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"Shut Up and Let Me Enjoy My Otome": Understanding and Measuring the Toxicity in Otome Game Communities
First large-scale study finds 22.20% of Weibo otome game posts toxic versus 3.71% on Reddit, with LLM detectors reaching 0.82 F1.
Researchers present the first large-scale measurement of toxicity in otome game communities, introducing OtomeSCAN, which collected and analyzed 620,045 posts from Weibo and Reddit over 18 months. They manually annotated 4,308 posts, identified eight target groups, and evaluated seven toxicity detectors, with their best LLM-based model reaching F1-scores of 0.82 on Weibo and 0.78 on Reddit. The study found 22.20% of Weibo posts were toxic versus 3.71% on Reddit, and toxicity rose to 37.09% within 72 hours during an external attack on Weibo. The authors also flagged 191 potential-coordination clusters, 64.40% of which targeted game developers.
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You Shall Not Pass into Ring-0! A User Privacy-Friendly Anti-Cheat Architecture for Personal Computers
Tirith replaces invasive kernel-level game anti-cheats with protected VMs and a dual-trusted virtualization monitor, preserving detection and near-native performance.
Researchers present Tirith, an anti-cheat architecture that runs video games in Protected Virtual Machines, sandboxing computations from untrusted root admins, and uses a virtualization monitor trusted by both players and developers to watch for malicious drivers. This removes the need for privacy-invasive ring-0 kernel anti-cheat components while matching their protection against a wide range of cheating mechanisms. To overcome VM stack limitations, the work contributes a security-focused Library OS kernel for games and an efficient graphics sharing pipeline for near-native rendering performance.
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Decentralized network congestion control for DAG-based distributed ledger system
Researchers propose node-specific variable proof-of-work to curb transaction spamming in DAG-based distributed ledgers, proving a Nash equilibrium enforces prescribed node behavior.
The paper proposes a variable, behavior-based node-specific proof-of-work model for DAG-based distributed ledger networks, where congestion is mainly driven by transaction spamming rather than user growth or token launches. The model grants equal opportunity to stakeholders regardless of computational resources and penalizes nodes issuing more than a prescribed number of transactions. System behavior is modeled as a non-cooperative game over finite network resources, and the authors prove existence of a Nash equilibrium enforcing the prescribed behavior.
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Automobile Camouflage to Hide from Flock Cameras
Schneier on Security highlights a printed vehicle-camouflage pattern tested to defeat Flock surveillance cameras and Axon body cameras.
The post discusses covering cars with printed patterns designed to fool Flock automated license-plate recognition software, with testing reportedly done against Flock and Axon body cameras. Reader comments question effectiveness against other ALPR vendors, Flock's RF MAC-address upgrade, and whether such camouflage might become regulated. The page also contains off-topic comment threads about anti-bot over-blocking and privacy.
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LG smart TVs caught logging audio with screen off and snooping on local devices
Gamers Nexus found LG smart TVs record microphone audio in standby, scan home networks, and feed LG Ad Solutions ad targeting.
A 135-minute Gamers Nexus investigation with Level1Techs and independent researchers found retail LG OLED TVs running webOS sweep local networks, gather device names and Wi-Fi metadata, and run Automated Content Recognition. Tests showed the TVs capture clean microphone audio while appearing powered down and store it offline, uploading once reconnected. The team also found RCE vulnerabilities in webOS now moving through responsible disclosure; LG claims 216 million smart TV sales, and its ad unit claims access to 363 million addressable devices in the US.
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Why Is SHAP Not a Reliable Standalone Explanation Framework for Malware Detection?
arXiv paper shows SHAP gives unreliable standalone explanations for malware detection, with attribution dilution and sign reversal in dependent PE feature spaces.
The paper argues SHAP's formal guarantees are insufficient for reliable malware interpretation because the explained feature-coalition game is fixed only by analyst choices, not by malware behavior in the data. In static Portable Executable feature spaces, dependent feature groups cause conditional SHAP to dilute credit by a factor of 1/m across redundant features, attribute importance to features the model never uses, and even reverse attribution signs; interventional SHAP queries off-manifold coalitions no real executable exhibits. Experiments on EMBER-2018, EMBER-2024, and BODMAS with fixed LightGBM and XGBoost detectors confirm these effects. The authors position SHAP as a limited diagnostic requiring explicit data-distribution statements and domain validation, not a standalone explanation framework.
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