Tootfinder

This story is cute: A malicious "Solidity" (that's the smart contract language Ethereum and other blockchains use) extension for Cursor, the Vibe-Coding Editor included code that steals your tokens/coins.
I find it funny for two reasons:
- Blockchainers love talking about how you need to verify things you interact with but someone wasn't checking if they have the right extension
- Programming smart contracts is hard because it's a massively hostile envir…

Code highlighting with Cursor AI for $500,000
Kaspersky GReAT experts uncover malicious extensions for Cursor AI that download the Quasar backdoor and a crypto stealer.

Malicious LLM-Based Conversational AI Makes Users Reveal Personal Information
Xiao Zhan, Juan Carlos Carrillo, William Seymour, Jose Such
https://arxiv.org/abs/2506.11680

Malicious LLM-Based Conversational AI Makes Users Reveal Personal Information
LLM-based Conversational AIs (CAIs), also known as GenAI chatbots, like ChatGPT, are increasingly used across various domains, but they pose privacy risks, as users may disclose personal information during their conversations with CAIs. Recent research has demonstrated that LLM-based CAIs could be used for malicious purposes. However, a novel and particularly concerning type of malicious LLM application remains unexplored: an LLM-based CAI that is deliberately designed to extract personal infor…

Privacy-Preserving Federated Learning against Malicious Clients Based on Verifiable Functional Encryption
Nina Cai, Jinguang Han
https://arxiv.org/abs/2506.12846

Privacy-Preserving Federated Learning against Malicious Clients Based on Verifiable Functional Encryption
Federated learning is a promising distributed learning paradigm that enables collaborative model training without exposing local client data, thereby protect data privacy. However, it also brings new threats and challenges. The advancement of model inversion attacks has rendered the plaintext transmission of local models insecure, while the distributed nature of federated learning makes it particularly vulnerable to attacks raised by malicious clients. To protect data privacy and prevent malici…

🇺🇦 #NowPlaying on BBCRadio3's #RoundMidnight
Milhouse Collective:
🎵 the Q
#MilhouseCollective
https://open.spotify.com/track/1263SYbpLx4fBsnlMMdnvR

IoT Malware Network Traffic Detection using Deep Learning and GraphSAGE Models
Nikesh Prajapati, Bimal Karki, Saroj Gopali, Akbar Siami Namin
https://arxiv.org/abs/2507.10758

IoT Malware Network Traffic Detection using Deep Learning and GraphSAGE Models
This paper intends to detect IoT malicious attacks through deep learning models and demonstrates a comprehensive evaluation of the deep learning and graph-based models regarding malicious network traffic detection. The models particularly are based on GraphSAGE, Bidirectional encoder representations from transformers (BERT), Temporal Convolutional Network (TCN) as well as Multi-Head Attention, together with Bidirectional Long Short-Term Memory (BI-LSTM) Multi-Head Attention and BI-LSTM and LSTM…

packet_delays: Internet packet delays (2002)
A network representing the difference in delay observed by packet probes sent from a computer at Rice University to similar machines at different universities, in c.2002. The edge weight denotes the difference in delay of the packet in milliseconds.
This network has 10 nodes and 9567 edges.
Tags: Technological, Communication, Weighted

QGuard:Question-based Zero-shot Guard for Multi-modal LLM Safety
Taegyeong Lee, Jeonghwa Yoo, Hyoungseo Cho, Soo Yong Kim, Yunho Maeng
https://arxiv.org/abs/2506.12299

QGuard:Question-based Zero-shot Guard for Multi-modal LLM Safety
The recent advancements in Large Language Models(LLMs) have had a significant impact on a wide range of fields, from general domains to specialized areas. However, these advancements have also significantly increased the potential for malicious users to exploit harmful and jailbreak prompts for malicious attacks. Although there have been many efforts to prevent harmful prompts and jailbreak prompts, protecting LLMs from such malicious attacks remains an important and challenging task. In this p…

@… Here this combines two of your favourite things: https://securelist.com/open-source-package-for-cur…

Code highlighting with Cursor AI for $500,000
Kaspersky GReAT experts uncover malicious extensions for Cursor AI that download the Quasar backdoor and a crypto stealer.

Uncovering Reliable Indicators: Improving IoC Extraction from Threat Reports
Evangelos Froudakis, Athanasios Avgetidis, Sean Tyler Frankum, Roberto Perdisci, Manos Antonakakis, Angelos Keromytis
https://arxiv.org/abs/2506.11325

Uncovering Reliable Indicators: Improving IoC Extraction from Threat Reports
Indicators of Compromise (IoCs) are critical for threat detection and response, marking malicious activity across networks and systems. Yet, the effectiveness of automated IoC extraction systems is fundamentally limited by one key issue: the lack of high-quality ground truth. Current extraction tools rely either on manually extracted ground truth, which is labor-intensive and costly, or on automated ground truth creation methods that include non-malicious artifacts, leading to inflated false po…

SecurityLingua: Efficient Defense of LLM Jailbreak Attacks via Security-Aware Prompt Compression
Yucheng Li, Surin Ahn, Huiqiang Jiang, Amir H. Abdi, Yuqing Yang, Lili Qiu
https://arxiv.org/abs/2506.12707

SecurityLingua: Efficient Defense of LLM Jailbreak Attacks via Security-Aware Prompt Compression
Large language models (LLMs) have achieved widespread adoption across numerous applications. However, many LLMs are vulnerable to malicious attacks even after safety alignment. These attacks typically bypass LLMs' safety guardrails by wrapping the original malicious instructions inside adversarial jailbreaks prompts. Previous research has proposed methods such as adversarial training and prompt rephrasing to mitigate these safety vulnerabilities, but these methods often reduce the utility of LL…