Runtime Verification of k-Safety Hyperproperties in HyperLTL
Bibliographic record
Abstract
This paper introduces a novel runtime verification technique for a rich sub-class of Clarkson and Schneider's hyperproperties. The primary application of such properties is in expressing security policies (e.g., information flow) that cannot be expressed in trace-based specification languages (e.g., LTL). First, to incorporate syntactic means, we draw connections between safety and co-safety hyperproperties and the temporal logic HYPERLTL, which allows explicit quantification over multiple executions. We also define the notion of monitorability in HYPERLTL and identify classes of monitorable HYPERLTL formulas. Then, we introduce an algorithm for monitoring k-safety and co-k-safety hyperproperties expressed in HYPERLTL. Our technique is based on runtime formula progression as well as on-the-fly monitor synthesis across multiple executions. We analyze different performance aspects of our technique by conducting thorough experiments on monitoring security policies for information flow and observational determinism on a real-world location-based service dataset as well as synthetic trace sets.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".