Cascade Error Correction Attack; Exploiting Implicit and Side Channel Information Leakage
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This work presents a complete mathematical model of a novel cryptanalytic attack that combines the Cascade error correction leakages with the side channel information leakage to construct a more powerful attack than either of these two launched alone. We find that a higher Quantum Bit Error Rate (QBER) leaves Cascade more vulnerable to reliable information being extracted from side channel leakage and as such, reliable complete key recovery. For a key length of 1024 bits and QBER of 0.2, on any device where the ratio between the difference of noiseless power consumption levels of an XOR function outputting 0 or 1, and power consumption noise, is greater than 1.7, a full key recovery is expected. For lower QBER, we see that for the ratio of the difference of noiseless power consumption and power consumption noise must higher in order to successfully recover the key.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it