{"paper":{"title":"Mystra: Declarative Dynamic Taint Analysis via Shadow Virtual Machine","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.SE"],"primary_cat":"cs.PL","authors_text":"Junkun Liu, Rui Yang, Yinzhi Cao, Zhuohao Zhang, Ziyang Li","submitted_at":"2026-07-14T03:31:14Z","abstract_excerpt":"Dynamic taint analysis (DTA) for interpreted languages like JavaScript and Python requires three capabilities: observing host-runtime operations, maintaining parallel taint states, and defining how taint propagates. Existing systems couple these capabilities within an instrumentation mechanism -- source-rewriting or engine-native -- either incurring high runtime overhead or demanding engine-specific embeddings. There is yet to be a runtime-independent abstraction of a general DTA that separates taint semantics and state transitions from how a host runtime observes and executes them.\n  We set o"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.12308","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2607.12308/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}