SATRA: Semantic-State Auditing for Transition and Risk Analysis in Persistent Memory

Persistent-memory writers consolidate ordered evidence with different security-relevant semantic roles. The resulting persistent meaning may differ from the semantics of any individual input item. Event-level observations such as delivery, write success, retrieval, and unsafe behavior do not by themselves reveal the security-relevant meaning that a writer makes persistent. We introduce SATRA, an auditable framework that makes writer-level persistent meaning an explicit object of security analysis and uses persistent semantic state to analyze formation and transitions under memory reuse. SATRA distinguishes input semantic roles from persistent semantic states. A formed artifact is audited as poison-supporting (P), corrective (C), unresolved (U), or neutral (N), while no write is recorded separately when no artifact is formed. Across the evaluated model profiles, controlled formation experiments show that different ordered evidence compositions are associated with different post-write audit-outcome distributions. The same operational vocabulary covers all reviewed outcomes in the evaluated profiles and is reproducible under independent annotation. In explicit private-memory chains, an upstream artifact becomes evidence for a downstream writer, allowing $P/U$ retention, semantic repair, correction preservation, and omission to be observed across linked writing boundaries. Controlled activation further shows that audited state and artifact visibility are associated with different reviewed behavior rates. Together, these results support persistent semantic state as an auditable intermediate representation connecting evidence composition, memory-writing outcomes, reuse transitions, and downstream availability and behavior.