Tootfinder

Plug. Play. Persist. Inside a Ready-to-Go Havoc C2 Infrastructure
Alessio Di Santo
https://arxiv.org/abs/2507.00189 https://arxiv.org…

Plug. Play. Persist. Inside a Ready-to-Go Havoc C2 Infrastructure
This analysis focuses on a single Azure-hosted Virtual Machine at 52.230.23.114 that the adversary converted into an all-in-one delivery, staging and Command-and-Control node. The host advertises an out-of-date Apache 2.4.52 instance whose open directory exposes phishing lures, PowerShell loaders, Reflective Shell-Code, compiled Havoc Demon implants and a toolbox of lateral-movement binaries; the same server also answers on 8443/80 for encrypted beacon traffic. The web tier is riddled with publ…

Beyond Reactive Safety: Risk-Aware LLM Alignment via Long-Horizon Simulation
Chenkai Sun, Denghui Zhang, ChengXiang Zhai, Heng Ji
https://arxiv.org/abs/2506.20949

Beyond Reactive Safety: Risk-Aware LLM Alignment via Long-Horizon Simulation
Given the growing influence of language model-based agents on high-stakes societal decisions, from public policy to healthcare, ensuring their beneficial impact requires understanding the far-reaching implications of their suggestions. We propose a proof-of-concept framework that projects how model-generated advice could propagate through societal systems on a macroscopic scale over time, enabling more robust alignment. To assess the long-term safety awareness of language models, we also introd…

Efficient Multi-Camera Tokenization with Triplanes for End-to-End Driving
Boris Ivanovic, Cristiano Saltori, Yurong You, Yan Wang, Wenjie Luo, Marco Pavone
https://arxiv.org/abs/2506.12251

Efficient Multi-Camera Tokenization with Triplanes for End-to-End Driving
Autoregressive Transformers are increasingly being deployed as end-to-end robot and autonomous vehicle (AV) policy architectures, owing to their scalability and potential to leverage internet-scale pretraining for generalization. Accordingly, tokenizing sensor data efficiently is paramount to ensuring the real-time feasibility of such architectures on embedded hardware. To this end, we present an efficient triplane-based multi-camera tokenization strategy that leverages recent advances in 3D ne…

Partially Observable Residual Reinforcement Learning for PV-Inverter-Based Voltage Control in Distribution Grids
Sarra Bouchkati, Ramil Sabirov, Steffen Kortmann, Andreas Ulbig
https://arxiv.org/abs/2506.19353

Partially Observable Residual Reinforcement Learning for PV-Inverter-Based Voltage Control in Distribution Grids
This paper introduces an efficient Residual Reinforcement Learning (RRL) framework for voltage control in active distribution grids. Voltage control remains a critical challenge in distribution grids, where conventional Reinforcement Learning (RL) methods often suffer from slow training convergence and inefficient exploration. To overcome these challenges, the proposed RRL approach learns a residual policy on top of a modified Sequential Droop Control (SDC) mechanism, ensuring faster convergenc…