Another Mirage of Breaking MIRAGE: Debunking Occupancy-Based Side-Channel Attacks on Fully Associative Randomized Caches
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
A recent work presented at USENIX Security 2025, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Systematic Evaluation of Randomized Cache Designs against Cache Occupancy (RCO)</i>, claims that cache-occupancy-based side-channel attacks can recover AES keys from the MIRAGE randomized cache. In this paper, we examine these claims and find that they arise from a flawed modeling of randomized caches in RCO. Critically, we find that the security properties of randomized caches strongly depend on the seeding methodology used to initialize random number generators (RNG) used in these caches. RCO's modeling uses a constant seed to initialize the cache RNGs for each simulated AES encryption, causing every simulated AES encryption to artificially evict the same sequence of cache lines. This departs from accurate modeling of such randomized caches, where eviction sequences vary randomly for each program execution. We observe that an accurate modeling of such randomized caches, where the RNG seed is randomized in each simulation, causes correlations between AES T-table accesses and attacker observations to disappear, and the attack to fail. These findings show that the previously claimed leakages are due to flawed modeling and that with correct modeling, MIRAGE does not leak AES keys via occupancy based side-channels.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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