Tootfinder

Capelli identity and contiguity relations of Radon hypergeometric function on the Grassmannian
Hironobu Kimura
https://arxiv.org/abs/2509.25900 https://arx…

Capelli identity and contiguity relations of Radon hypergeometric function on the Grassmannian
The Radon hypergeometric function (Radon HGF) F(z;\a) of type łon the Grassmannian Gr(m,N), N=rn for some integers r,n>0, is defined as a Radon transform of the character of the universal covering of the Lie group H_ł\subset GL(N) specified by a partition łof n, where \a \in C^{n} is the parameter to in the character. For this Radon HGF, we give the contiguity relations of the form L^{(i,j)}F(z;\a)=\b(\a_{0}^{(j)})F(z;\a+\e^{(i)}-\e^{(j)}) with a differential operator L^{(i,j)} of order r an…

Beyond Western Politics: Cross-Cultural Benchmarks for Evaluating Partisan Associations in LLMs
Divyanshu Kumar, Ishita Gupta, Nitin Aravind Birur, Tanay Baswa, Sahil Agarwal, Prashanth Harshangi
https://arxiv.org/abs/2509.22711

Beyond Western Politics: Cross-Cultural Benchmarks for Evaluating Partisan Associations in LLMs
Partisan bias in LLMs has been evaluated to assess political leanings, typically through a broad lens and largely in Western contexts. We move beyond identifying general leanings to examine harmful, adversarial representational associations around political leaders and parties. To do so, we create datasets \textit{NeutQA-440} (non-adversarial prompts) and \textit{AdverQA-440} (adversarial prompts), which probe models for comparative plausibility judgments across the USA and India. Results show …

Servant, Stalker, Predator: How An Honest, Helpful, And Harmless (3H) Agent Unlocks Adversarial Skills
David Noever
https://arxiv.org/abs/2508.19500 https://

Servant, Stalker, Predator: How An Honest, Helpful, And Harmless (3H) Agent Unlocks Adversarial Skills
This paper identifies and analyzes a novel vulnerability class in Model Context Protocol (MCP) based agent systems. The attack chain describes and demonstrates how benign, individually authorized tasks can be orchestrated to produce harmful emergent behaviors. Through systematic analysis using the MITRE ATLAS framework, we demonstrate how 95 agents tested with access to multiple services-including browser automation, financial analysis, location tracking, and code deployment-can chain legitimat…

Microsoft Azure/Cloud/AD considered harmful (twice, again)...
Context: https://cyberplace.social/@GossiTheDog/115220941705031025 and

Is the Solar System a Wilderness or a Construction Site? Conservationist and Constructivist Paradigms in Planetary Protection
Luk\'a\v{s} Likav\v{c}an
https://arxiv.org/abs/2508.20145

Is the Solar System a Wilderness or a Construction Site? Conservationist and Constructivist Paradigms in Planetary Protection
Outer space exploration is one of the most prominent domains of earth-space governance. In this context, multiple policy documents by the UN, NASA, or Committee on Space Research (COSPAR) pledge to protect extraterrestrial environments from harmful human influence under the framework of planetary protection or planetary stewardship, understood primarily as the isolation of other celestial bodies from possible biological contamination. This paper analyses justifications of this framework that re…

Context Matters: Incorporating Target Awareness in Conversational Abusive Language Detection
Raneem Alharthi, Rajwa Alharthi, Aiqi Jiang, Arkaitz Zubiaga
https://arxiv.org/abs/2508.12828

Context Matters: Incorporating Target Awareness in Conversational Abusive Language Detection
Abusive language detection has become an increasingly important task as a means to tackle this type of harmful content in social media. There has been a substantial body of research developing models for determining if a social media post is abusive or not; however, this research has primarily focused on exploiting social media posts individually, overlooking additional context that can be derived from surrounding posts. In this study, we look at conversational exchanges, where a user replies t…

OpenAI raised concerns about anti-competitive conduct by "entrenched companies" in a September EU meeting; source: OpenAI targeted Google, Microsoft, and Apple (Samuel Stolton/Bloomberg)
https://www.bloomberg.com/news/articles/20

OpenAI Warns EU Antitrust Watchdogs of Big Tech’s Data Dominance
OpenAI has raised concerns with European Union antitrust enforcers over potentially harmful conduct by the likes of Alphabet Inc.’s Google, Microsoft Corp. and Apple Inc.

Context Misleads LLMs: The Role of Context Filtering in Maintaining Safe Alignment of LLMs
Jinhwa Kim, Ian G. Harris
https://arxiv.org/abs/2508.10031 https://

Context Misleads LLMs: The Role of Context Filtering in Maintaining Safe Alignment of LLMs
While Large Language Models (LLMs) have shown significant advancements in performance, various jailbreak attacks have posed growing safety and ethical risks. Malicious users often exploit adversarial context to deceive LLMs, prompting them to generate responses to harmful queries. In this study, we propose a new defense mechanism called Context Filtering model, an input pre-processing method designed to filter out untrustworthy and unreliable context while identifying the primary prompts contai…

Safe and Efficient In-Context Learning via Risk Control
Andrea Wynn, Metod Jazbec, Charith Peris, Rinat Khaziev, Anqi Liu, Daniel Khashabi, Eric Nalisnick
https://arxiv.org/abs/2510.02480

Safe and Efficient In-Context Learning via Risk Control
Large language models (LLMs) demonstrate a remarkable ability to learn new tasks from a few in-context examples. However, this flexibility introduces safety concerns: LLMs can be influenced by incorrect or malicious demonstrations -- for example, if an adversary tampers with or injects harmful examples without a human supervisor noticing. This motivates principled designs in which the system itself includes built-in mechanisms to guard against such attacks. We propose a novel approach to limit …

Are LLMs Enough for Hyperpartisan, Fake, Polarized and Harmful Content Detection? Evaluating In-Context Learning vs. Fine-Tuning
Michele Joshua Maggini, Dhia Merzougui, Rabiraj Bandyopadhyay, Ga\"el Dias, Fabrice Maurel, Pablo Gamallo
https://arxiv.org/abs/2509.07768

Are LLMs Enough for Hyperpartisan, Fake, Polarized and Harmful Content Detection? Evaluating In-Context Learning vs. Fine-Tuning
The spread of fake news, polarizing, politically biased, and harmful content on online platforms has been a serious concern. With large language models becoming a promising approach, however, no study has properly benchmarked their performance across different models, usage methods, and languages. This study presents a comprehensive overview of different Large Language Models adaptation paradigms for the detection of hyperpartisan and fake news, harmful tweets, and political bias. Our experimen…