Search: “Fairplay”

Google Play Early Access Abused to Push Thousands of Deceptive Android Apps

Bitdefender found abuse of Google Play's review-free Early Access program to push thousands of deceptive casino and reward apps promoted with AI deepfake ads.

Bitdefender reports threat actors are abusing Google Play's Early Access program, which blocks public reviews and ratings, to distribute thousands of deceptive casino, reward, and utility apps. A GTA imitator, 'Vice Streets: Open World,' surpassed 1 million downloads before disappearing, with apps promoted via AI celebrity deepfake ads on TikTok and Facebook and monetized through endless ad serving and payouts that never arrive. The report coincides with disclosures on Android malware families Hagaseca, Mantax Otax, StreamRat, and GoldFactory's Gigabud banking trojan with its Vwork companion app used for cloned-app financial fraud.

The Hacker News·updated Sep 11, 12:14 UTC · 5d ago·first Sep 10, 14:36 UTC · 6d agoPhishing & fraud in the wild 6 sources

Kids’ online safety bill faces dim prospects of passage this session despite progress

Kids Online Safety Act clears Senate committee but passage looks unlikely this session amid House-Senate deadlock over the duty-of-care provision.

KOSA advanced out of the Senate Commerce Committee, but the chambers remain split on a duty-of-care provision requiring platforms to act with reasonable caution to prevent foreseeable harm, which House leadership opposes over First Amendment and negligence-lawsuit concerns. The Senate passed KOSA 91-3 last Congress before it died in the House, and the House passed its own version without the duty of care in June as part of a larger package, after stripping a state-law preemption provision. Observers say even Senate passage this year is a struggle given the short calendar, with a lame-duck window between November and January the more plausible path, and Majority Leader John Thune controlling whether a roll-call vote happens.

The Record·Aug 24, 00:00 UTC · 23d agoPolicy & legal

Multiple VLC Media Player Vulnerabilities Allow Attackers to Corrupt or Read Heap Memory

Two VLC 3.0 flaws, CVE-2026-56711 (heap corruption, CVSS 8.6) and CVE-2026-73324 (memory leak), let crafted PNGs or RTSP playlists corrupt memory or leak data.

Hap Security researcher Fabian Wahle disclosed two VLC Media Player flaws on September 9, 2026, affecting versions 3.0.0 through 3.0.23. CVE-2026-56711 (CVSS 8.6) is an integer overflow and out-of-bounds write in the AllocatePicture function, exploitable via a crafted PNG with oversized IHDR dimensions, potentially causing crashes or code execution. CVE-2026-73324 (CVSS 6.9) lets a malicious RTSP server read adjacent heap memory through an unterminated 4096-byte response line, triggerable via a realrtsp playlist URL. No patched release is confirmed yet; users should avoid untrusted media files, playlists, and RTSP streams.

Cyber Security News·Sep 12, 14:31 UTC · 4d agoVulnerability 2 sourcesCVE-2026-56711CVE-2026-733241

Microsoft Commits to Sweeping AI Privacy Rules for Students. Will Other Tech Giants Follow?

Microsoft signed legally binding AI privacy and safety standards for schools with the American Federation of Teachers, effective November 1.

Microsoft's agreement with the American Federation of Teachers prohibits using student or educator data to train AI systems, bans selling data or using it for ads and product development, and forbids AI companions designed to foster emotional dependency, with third-party audits required. The standards apply to all schools under Microsoft contract starting November 1. NYC and LA school districts announced one-year moratoriums on student AI use, while OpenAI and Anthropic pursue similar pacts and Google remains noncommittal.

SecurityWeek·Sep 15, 20:24 UTC · 1d agoAI policy

VU#308749: Remote Code Execution and Arbitrary File Read Vulnerabilities in Kaltura Servers

Insecure deserialization in Kaltura's legacy HTML5 Player V2 (mwEmbed) allows arbitrary file read and remote code execution; Player V7 is unaffected.

CERT/CC issued VU#308749 for two flaws in Kaltura's HTML5 Player V2 library (mwEmbed/html5lib), both caused by the same insecure deserialization bug. The flaws enable arbitrary file read and remote code execution via the exposed mwEmbedLoader.php endpoint. Affected versions include html5lib v2.45, v2.103 and earlier, and other v2.x releases; the currently supported Player V7 is unaffected. No evidence of exploitation was cited.

CERT/CC Vulnerability Notes·Aug 25, 16:11 UTC · 22d agoVulnerability

Meta FAIR Introduces AI Research Preference Models (RPMs): Ranking ML Experiments Before Spending GPU Hours

Meta FAIR, Oxford and UCL introduce Research Preference Models that rank unexecuted ML experiments, lifting AIRS-Bench scores from 0.684 to 0.729 and cutting compute ~1.6×.

Researchers from Meta FAIR, Oxford, and UCL introduce Research Preference Models (RPMs), which use frozen pretrained LLMs (Qwen3.6-27B backbone, no fine-tuning) to rank unexecuted experiment candidates and execute only the winner of a pairwise knockout tournament. Two variants shipped: an inference-only LLM-as-a-judge and an agentic variant that runs small pilot experiments in an H200 sandbox. On AIRS-Bench (20 tasks, 24 hours on one H200, 10 seeds), scores rise from 0.684 (random) to 0.711 and 0.729 versus a 0.748 validation oracle, and both variants reach the baseline's 24-hour score in roughly 15 hours. The team reports new SOTA on WinoGrande (94.1% with Agentic RPM) and SVAMP (95.7% with inference-only).

MarkTechPost·Sep 6, 20:25 UTC · 10d agoAI research

TCRF taken offline by DDoS attack after Claude user ban

The Cutting Room Floor game wiki was taken offline by a DDoS attack after a user leveraging Claude was banned.

The Cutting Room Floor (TCRF), a wiki documenting unused video game content, was knocked offline by a distributed denial-of-service attack. The attack reportedly followed moderation action banning a user who was using Anthropic's Claude. The incident highlights friction between community sites and AI-assisted users and tools.

Lobsters · security·Aug 28, 21:26 UTC · 19d agoAI safety & security in the wild

AI for Games in the Foundation Model Era

Survey organizes foundation-model AI for games into six roles and analyzes which capabilities transfer across playing, design, building, runtime adaptation, and testing.

A survey maps foundation-model and learned world-model research across the game lifecycle into six roles: playing/acting, modeling players and games, designing games, building/maintaining games, runtime generation/adaptation, and testing/evaluation. The authors identify cross-role connections such as trajectories training world models and design specifications driving executable implementations. Control schemes, rules, engine interfaces, state representations, and player contexts often remain setting-specific, so downstream claims require validation in the target setting. Evaluation is most standardized for bounded game playing, while persistent state, repeated revision, validated player modeling, and automated testing remain less established.

Hugging Face daily papers·Sep 14, 20:00 UTC · 2d agoAI research

Before You Poll with LLMs: A Deliberative Diagnostic Framework

Deliberative diagnostic shows all five tested frontier LLMs misrepresent human belief shifts after arguments, with GPT-5.1 reversing on outgroup questions.

The Deliberative Polling Diagnostic Framework compares human and LLM persona belief shifts after identical informational interventions, using data from America in One Room (526 personas, 72 questions). All five frontier models tested failed uniquely: GPT-5.1 exhibited partisan reversal (80% on outgroup vs 26% on policy questions), Gemini 2.0 Flash, Claude Sonnet 4.5 and Llama 3.3 70B overshot at 5-7x human magnitude, and DeepSeek V3 showed near-zero change (rigidity). The authors term the underlying signature 'self-sycophancy', conformity to the model's internal persona stereotype rather than reasoning from provided information.

arXiv cs.AI / cs.LG / cs.CL·Sep 14, 16:41 UTC · 2d agoAI research

Why User Studies and Participant Experience Reporting Matter for VR Motion Privacy?

User studies explain VR motion privacy mechanism acceptance far better than physical deviation metrics, a study of three mechanisms finds.

The paper examines how well physical deviation predicts user acceptance of VR motion privacy mechanisms compared to user studies, testing three mechanisms at five deviation levels. User studies explain substantially more variation in mechanism acceptance, though physical deviation remains significant, and prior VR experience affects acceptance. Public VR game leaderboards expose motion recordings from hundreds of thousands of users, creating identification and profiling risks. The authors recommend combining physical deviation metrics with user studies and reporting participants' prior experience.

arXiv cs.CR·Sep 11, 04:08 UTC · 5d agoResearch

How to Investigate GitHub PAT Compromise: Lessons From a Multi-Organization Campaign

Wiz CIRT published an investigation playbook for GitHub PAT compromise after responding to a coordinated multi-organization campaign.

Wiz's Computer Incident Response Team shared lessons from its response to a coordinated campaign compromising GitHub personal access tokens across multiple organizations. The post provides a practical playbook covering detection, scoping, and investigation steps for token compromise. Specific victim names, affected counts, and attribution are not provided in the announcement.

Wiz Blog·Aug 13, 15:04 UTC · Aug 13, 2026Threat actor in the wild1

Attackers Use Multi-Hop Google Redirects for Phishing Campaign

Threat actors chain multiple legitimate Google service redirects to evade detection and deliver credential harvesting or ScreenConnect remote access tooling.

Threat actors are abusing multiple legitimate Google services to build multi-hop redirect chains that obscure the final destination and bypass reputation-based filtering. The campaign culminates in credential harvesting pages or silent installation of the ScreenConnect remote access tool. Defenders should watch for links that traverse trusted Google domains before landing on malicious endpoints.

Dark Reading·Sep 8, 21:03 UTC · 8d agoPhishing & fraud in the wild

Subgroup Membership Inference Audits of Differentially Private Synthetic Text

Audits of 32 differentially private synthetic-text releases show subgroup membership leakage is concentrated in few records and systematically underestimated by average-case attacks.

The paper defines a subgroup-targeted membership inference game in which the target pool is an explicit parameter, to audit residual leakage in differentially private synthetic text releases. The audit instantiates 32 proxies across four datasets, three generators (DP-SGD fine-tuning, API-based prompting, and activation steering), and five privacy budgets. DP substantially reduces average leakage at every budget, but remaining leakage is concentrated: roughly a tenth of records carries about 40% of it, and the noise removes more measured leakage from random records than from high-risk ones. Which records leak depends on the release mechanism, so record-level risk cannot be assessed independently of the release.

arXiv cs.CR·Sep 9, 07:56 UTC · 7d agoResearch

When the prompt becomes the payload: A practical pen-testing guide for GenAI, LLM and RAG applications

CSO Online publishes a practical penetration-testing guide for GenAI, LLM, and RAG applications, covering prompt injection, retrieval poisoning, and tenant isolation testing.

The guide frames LLM applications as attack graphs spanning prompts, retrieval layers, vector stores, tools, identities, and downstream APIs, arguing that conventional web testing misses instruction-vs-data channel risks. It builds on OWASP prompt injection guidance (direct vs. indirect injection) and NIST's 2025 adversarial machine-learning taxonomy, noting that RAG and fine-tuning do not remove injection risk. Recommended practices include documenting trust transitions across components, using canaries and synthetic records to avoid test side effects, running multi-turn and obfuscated injection campaigns, and verifying chains from poisoned documents to observable state changes. It also details testing RAG pipelines via controlled document poisoning across metadata, OCR layers, and code comments, plus cross-tenant isolation checks on retrieved document IDs.

CSO Online·Sep 9, 10:00 UTC · 7d agoAI safety & security1

ReactHuman: A Physics-Grounded Benchmark for Human-Like Reactive Decision-Making in Embodied Multimodal LLMs

ReactHuman benchmark tests whether multimodal LLMs react safely to sudden household hazards; seven evaluated models mishandle roughly one hazard in three.

ReactHuman is the first physics-grounded benchmark for human-like reactive decision-making, placing a multimodal LLM as the brain of a simulated humanoid facing 17 event families of sudden household hazards across over 1,000 bit-for-bit reproducible scenes with annotation-free ground truth from 240 Hz rigid-body simulation, including adversarial objects whose appearance contradicts their physics. A five-metric suite scores each reaction along reasonable, safe, and physically grounded axes, and every committed plan is physically executed. Seven representative MLLMs mishandle roughly one hazard in three, act from fixed dispositions rather than the observed scene, trust appearance over motion, and miss interception points at meter scale; none of these failures shrink with model scale.

Hugging Face daily papers·Sep 8, 20:00 UTC · 8d agoAI research

Safety for Whom? Boundary-Aware Self-Distillation for Controlled LLM Safety Refusal

A self-distillation safety framework tunes narrow-boundary refusals in Qwen3-8B, raising target-domain refusal to 84.75% while cutting over-refusal from 15.20% to 5.20%.

The paper formulates narrow-boundary safety, where deployments need refusals within specific topics rather than whole subjects, and proposes an offline self-generated framework with controlled topic generation, escalating retries, and harmful-benign boundary pairs. On political persuasion with Qwen3-8B, the method raised target-domain refusal from 9.47% to 84.75% and cut the mean unsafe-response rate across three broader benchmarks from 26.26% to 0.14%. Verified target-model responses reduced over-refusal from 15.20% to 5.20%, and boundary-pair data cut comply-side over-refusal on held-out pairs from 32.94% to 4.16%. Results show data composition controls the safety-usability trade-off and alignment should be evaluated on both sides of the refusal boundary.

Hugging Face daily papers·Sep 2, 20:00 UTC · 14d agoAI safety & security1

"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.

arXiv cs.CR·Sep 7, 21:44 UTC · 9d agoResearch

The Surprising Effectiveness of Approximate Value Iteration in Self-Play

Minimal approximate value iteration self-play learns more accurate value functions than AlphaZero in Connect Four and Hex while cutting training and inference costs.

The paper trains a minimal self-play implementation of Approximate Value Iteration (AVI) without MCTS and uses ground-truth oracles for exact evaluation in Connect Four, 7x7 Hex, and synthetic games. AVI learns more accurate value functions than AlphaZero, and its one-step-lookahead greedy policies remain competitive with MCTS-based policies at substantially lower training and inference cost. Preliminary experiments on Othello and 9x9 Go show AVI trains stably on larger games, suggesting simpler approaches have become increasingly practical with modern deep-learning tools.

arXiv cs.AI / cs.LG / cs.CL·Sep 8, 17:37 UTC · 8d agoAI research

CVE-2026-73334: Apache Parquet Hadoop: File-controlled KMS URL is forwarded to pluggable KmsClient that skips host validation

Apache Parquet Hadoop CVE-2026-73334: a file-controlled KMS URL reaches pluggable KmsClients without host validation in parquet-java 1.12 through 1.18.0.

Apache disclosed CVE-2026-73334, a moderate issue in the org.apache.parquet.crypto.keytools package of parquet-java, versions 1.12 through 1.18.0. The package implements envelope encryption that wraps data keys via a Key Management Service. A KMS URL controlled by the Parquet file is forwarded to a pluggable KmsClient that skips host validation, which could allow crafted files to redirect KMS requests.

oss-security·Sep 8, 15:46 UTC · 8d agoVulnerabilityCVE-2026-733341

Guiding Worker Self-Selection in Crowdsourcing Contests: An LLM-Augmented Algorithmic Approach

Researchers introduce GRAF, a greedy framework for crowdsourcing contest self-selection, and LLMScore, an LLM-driven method that auto-designs its scoring algorithm.

The paper studies self-selection in Tullock contests (SSTC), where workers choose contests and then compete within them. GRAF is a greedy polynomial-time framework that orders workers by a score vector with zero worker regret and platform optimality guarantees in special cases. LLMScore is an LLM-driven evolutionary framework that produces human-readable, inspectable scoring code, jointly optimizing platform utility and worker satisfaction. Across 1,000 synthetic instances in four settings, GRAF with LLMScore achieves high-quality, often near-optimal outcomes with low worker regret, transferring from small training instances to larger, structurally different settings.

arXiv cs.AI / cs.LG / cs.CL·Sep 7, 16:51 UTC · 9d agoAI research1

Omniscience for the Masses: New Threats in the Metaverse's Democratized World Creation

First systematic assessment of 25 metaverse platforms reveals five novel world-creation attacks enabling covert user surveillance on Roblox, Horizon Worlds, and VRChat.

Researchers present the first systematic security and privacy assessment of metaverse world creators, surveying 25 platforms that support user-created worlds. They designed and implemented five novel attacks that abuse standard creator tools to violate spatial, visual, and auditory constraints, enabling covert user surveillance and manipulation without software vulnerabilities or developer-level privileges. Five previously proposed attacks were replicated using only standard world-creation features. The authors conclude that existing platform vetting, runtime protections, and creator policies are insufficient to mitigate malicious world creators.

arXiv cs.CR·Sep 11, 08:00 UTC · 5d agoResearch

Engineered Persuasion: Evaluating Personalized Pretexts in LLM-Generated Spear Phishing

A study of 180 US workers found each LLM phishing personalization level raised click-intention odds by 28%, but credibility depends on context fit.

The arXiv paper evaluates how personalized pretexts in LLM-generated spear phishing affect perceived credibility, using 180 US working adults across 1,436 evaluations of emails with four cumulative personalization levels, from workplace context to shared-project details. Convincingness rose 2.40 points per level in sensitivity analysis and click-intention odds increased 28% per level, while non-clickers shifted toward deleting rather than reporting. Qualitative coding showed details matching the recipient's role and routines supported credibility, whereas incorrect, vague, or channel-inappropriate details raised suspicion. The authors argue personalization effectiveness depends on pretext fit, with implications for workplace security training.

arXiv cs.CR·Sep 3, 19:21 UTC · 13d agoResearch

PrivAudit: A Dual-Lens Auditing Framework for Website Privacy Practices under the CCPA

PrivAudit framework audits 998 websites for CCPA compliance, finding stronger disclosures but pervasive, weakly responsive third-party cookie tracking.

PrivAudit is an automated dual-lens auditing framework combining LLM-based analysis of privacy policies grounded in CCPA provisions with automated browser measurements of cookie writes under diverse privacy configurations. Applied to 998 websites, it finds CCPA-subject sites disclose opt-outs, data sharing, and user rights more frequently, yet tracking remains pervasive: 6,392 targeting cookies, 49% third-party writes. Cookies show limited-to-moderate responsiveness to privacy signals and consent choices even when sites claim to honor them. The framework is open-sourced and shared with regulators.

arXiv cs.CR·Sep 9, 04:31 UTC · 7d agoResearch

ZDI-26-588: Fabric.js loadFromJSON Server-Side Request Forgery Vulnerability

ZDI disclosed a server-side request forgery in Fabric.js loadFromJSON (CVE-2026-19504) that can leak sensitive information from affected implementations.

Zero Day Initiative advisory ZDI-26-588 describes a server-side request forgery vulnerability in the Fabric.js canvas library's loadFromJSON function. Exploitation requires interaction with the library and attack vectors vary by implementation, potentially exposing sensitive information. The issue is rated 4.0 on the CVSS scale and tracked as CVE-2026-19504. The advisory does not report exploitation in the wild.

ZDI Published Advisories·Aug 24, 05:00 UTC · 23d agoVulnerabilityCVE-2026-19504

Google Links Redact Extortion Group to BlackFile Rebrand

Infosecurity Magazine·Aug 7, 09:40 UTC · Aug 7, 2026Ransomware

Malicious Apache Modules Hijack Brazilian Government Site Traffic to Push Betting Pages

Check Point links Gambling Goblin, a Chinese-speaking cluster, to malicious Apache modules hijacking Brazilian government servers to promote betting sites.

Check Point Research tracks Gambling Goblin, a Chinese-speaking cluster, installing malicious Apache reverse-proxy modules on compromised Brazilian government and education web servers since mid-2025. The modules divert visitors to gambling and fake app-store pages while stripping the site's security headers, likely for large-scale SEO manipulation. The group's Linux arsenal includes DownPro, AlphaAgent, oRAT, a 3snake-based credential stealer, and SSH brute-forcing tools, and it is tied to Trend Micro's Earth Berberoka. Related SEO-fraud campaigns on .gov.br domains were documented by ESET (GhostRedirector), Palo Alto Networks Unit 42, and Hunt.io.

The Hacker News·Sep 2, 13:44 UTC · 14d agoThreat actor in the wild

New Chaos Variant Targets Misconfigured Cloud Deployments, Adds SOCKS Proxy

Darktrace says the Chaos botnet now targets misconfigured cloud deployments like Hadoop and added a SOCKS proxy for traffic relaying.

Darktrace identified a new 64-bit ELF variant of the Chaos botnet targeting misconfigured cloud deployments, expanding beyond the malware's traditional focus on routers and edge devices. Captured in a deliberately misconfigured Hadoop honeypot instance, the intrusion began with an HTTP request creating an application that ran embedded shell commands to fetch the Chaos agent binary from pan.tenire[.]com, set chmod 777 permissions, execute it, and delete the artifact to reduce forensic traces. The restructured variant adds a SOCKS proxy feature letting compromised systems ferry attacker traffic, while removing SSH-based spread and router-exploit functions, suggesting monetization beyond crypto mining and DDoS-for-hire. Possible Chinese origin is suggested by language artifacts and infrastructure; the delivery domain was previously used in Silver Fox's Operation Silk Lure phishing campaign delivering ValleyRAT.

The Hacker News·Aug 18, 12:13 UTC · 29d agoMalware1

RegionFed: Federated Learning for Personalized Query Understanding in Heterogeneous Retail Environments

RegionFed is a gradient-level federated learning framework enabling personalized retail query understanding while matching centralized accuracy with differential privacy.

RegionFed is an architecture-robust federated learning framework for personalized query understanding that operates at the gradient level, using the l2 conflict between regional and global gradients to diagnose heterogeneity and control personalization. Existing parameter-level personalized FL methods collapse on transformers, falling below 10% accuracy on T5, while RegionFed deploys unchanged on T5-Small, T5-3B, RoBERTa, and CNNs. RegionFed-Meta achieves 92.27% across Amazon ESCI, Amazon Reviews, and LEAF-FEMNIST, within 0.23 percentage points of the centralized upper bound, with epsilon-approx-0.60 differential privacy.

arXiv cs.AI / cs.LG / cs.CL·Sep 4, 17:50 UTC · 12d agoAI research1

Quantifying the Engagement Trap: Impact of Short-form Video Recommender Systems on Users with ADHD

A 302-participant study finds engagement-optimized short-form video recommenders cause disproportionate time blindness and distress for users with ADHD.

Researchers ran a stratified Prolific study with 302 participants comparing short-form video recommendation experiences with and without ADHD. Participants with ADHD reported significantly higher time blindness, post-usage regret, and emotional distress despite perceiving recommendations as similarly relevant. The paper proposes neurodiversity-aware, human-centered design interventions to mitigate these algorithmic harms.

arXiv cs.AI / cs.LG / cs.CL·Sep 7, 17:38 UTC · 9d agoAI research

Sociotechnical Aspects of Tor Relay Rejection

User study and simulations of Tor's relay end-of-life rejection policy find operators favor it; network churn affects anonymity more than EoL exclusions.

The study examines the Tor Project's 2019 end-of-life policy that rejects outdated relays, which constitute a notable fraction of consensus weight. A user study of 26 relay operators found they generally view the policy favorably despite limited awareness, though operational practices occasionally exclude newly installed relays. Historical-data-driven simulations show the policy gives adversaries only marginal advantage, with network churn exerting a more pronounced effect on user anonymity. Analysis of four exclusion rounds shows a minority of rejected relays typically account for over 50% of the security provided by all excluded relays, informing EoL policy recommendations.

arXiv cs.CR·Sep 14, 08:12 UTC · 2d agoResearch