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arXiv 最近完成扫描：2026-11-15 03:37 · 全站最近入库：2026-11-14 15:09各信源进度 →

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新论文 · v1arXiv:2611.12155

StealthyPoison: Manipulating LLM Agent Behavior through Procedural Poisoning of Third-Party Skills

Jingfen Qiao, Rui Li, Shuaiqun Pan, Olga Gadyatskaya, Andrew Yates, Evangelos Kanoulas

原文摘要

LLM agents increasingly retrieve third-party skills from public registries and follow their procedures to complete user tasks. This reliance allows attackers to manipulate agent behavior by poisoning the skills. However, overtly malicious instructions can provide explicit signals for detection. We present StealthyPoison, a stealthy procedural poisoning attack that corrupts a skill's decision-critical rules to induce incorrect task outcomes while preserving its advertised functionality and normal execution. Unlike prior knowledge corruption attacks that craft poisoned content for known target question-answer pairs, StealthyPoison constructs reusable poisoned procedures from a publicly available third-party skill, without access to actual user requests or their ground-truth answers. To make the poisoned procedure generalize to unseen requests, it first synthesizes requests from the target skill and executes its clean version using a surrogate agent to obtain pseudo-references. These references and surrogate execution feedback guide the iterative refinement of payloads that alter decision-critical rules. To increase the likelihood that the poisoned skill is retrieved and selected over benign alternatives, the attack augments the poisoned skill's retrieval metadata with independently generated request descriptions. We evaluate StealthyPoison on 566 eligible target skills and 5,244 requests against more than 26,000 registry entries. It improves top-1 poisoned-skill placement by 13.6% relative to both HotFlip and GCG. Constructed using feedback from a single surrogate model, the procedural poisoning payloads transfer effectively and without modification to three held-out victim models and achieve end-to-end attack success in a Claude Code workflow powered by Opus 4.8. The defense evaluation further shows that most of these payloads evade detection by both SkillSpector and GPT-5.4-mini.

cs.CR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.37162

Coverage Under Contradiction: A Benchmark for Negation-Aware Retrieval

Pasha Abrishamchian, Eugene Yang, Andrew Yates, Nazli Goharian, Ophir Frieder

原文摘要

Coverage-evaluation benchmarks quantify whether retrieved evidence covers the different pieces of information needed to satisfy an information need. Existing benchmarks mostly consider factual claims that may already be known to pretrained retrieval components, leaving open whether retrievers find corpus evidence when it contradicts those priors. We study how retrievers behave when their parametric knowledge and the information in a corpus disagree. To do so, we develop a process to augment a nugget-based coverage benchmark with controlled contradictions. For each nugget, we create a negated counterpart and edit its supporting documents to support that negation while preserving the context. Applying this process to CoverageBench, a benchmark measuring information coverage, we create CoverageBench-Negate, spanning six datasets and roughly 63,000 original-edited evidence pairs (one per nugget-document link). We attach rich construction metadata to every pair and additionally provide out-of-family LLM judgments over all pairs and a human audit of a sample. In baseline experiments, BM25 separates original from edited near-twin evidence substantially better than dense retrievers (Qwen3 and Jina), which stay near chance. We release the benchmark on Hugging Face Datasets (nuggets, documents, pairs) together with the construction materials, including the prompts used to create it.

cs.IR · cs.CL

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arXiv ↗PDF ↗
首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1527ACL Anthology · ACL 2026

CommonLID: Re-evaluating State-of-the-Art Language Identification Performance on Web Data

Pedro Ortiz Suarez, Laurie Burchell Common Crawl Foundation, Catherine Arnett EleutherAI, Rafael Mosquera, Sara Hincapié Monsalve Factored, Thom Vaughan Common Crawl Foundation, Damian Stewart, Malte Ostendorff Common Crawl Foundation 等 97 位作者

原文摘要

Language identification (LID) is a fundamental step in curating multilingual corpora. However, LID models still perform poorly for many languages, especially on the noisy and heterogeneous web data often used to train multilingual language models. In this paper, we introduce CommonLID, a community-driven, human-annotated LID benchmark for the web domain, covering 109 languages. Many of the included languages have been previously under-served, making CommonLID a key resource for developing more representative high-quality text corpora. We show CommonLID’s value by using it, alongside five other common evaluation sets, to test eight popular LID models. We analyse our results to situate our contribution and to provide an overview of the state of the art. In particular, we highlight that existing evaluations overestimate LID accuracy for many languages in the web domain. We make CommonLID and the code used to create it available under an open, permissive license.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.587ACL Anthology · ACL 2026

Making Large Language Models Efficient Dense Retrievers

Yibin Lei University of Amsterdam, Shwai He University of Maryland, College Park, Ang Li University of Maryland, College Park and Duke University, Andrew Yates Johns Hopkins University

原文摘要

Recent work has shown that directly fine-tuning large language models (LLMs) for dense retrieval yields strong performance, but their substantial parameter counts make them computationally inefficient. While prior studies have revealed significant layer redundancy in LLMs for generative tasks, it remains unclear whether similar redundancy exists when these models are adapted for retrieval tasks, which require encoding entire sequences into fixed representations rather than generating tokens iteratively. To this end, we conduct a comprehensive analysis of layer redundancy in LLM-based dense retrievers. We find that, in contrast to generative settings, MLP layers are substantially more prunable, while attention layers remain critical for semantic aggregation. Building on this insight, we propose EffiR, a framework for developing efficient retrievers that performs large-scale MLP compression through a coarse-to-fine strategy (coarse-grained depth reduction followed by fine-grained width reduction), combined with retrieval-specific fine-tuning. Across diverse BEIR datasets and LLM backbones, EffiR achieves substantial reductions in model size and inference cost while preserving the performance of full-size models.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表