's output layer, whereas commercial large language model APIs typically return only the final generated text or top-k token probabilities.77% similarUnverifiedClosed-source models only allow inference at the black-box level of inputs and outputs, whereas open weights, training details, and internal mechanisms make the causes of anomalous behavior explainable.70% similarVerifiedHard distillation refers to the student model directly learning the final output labels or generated text of the teacher model, while soft distillation requires the student model to learn the complete probability distribution (logits) output by the teacher model.68% similarUnverifiedIn BLACKBOX, the language model is responsible for choosing tools, interpreting graph query results, synthesizing evidence, and generating fault explanations, but the graph always remains the source of operations facts.65% similarUnverifiedIn confidence-gated distillation, the distillation signal for a sample is only incorporated into training when the teacher model's prediction confidence for that sample exceeds a threshold.64% similar
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