SENSE: Enhancing Microarchitectural Awareness for TEEs via Subscription-Based Notification
Bibliographic record
Abstract
Effectively mitigating side-channel attacks (SCAs) in Trusted Execution Environments (TEEs) remains challenging despite advances in existing defenses.Current detection-based defenses hinge on observing abnormal victim performance characteristics but struggle to detect attacks leaking smaller portions of the secret across multiple executions.Limitations of existing detectionbased defenses stem from various factors, including the absence of a trusted microarchitectural data source in TEEs, low-quality available data, inflexibility of victim responses, and platformspecific constraints.We contend that the primary obstacles to effective detection techniques can be attributed to the lack of direct access to precise microarchitectural information within TEEs.We propose SENSE, a solution that actively exposes underlying microarchitectural information to userspace TEEs.SENSE enables userspace software in TEEs to subscribe to fine-grained microarchitectural events and utilize the events as a means to contextualize the ongoing microarchitectural states.We initially demonstrate SENSE's capability by applying it to defeat the state-of-the-art cache-based side-channel attacks.We conduct a comprehensive security analysis to ensure that SENSE does not leak more information than a system without it does.We prototype SENSE on a gem5-based emulator, and our evaluation shows that SENSE is secure, can effectively defeats cache SCAs, and incurs negligible performance overhead (1.2%) under benign situations.
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How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".