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An automated, feedback-guided attack generation pipeline is built and applied to Claude Code and Codex in their native runtimes, showing that production agents are vulnerable to context-aware, multi-step injection over long horizons.
Conformal Privacy Auditing is introduced, a distribution-free calibration framework that provides a statistical certificate of re-identification risk for each released document against LLM-empowered adversaries and enables audits of open-source models and proprietary API models in a unified framework.
A reverse-training framework is introduced that weakens the trigger-target association, producing low-ASR backdoor models while preserving clean-input performance and exposing a fundamental attacker-defender asymmetry in existing defense paradigms.
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