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Aggregate arXiv cs.AI 人工智能 28 Aug 2026 - 15:30

Approved Too Late: Verdict Staleness in LLM-Guarded Self-Adaptive Systems

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关键摘要

arXiv:2608.…

  • 26306v1 Announce Type: new Abstract: A large language model (LLM) guar…
  • This creates an Execute-stage time-of-check to time-of-use (TOCTOU) ha…
  • We study verdict freshness: whether a guardrail verdict remains valid …

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正文提要

arXiv:2608.26306v1 Announce Type: new Abstract: A large language model (LLM) guardrail for a self-adaptive system (SAS) may issue an approval that is correct at check time but stale by actuation. This creates an Execute-stage time-of-check to time-of-use (TOCTOU) hazard. We study verdict freshness: whether a guardrail verdict remains valid when used. We distinguish three quantities that answer different questions: all-candidate verdict change under fixed-action replay, oracle-labeled approval expiry on recorded closed-loop trajectories, and judge-conditioned use-time invalidity. Across five reproducible SAS environments, all-candidate verdict-change rates span 5.3-48.4% at a common replay shift of eight simulator steps. We introduce the Freshness-Bounded Shield (FBS), which estimates each approval's validity horizon from its safe-side margin and recent feature volatility, without an explicit plant-dynamics model. Using fixed settings documented in the artifact, FBS reduces oracle-labeled approval-expiry rates from 3.4-24.7% to 0-1.8% at the same shift. A separate audit of four LLM judges finds nonzero judge-conditioned use-time invalidity in every approval stream. We formulate a freshness contract: every approval must be correct at check time and remain valid at use time.

来源:https://arxiv.org/abs/2608.26306

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