arXiv AI By Zhiwei Xu, Xuyang Liu, Xiaolin Peng, Weixian Deng, Jiabao Gao, Zirui Li, Tian Qiu, Boya Zhou, Wei Jiao, Hai Wan, Xibin Zhao

TrustJev: Separating Policy Authority from Untrusted Evidence in LLM-Based Decisions

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arXiv Computation and Language
Oct 19

Identify, Classify, Act: Per-Instruction Controlled Compliance for LLMs over External Data

The paper introduces EDIC, a framework for per‑instruction compliance in large language models that interact with external data. It decomposes the task into identifying, classifying, and acting on each embedded directive, using a scene‑grounded pipeline and a generative judge trained with GRPO to attribute compliance at the instruction level. Experiments on two backbones and five benchmarks show that EDIC improves safety and utility, achieving up to a 7.7% increase in harmful‑rejection and a 12.5% boost in Arena‑Hard‑Auto utility on Qwen3‑4B.

By Zesheng Shi, Xue Li, Chenrui Dai, Jing Li
arXiv AI
Oct 9

LTBD: Learnable Trust-Boundary Delimiters for Prompt Injection Defense

The paper introduces Learnable Trust‑Boundary Delimiters (LTBD), a lightweight defense that embeds learnable delimiters into LLM inputs to explicitly mark trusted user instructions versus untrusted external data. LTBD keeps the model’s parameters unchanged, achieving superior performance over inference‑time defenses and matching training‑based methods while adding negligible inference overhead. Experiments show near‑zero attack success rates on AlpacaFarm and TaskTracker, and the method remains robust even against adaptive attackers who know the defense.

By Luman Zhao, Minghui Xu, Yue Zhang, Yijun Yang
arXiv AI
Aug 24

Trustworthy RAG: An Evaluation Agent for Detecting Misinformation and Knowledge Poisoning in Generative AI Systems

The paper introduces Trustworthy RAG, an evaluation agent designed to detect misinformation and knowledge poisoning in Retrieval-Augmented Generation systems. It combines natural language inference verification, a five-signal poison detector, and a weighted Trust Index to assess the reliability of retrieved content. Experiments on multiple LLMs show high accuracy and precision, with the agent effectively blocking unsafe advice in a secure-coding assistant scenario.

By Balkrishna Giri, Md Toufique Hasan, Jussi Rasku, Muhammad Waseem, Pekka Abrahamsson
arXiv AI
Sep 24

Policy-as-Skill: Governed LLM Decision Support with Evidence, Deterministic Control, and Audit

The paper introduces Policy-as-Skill (PaS), a modular runtime that packages evidence validation, review routing, version control, and auditability as executable, versioned policy capabilities for large language models. PaS was evaluated on 600 development tasks using a Gemma4 backend, achieving 53.8% exact accuracy and high audit completeness, and deterministic control further improves aggregate accuracy to 61.2% in a task‑dependent manner.

By Kabeh Mohsenzadegan, Vahid Tavakkoli, Kyandoghere Kyamakya