System Capybara: Tracking Capabilities for Separation and Freshness (Extended Version)

2026-07-13T08:52:10Z096b9edc87af7e78ffb01e502b283d421a86f28372144820b115b30a51df9a8c
LLM-securityNetKATPython-LiquidScalacapture-checkingdata-race-freedomdenotational-semanticsfuzzinggrammar-inferenceinformation-flow-controlmemory-safetynetwork-analysisnoninterferenceprompt-injectionprovenancequantum-software-semanticssecure-programmingseparation-types

What happened

This collection of recent PL/security papers contains several items with direct security relevance. Key points: (1) “Toward Inferring Accurate Context-free Grammars…” (Xvada) reports discovery of a vulnerability in the widely used Python Liquid templating engine and additional bugs found via grammar-guided fuzzing; fixes were applied by the maintainers. (2) “The LLMbda Calculus” gives an executable, machine-checked calculus and interpreter that enforces provenance-based defenses against prompt-injection (dynamic information-flow labels, reclassification) and proves termination-insensitive non‑

Why it matters

A reviewed impact interpretation has not been published for this record.

Evidence and limitations

Source ID
arxiv_cs_pl
Record identifier
096b9edc87af7e78ffb01e502b283d421a86f28372144820b115b30a51df9a8c
Enrichment time
2026-07-13T08:52:10Z
AI-assisted enrichment
Yes

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