arXiv subjects

Haochang Hao

Publications and source records attributed to Haochang Hao.

3 recordsLinked to original sources

arXiv2026

Poise: Position-Aware One-Instruction Skill Injection for Silent Execution on LLM Agents

Agent skills extend general-purpose agents, but their open format enables skill poisoning: a tampered skill can make an agent run an attacker's command while completing the user's legitimate task. Invocation alone is insufficient; the attack-specific action must complete while that task still passes its verifier. We therefore define Attack Success Rate (ASR) to require a postcondition-validated sandbox action and a passing task verifier in the same trial. Skill files expose a reliability-visibility trade-off between a preloaded but conspicuous YAML frontmatter block and a longer body, where arbitrary placement may be skipped or locally incongruent. We introduce Poise, a position-aware attack that uses context-aware generation to place exactly one benign-looking, command-bearing instruction at a structurally feasible body position. On the eligible Skill-Inject pool with codex+gpt-5.2, Poise achieves 89.3\% ASR, 28.0 points above a context-free random-placement body baseline and comparable to the 86.7\% ASR of a high-exposure YAML-only baseline. Under the SkillTester audit, four LLM judges falsely flag 74.6\% of clean skills on average across both benchmarks, while only 5.6\% of Poise variants gain a new high-risk alert over their clean counterparts. One locally plausible, command-bearing body instruction therefore matches YAML-level reliability, while the resulting poisoned skill seldom adds a new high-risk finding over its clean counterpart.

cs.CR↗

arXiv2026

Verifiable Rewards Beyond Math and Code: Lightweight Corpus-Grounded Process Supervision for Factual Question Answering

Applying reinforcement learning to improve factual accuracy in knowledge-intensive question answering faces a reward design dilemma. Response-level rewards provide only coarse supervision and cannot distinguish correct from incorrect statements within a reasoning trace. Sentence-level alternatives offer finer-grained feedback, but typically rely on NLI verifiers, LLM judges, or knowledge-verification pipelines that are expensive to deploy at RL scale and often unreliable for rare-entity facts, where accurate reward signals are especially important. We propose CorVer (Corpus Verify), a lightweight, plug-in-ready process reward that replaces neural verifiers with a corpus-grounded signal derived from Wikipedia co-occurrence statistics. CorVer assigns sentence-level credit and maps it to token-level advantages via a simple alignment, requiring only a 0.5B extractor and a single corpus lookup per sentence. Across 30 (model, benchmark) cells spanning six instruction-tuned models (3B to 14B) and five QA benchmarks, CorVer improves over the raw baseline for every cell, with an average TriviaQA gain of +4.1 pp. It also outperforms four neural-verifier baselines in 18 of 20 cells under their feasible configurations, while training 4.8 to 8.4x faster.

cs.CL↗

arXiv2026

SafeCRS: Personalized Safety Alignment for LLM-Based Conversational Recommender Systems

Current LLM-based conversational recommender systems (CRS) primarily optimize recommendation accuracy and user satisfaction. We identify an underexplored vulnerability in which recommendation outputs may negatively impact users by violating personalized safety constraints, when individualized safety sensitivities -- such as trauma triggers, self-harm history, or phobias -- are implicitly inferred from the conversation but not respected during recommendation. We formalize this challenge as personalized CRS safety and introduce SafeRec, a new benchmark dataset designed to systematically evaluate safety risks in LLM-based CRS under user-specific constraints. To further address this problem, we propose SafeCRS, a safety-aware training framework that integrates Safe Supervised Fine-Tuning (Safe-SFT) with Safe Group reward-Decoupled Normalization Policy Optimization (Safe-GDPO) to jointly optimize recommendation quality and personalized safety alignment. Extensive experiments on SafeRec demonstrate that SafeCRS reduces safety violation rates by up to 96.5% relative to the strongest recommendation-quality baseline while maintaining competitive recommendation quality. Warning: This paper contains potentially harmful and offensive content.

cs.CL↗