Tootfinder

Semantics-Aligned, Curriculum-Driven, and Reasoning-Enhanced Vulnerability Repair Framework
Chengran Yang, Ting Zhang, Jinfeng Jiang, Xin Zhou, Haoye Tian, Jieke Shi, Junkai Chen, Yikun Li, Eng Lieh Ouh, Lwin Khin Shar, David Lo
https://arxiv.org/abs/2510.01002

Semantics-Aligned, Curriculum-Driven, and Reasoning-Enhanced Vulnerability Repair Framework
Current learning-based Automated Vulnerability Repair (AVR) approaches, while promising, often fail to generalize effectively in real-world scenarios. Our diagnostic analysis reveals three fundamental weaknesses in state-of-the-art AVR approaches: (1) limited cross-repository generalization, with performance drops on unseen codebases; (2) inability to capture long-range dependencies, causing a performance degradation on complex, multi-hunk repairs; and (3) over-reliance on superficial lexical p…

Weaknesses of each one-loss team, plus Week 6 fantasy prep https://www.nytimes.com/athletic/6698191/2025/10/07/nfl-weaknesses-2025-contenders-bills-colts-scoop-city/

Amazon unveils its Autonomous Threat Analysis system, born from a 2024 internal hackathon, to use AI agents competing in teams to identify vulnerabilities (Lily Hay Newman/Wired)
https://www.wired.com/story/amazon-autonomous-threat-analysis/

Amazon Is Using Specialized AI Agents for Deep Bug Hunting
Born out of an internal hackathon, Amazon’s Autonomous Threat Analysis system uses a variety of specialized AI agents to detect weaknesses and propose fixes to the company’s platforms.

Reid's debut NFL draft QB Hot Board: Strengths and weaknesses for the top 14 passers https://www.espn.com/nfl/draft2026/story/_/id/46532548/2026-nfl-draft-ranking-top-quarterbacks-hot-board-team-fits-streng…

...normally that won’t be the case, even when the person seems nearly totally to blame (and that isn’t even a very common situation) normally there are obvious weaknesses in the system that put them in the place to fail and will likely put anyone else in the same place in the future.

Security and Privacy Assessment of U.S. and Non-U.S. Android E-Commerce Applications
Urvashi Kishnani, Sanchari Das
https://arxiv.org/abs/2510.12031 https://

Security and Privacy Assessment of U.S. and Non-U.S. Android E-Commerce Applications
E-commerce mobile applications are central to global financial transactions, making their security and privacy crucial. In this study, we analyze 92 top-grossing Android e-commerce apps (58 U.S.-based and 34 international) using MobSF, AndroBugs, and RiskInDroid. Our analysis shows widespread SSL and certificate weaknesses, with approximately 92% using unsecured HTTP connections and an average MobSF security score of 40.92/100. Over-privileged permissions were identified in 77 apps. While U.S. …

Neighborhood-Adaptive Generalized Linear Graph Embedding with Latent Pattern Mining
S. Peng, L. Hu, W. Zhang, B. Jie, Y. Luo
https://arxiv.org/abs/2510.05719 https://

Neighborhood-Adaptive Generalized Linear Graph Embedding with Latent Pattern Mining
Graph embedding has been widely applied in areas such as network analysis, social network mining, recommendation systems, and bioinformatics. However, current graph construction methods often require the prior definition of neighborhood size, limiting the effective revelation of potential structural correlations in the data. Additionally, graph embedding methods using linear projection heavily rely on a singular pattern mining approach, resulting in relative weaknesses in adapting to different …

1 Drive Explains Raiders' Offensive Struggles https://www.si.com/nfl/raiders/las-vegas-zaire-franklin-pete-carroll-geno-smiith

TRADE: Cowboys land potential impact defender Logan Wilson https://insidethestar.com/trade-cowboys-land-potential-impact-defender-logan-wilson

InfoMosaic-Bench: Evaluating Multi-Source Information Seeking in Tool-Augmented Agents
Yaxin Du, Yuanshuo Zhang, Xiyuan Yang, Yifan Zhou, Cheng Wang, Gongyi Zou, Xianghe Pang, Wenhao Wang, Menglan Chen, Shuo Tang, Zhiyu Li, Siheng Chen
https://arxiv.org/abs/2510.02271

InfoMosaic-Bench: Evaluating Multi-Source Information Seeking in Tool-Augmented Agents
Information seeking is a fundamental requirement for humans. However, existing LLM agents rely heavily on open-web search, which exposes two fundamental weaknesses: online content is noisy and unreliable, and many real-world tasks require precise, domain-specific knowledge unavailable from the web. The emergence of the Model Context Protocol (MCP) now allows agents to interface with thousands of specialized tools, seemingly resolving this limitation. Yet it remains unclear whether agents can ef…

Type, Ability, and Effect Systems: Perspectives on Purity, Semantics, and Expressiveness
Yuyan Bao, Tiark Rompf
https://arxiv.org/abs/2510.07582 https://ar…

Type, Ability, and Effect Systems: Perspectives on Purity, Semantics, and Expressiveness
Programming benefits from a clear separation between pure, mathematical computation and impure, effectful interaction with the world. Existing approaches to enforce this separation include monads, type-and-effect systems, and capability systems. All share a tension between precision and usability, and each one has non-obvious strengths and weaknesses. This paper aims to raise the bar in assessing such systems. First, we propose a semantic definition of purity, inspired by contextual equivalen…

LegalSim: Multi-Agent Simulation of Legal Systems for Discovering Procedural Exploits
Sanket Badhe
https://arxiv.org/abs/2510.03405 https://arxiv.org/pdf/2…

LegalSim: Multi-Agent Simulation of Legal Systems for Discovering Procedural Exploits
We present LegalSim, a modular multi-agent simulation of adversarial legal proceedings that explores how AI systems can exploit procedural weaknesses in codified rules. Plaintiff and defendant agents choose from a constrained action space (for example, discovery requests, motions, meet-and-confer, sanctions) governed by a JSON rules engine, while a stochastic judge model with calibrated grant rates, cost allocations, and sanction tendencies resolves outcomes. We compare four policies: PPO, a co…

MALF: A Multi-Agent LLM Framework for Intelligent Fuzzing of Industrial Control Protocols
Bowei Ning, Xuejun Zong, Kan He
https://arxiv.org/abs/2510.02694 https://

MALF: A Multi-Agent LLM Framework for Intelligent Fuzzing of Industrial Control Protocols
Industrial control systems (ICS) are vital to modern infrastructure but increasingly vulnerable to cybersecurity threats, particularly through weaknesses in their communication protocols. This paper presents MALF (Multi-Agent LLM Fuzzing Framework), an advanced fuzzing solution that integrates large language models (LLMs) with multi-agent coordination to identify vulnerabilities in industrial control protocols (ICPs). By leveraging Retrieval-Augmented Generation (RAG) for domain-specific knowle…

"Your Doctor is Spying on You": An Analysis of Data Practices in Mobile Healthcare Applications
Luke Stevenson, Sanchari Das
https://arxiv.org/abs/2510.06015 https://

"Your Doctor is Spying on You": An Analysis of Data Practices in Mobile Healthcare Applications
Mobile healthcare (mHealth) applications promise convenient, continuous patient-provider interaction but also introduce severe and often underexamined security and privacy risks. We present an end-to-end audit of 272 Android mHealth apps from Google Play, combining permission forensics, static vulnerability analysis, and user review mining. Our multi-tool assessment with MobSF, RiskInDroid, and OWASP Mobile Audit revealed systemic weaknesses: 26.1% request fine-grained location without disclosu…