The Mirage of Breaking MIRAGE: Analyzing the Modeling Pitfalls in Emerging “Attacks” on MIRAGE
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 letter studies common modeling pitfalls in security analyses of hardware defenses to highlight the importance of accurate reproduction of defenses. We provide a case study of MIRAGE (Saileshwar and Qureshi 2021), a defense against cache side channel attacks, and analyze its incorrect modeling in a recent work (Chakraborty et al., 2023) that claimed to break its security. We highlight several modeling pitfalls that can invalidate the security properties of any defense including a) incomplete modeling of components critical for security, b) usage of random number generators that are insufficiently random, and c) initialization of system to improbable states, leading to an incorrect conclusion of a vulnerability, and show how these modeling bugs incorrectly cause set conflicts to be observed in a recent work’s (Chakraborty et al., 2023) model of MIRAGE. We also provide an implementation addressing these bugs that does not incur set-conflicts, highlighting that MIRAGE is still unbroken.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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