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DomainAll Cyber AIWindow24h 3d 7d 30d AllIn the wild Important only Show duplicates

CategoryAll Vulnerability 18 Policy & legal 17 Exploit / PoC 8 Ransomware 8 Malware 7 Data breach 5 Phishing & fraud 5 Industry 5 AI tools & infra 3 Advisory 2 Threat actor 2 AI safety & security 1

SeverityAll critical 29 high 15 medium 16 low 15 info 6

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Cyberattack causes a flight delay? Airlines won’t owe you a hotel or meal

A new DOT rule exempts airlines from providing meal vouchers or hotels for cyberattack-caused delays if carriers comply with applicable cybersecurity regulations.

A Department of Transportation rule published in September 2026 adds "cybersecurity attacks" to a list of 10 "not controllable" flight disruption causes, creating a new delay tracking category and relieving compliant airlines of customer service obligations like meal vouchers and hotels. The rule stems from the FAA Reauthorization Act of 2024 and applies only when carriers demonstrate compliance with applicable cybersecurity regulations. Consumer groups reacted cautiously: FlyersRights criticized the lack of public comment, while the National Consumers League saw both certainty benefits and risks from ambiguous wording. The article cites prior aviation incidents including Scattered Spider's airline attacks and the 2024 Collins Aerospace hack that disrupted European flights.

CyberScoopSep 11, 21:26 UTC · 4d agoPolicy & legal

Delta investigates in-flight Wi-Fi spoofing on post

A passenger on Delta flight 591 allegedly spoofed the airline's in-flight Wi-Fi with a rogue access point, prompting an FBI and FAA investigation.

Delta Airlines is investigating an incident on flight 591 from Las Vegas to Atlanta in which a passenger reportedly created a rogue Wi-Fi network named 'Delta WiFi Fast' to steal sensitive passenger data. The cabin crew deactivated the aircraft's Wi-Fi for roughly 30 minutes, and Delta says flight safety and aircraft operating systems were never affected. The FBI's Atlanta office and the FAA acknowledged awareness of the incident, which bears hallmarks of an evil twin attack involving spoofed login portals and credential harvesting. DEF CON organizers said this year's conference in Las Vegas also suffered multiple similar deauthentication Wi-Fi attacks.

CyberScoopAug 11, 21:35 UTC · Aug 11, 2026Phishing & fraud in the wild

Saving Jet Fuel

Tutorial optimizes flight paths to cut jet fuel costs using open-source Scikit-decide planning framework and OpenAP aircraft performance models.

A technical walkthrough demonstrates wind-aware flight path optimization using Scikit-decide, an open-source framework for reinforcement learning and automated planning, paired with OpenAP fuel-consumption models built by Dr. Junzi Sun at TU Delft and NOAA wind data. A Boeing 787-9 flying EWR to FCO can require roughly $68K in fuel, and adjusted routing could save thousands. The post uses Python 3.12, DuckDB with spatial extensions, and QGIS for map rendering.

Hacker News · AISep 15, 23:17 UTC · 17h agoAI tools & infra HN 45↑ · 19 comments

The Fraud Ecosystem: A Transition From Known Marketplaces to a Fragmented Environment

Rapid7 analyzes how fraud marketplaces are fragmenting into specialized shops after larger marketplaces were dismantled, aided by new MITRE F3 framework

Rapid7 reports a shift from large known fraud marketplaces to a fragmented environment of smaller specialized storefronts such as Xleet, Blackpass, Infodig, and Styx, operating across dark web channels, Telegram, and P2P options. These Fraud-as-a-Service shops sell stolen accounts, PII, synthetic identity generation, infrastructure, and money laundering support, supporting schemes like business email compromise. MITRE's Fraud Fighting Framework (F3), introduced in early 2026, aims to help security teams prioritize monitoring of fraud TTPs, particularly account takeover techniques. Fraud damages are anticipated to approach hundreds of billions of USD.

Rapid7 BlogSep 11, 13:33 UTC · 5d agoPhishing & fraud

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 papersSep 2, 20:00 UTC · 13d agoAI safety & security1

DEF CON crowd suspected in fake-hotspot attack on Delta flight

FBI Atlanta is investigating a fake in-flight Wi-Fi hotspot attack on a Delta flight, with DEF CON attendees suspected and no arrests made.

FBI Atlanta confirmed it is investigating an attack involving a rogue Wi-Fi hotspot used against a Delta flight. The timing and technique led to speculation that DEF CON attendees were involved. No arrests have been made, and details about affected victims remain limited.

Ars Technica · SecurityAug 12, 00:08 UTC · Aug 12, 2026Phishing & fraud in the wild

Trump may be forced to reveal secret rules feds use for AI safety testing

Protect Democracy sued four federal agencies to force disclosure of the administration's secret framework for frontier AI safety reviews.

Nonprofit Protect Democracy sued four federal agencies, including the Office of the National Cyber Director, OSTP, Treasury and Commerce, seeking disclosure of the secret voluntary framework used for pre-release safety reviews of frontier AI models. The complaint demands the framework text, participant identities and selection criteria by September 30, alleging OpenAI negotiated a private agreement limiting distribution of its cutting-edge models to government-vetted partners. The suit follows the launch of the GOLD EAGLE clearinghouse and the completion of the review framework on August 3, with California Senator Josh Becker supporting the request while the state considers the SB 813 bill for transparent AI safety standards.

Ars Technica · AISep 2, 17:58 UTC · 13d agoAI policy

ENCP: Episode-Normalized Conformal Prediction for Vision-and-Language Navigation

ENCP calibrates conformal prediction per navigation episode, giving step-level coverage guarantees for vision-language navigation agents despite within-episode dependence.

Episode-Normalized Conformal Prediction (ENCP) rescales a nonconformity score by a VLN policy's residual confidence and calibrates one maximum score per episode, preserving step-level coverage of at least 1−α despite dependence among steps within an episode. Across four VLN policies and three nonconformity scores on R2R and REVERIE, ENCP meets all reported empirical step-coverage targets in seen-to-unseen evaluation. The model-agnostic uncertainty estimates can signal when an agent should defer to a stronger predictor or human assistance.

arXiv cs.AI / cs.LG / cs.CLSep 15, 17:42 UTC · 23h agoAI research

From Intent to Execution Grant: An Execution-Boundary Conformance Profile for High-Risk AI Actions

Researchers specify EBL-Core, an execution-boundary conformance profile binding AI agent intents, policies, and evidence into verifiable execution grants, validated with bounded tests.

The paper defines EBL-Core, a conformance profile deciding whether one fully materialized AI-generated candidate action may receive action-scoped execution authority. It binds a structured intent object, Root and Operational Policies, typed evidence, and a verifiable Decision Derivation through an Execution Release Contract, with lifecycle rules for Redemption and Revocation. Evaluation included 34 static vectors, 15 lifecycle checks, and 100 trials of 32 concurrent Redemption attempts yielding exactly one winner per trial. The authors state these bounded results demonstrate executability of the specified subset, not production readiness or complete mediation.

arXiv cs.CRSep 10, 14:21 UTC · 6d agoAI safety & security1

The Model Proposes, the Code Disposes: A Pre-Registered Ablation of a Verifier-and-Acceptance Stage in an LLM-Orchestrated Offensive-Security Agent

Pre-registered ablation finds a model verifier stage in an LLM offensive-security agent suppresses findings; removing it eliminated suppression with precision tradeoff.

The paper evaluates a verifier-and-acceptance stage in an LLM-orchestrated offensive-security agent via a pre-registered 20-run confirmatory ablation and a 2x2 factorial study with 40 runs on vulnerable lab targets. Removing the stage eliminated pre-report suppression (median 2 vs 0 findings, p = 0.00003) but reduced model-blinded shipped precision (0.471 vs 0.353, p = 0.0087). Suppression was attributed to the model verifier rather than deterministic acceptance rules, and an instrumented canary recorded zero external contacts in all 60 runs. The full design retained 93.8% of model-adjudicated true candidates but failed its pre-registered non-inferiority floor of 0.90.

arXiv cs.CRSep 14, 17:13 UTC · 2d agoResearch

SchemeArena: Factorized Stress Testing of Scheming in LLM Agents

Researchers introduce SchemeArena, a 400-scenario benchmark stress-testing scheming in LLM agents, finding explicit instrumental goals are the strongest driver of covert misaligned behavior.

The paper presents SchemeArena, a 400-scenario benchmark built through factorized scenario synthesis spanning safety-relevant tool domains, instrumental goals, oversight conditions and pressure mechanisms. The accompanying SCOUT monitor grounds multi-criteria scheming judgments in evidence drawn from agents' reasoning and actions. Stress tests across five LLM agents show explicit instrumental goals are the strongest driver of scheming propensity, while action-only monitoring increased scheming in several closed models, suggesting partial oversight can act as an optimization constraint. The benchmark, code and monitor are released at github.com/launchnlp/SchemeArena.

Hugging Face daily papersSep 7, 20:00 UTC · 8d agoAI safety & security

Perturbation Probing: A New Diagnostic for the Fragility of LLM Safety

Unit 42 research shows LLM safety refusals concentrate in a thin neural layer, motivating external, multi-layered AI security controls.

Palo Alto Networks Unit 42 introduces Perturbation Probing, a diagnostic technique for measuring the fragility of LLM safety mechanisms. The research finds that safety refusal behavior is localized within a thin neural layer, implying small perturbations can undermine built-in refusals. The authors argue this motivates external, multi-layered security defenses on top of model-internal safety training.

Palo Alto Unit 42Aug 28, 22:00 UTC · 18d agoAI safety & security

Beyond Training: A Feasibility Taxonomy for Inference-Time AI Governance

A study maps twenty inference-time AI governance mechanisms, finding commercial readiness only against cooperative deployers and no adequate defense versus state-level adversaries.

The paper develops a feasibility taxonomy of twenty inference-time AI governance mechanisms across monitoring, verification, and enforcement, each rated on a four-point readiness scale against a four-vendor evidence base. Fifteen of the twenty mechanisms have commercial technical substrates in production today, though governance-grade assurance and adversarial robustness vary substantially. Stress testing shows readiness holds only against a cooperative deployer and low-to-medium-capability user: no mechanism rates adequate against a high-capability state-level deployer, and fine-tuning removes model-internal enforcement components. A second-rater reliability check on readiness ratings returned a quadratic-weighted Cohen's kappa of 0.74.

arXiv cs.CRSep 9, 12:37 UTC · 7d agoAI policy

Evaluating Verified Autonomy in Quantum Engineering

Quantum-Harbor lab and QIQCBench (49 tasks) expose wide performance gaps across 17 frontier agentic systems in verified quantum engineering.

Researchers built Quantum-Harbor, a virtual laboratory providing a controlled execution environment where scientific AI agents interacting with quantum systems can have both actions and conclusions directly verified. QIQCBench contributes 49 expert-authored tasks spanning calibration and control, error correction and compilation, and sensing and networking. Across 17 frontier agentic systems, verified performance varied widely, exposing a substantial gap between demonstrated capability and reliable autonomous operation.

arXiv cs.AI / cs.LG / cs.CLSep 15, 16:46 UTC · 1d agoAI research1

Deep Learning-Based Detection of Electrical Faults and Power Quality Disturbances in Aerospace Power Systems

Compact ResNet detects electrical faults in 400 Hz aircraft power systems with 95.87 percent accuracy after deployment on Xilinx Zynq MPSoC.

The study targets multiclass fault and power quality disturbance detection in 400 Hz aerospace networks using a high-fidelity simulation model inspired by the Boeing 787 electrical architecture, covering 21 normal, disturbance, switching, open-circuit, and short-circuit conditions. Two 73,500-sample datasets are built from 1D waveforms and STFT time-frequency representations, augmented with domain randomization and class-specific GANs, and the time-series dataset is released via IEEE DataPort. A compact ResNet with 175,685 parameters achieved 96.94 percent software test accuracy, and 95.87 percent after 8-bit quantization on a Xilinx Zynq UltraScale Plus ZCU102 with 6.90 ms mean accelerator latency per record.

arXiv cs.AI / cs.LG / cs.CLSep 9, 17:19 UTC · 6d agoAI research

Refuse without Refusal: A Structural Analysis of Safety-Tuning Responses for Reducing False Refusals in Language Models

Study shows training LLMs on refusal rationales instead of boilerplate refusal statements reduces false refusals while maintaining safety performance.

The paper decomposes safety-tuning responses into a boilerplate refusal statement and an explanatory rationale, finding that refusal statements push models to rely on superficial cues and misjudge benign queries as harmful. Training solely on rationales reduces false refusals while maintaining comparable safety performance, and the benefits carry over to in-context learning configurations and remain compatible with inference-time mitigations. The results argue for precisely curated, fine-grained safety supervision datasets when aligning LLMs.

Hugging Face daily papersSep 3, 20:00 UTC · 12d agoAI safety & security1

ActionSplice: In-Flight Action Editing for Interactive World Models

ActionSplice enables in-flight action editing in chunk-autoregressive video world models via a lightweight corrector, avoiding rollback or waiting for the next chunk.

ActionSplice is an inference framework that formulates in-flight action editing for chunk-autoregressive video world models as Counterfactual State Transport (CST), where a lightweight corrector transports the interrupted backbone-native representation toward the matched state induced by the revised action. The world model and sampler remain frozen, and sampling resumes without replaying completed evaluations. Across minWM-Wan Action2V and HY-WM1.5, the retargeting variant CST-R reduces rollback-relative LPIPS by 61.5% and 75.9% versus direct condition swapping, while the temporal-splicing variant CST-T reduces suffix LPIPS by 56.1% and 77.5% with 2.73x and 1.69x pixel-ready speedups over waiting.

Hugging Face daily papersSep 7, 20:00 UTC · 8d agoAI research

How Fragile Is Safety Alignment at Frontier Scale? A Single-Direction Attack on a 320B MoE

Researchers show directional ablation breaks refusal in GLM-5.3-Flash, a 320B-parameter MoE, cutting refusal by 41–89 points across seven benchmarks.

The study extends directional ablation, a white-box attack that removes an aligned LLM's refusal behavior, from dense models up to ~70B parameters to GLM-5.3-Flash, a 320B-parameter mixture-of-experts model with 288 routed experts, four-wide hyper-connection residual, and block-FP8 quantization. Editing attention, dense, and routed-expert writers jointly removes 0.776 of refusal, with 74% of the effect existing only under the joint intervention; the conventional module-name-based recipe reaches only 0.066 and fails silently on MoE architectures. The attack yields 41–89 percentage-point reductions in refusal across seven harmful benchmarks with no detected capability change, and a category-concentrated refusal residue survives all edits at ranks 1 to 12.

arXiv cs.CRSep 9, 06:48 UTC · 7d agoAI safety & security

Why federal cyber defense demands an offense-driven mindset

Opinion piece argues federal agencies should prioritize exploitability and autonomous penetration testing over static CVSS-based patching, citing CISA's BOD 26-04 and NSA's CAPT program.

The author argues that static CVSS-driven patching leaves federal agencies exposed because vulnerable does not equal exploitable, and adversaries chain misconfigurations and stolen credentials instead of burning zero-days. The piece cites CISA's BOD 26-04 and the NSA's Continuous Autonomous Penetration Testing (CAPT) program, which logged 28,282 completed pentests across 822 Defense Industrial Base organizations and closed 71% of critical findings within 30 days. It recommends defining risk by exploitability and mission impact and augmenting human pentesting with autonomous validation and verification.

CyberScoopSep 8, 18:30 UTC · 7d agoIndustry

Hiding Prompt Injection in Legal Filing

A judge banned a plaintiff from electronic court filings after hidden prompt-injection text was discovered planted in legal documents.

Bruce Schneier's blog discusses an incident in which hidden prompt-injection instructions were planted inside a legal filing, apparently targeting AI systems that might process court documents. Judge Walter Spader Jr. responded by banning the plaintiff from electronic filings, requiring all future submissions as printed hard copies. Commenters debate whether the tactic could affect future AI-based processing of court records and whether plain-text formats will regain favor.

Schneier on SecurityAug 31, 11:04 UTC · 16d agoAI safety & security in the wild

Australia is replacing the Essential Eight with a new cyber framework. Here’s how exposure management can help you get ahead of it.

Australia's ASD is replacing the Essential Eight with an outcomes-based Essentials series covering IT, cloud, OT and likely agentic AI, with deprecation from mid-2027.

The Australian Signals Directorate announced in June 2026 that the Essential Eight will be replaced by an outcomes-focused Essentials series structured as chapters covering enterprise IT (including identity and SaaS), cloud, OT, and likely agentic AI. Deprecation begins around mid-2027 with full retirement around mid-2028, though timelines are targets; the Essential Eight is mandatory for roughly 98 non-corporate Commonwealth entities but voluntary for private firms. Tenable argues the shift demands continuous security posture evidence via exposure management rather than point-in-time checklist assessments.

Tenable BlogSep 15, 13:32 UTC · 1d agoPolicy & legal1

Large Language Models for HVAC Operations in Building Energy Systems: A Critical Review of Methods, Applications, and Deployment Readiness

Systematic review of 66 studies finds LLMs for HVAC operations are mostly research-stage, with no ready-now deployment and only four pilot-level studies.

A critical review of 66 peer-reviewed studies from 2023 to March 2026 examines LLMs for HVAC operations in building energy systems. Only four studies reach pilot-level evidence, none reports sustained operational deployment, and 63 of 66 are research-only. Conventional ML, MPC, and RL remain dominant for high-frequency control and short-horizon forecasting, and the evidence supports LLMs primarily as semantic and workflow layers rather than autonomous controllers.

arXiv cs.AI / cs.LG / cs.CLSep 4, 16:08 UTC · 12d agoAI research

Access Control as Verified Parse Constraints

Researchers verify a class of EverParse validators that correctly enforce access-control policies, deploying a machine-checked enforcement gate on seL4.

The paper targets enforcement-code bugs in commercial security gateways by proving that forward-only, backtrack-free EverParse validators are verified recognizers for a bounded finite-state class that includes access-control decision functions with fixed-offset fields and bounded disjunction. Encoding a bounded policy language into a fixed-size byte buffer allows an SMT solver to verify the enforcement code once, covering all byte values, policies, requests, and sessions. Editing rule content over a fixed endpoint set requires no new proof, while adding endpoints reruns the toolchain. A deployment on the seL4 microkernel ensures every request passes through the gate and unverified components cannot corrupt the enforcement chain.

arXiv cs.CRSep 11, 06:33 UTC · 5d agoResearch

Ask Before You Optimize: Dynamic Pre-Formulation Clarification for Interactive Optimization

Researchers release OR-Clarify, a benchmark testing whether LLM agents ask clarifying questions before formulating optimization models from incomplete requests.

OR-Clarify evaluates pre-formulation clarification in operations research: each task gives a partial problem description, withholds structured hidden slots, and scores agents via bounded interaction with a simulated user, measuring slot recovery, stopping behavior, silent assumptions, and interaction cost. The authors also propose InterOPT, a two-stage framework that identifies formulation-critical gaps to decide when to ask or stop. In choice-based experiments InterOPT substantially outperforms all baselines in exact slot recovery and remains competitive in the open-ended setting.

Hugging Face daily papersSep 3, 20:00 UTC · 12d agoAI research1

The AI policy window is open. We need to act.

OpenAI calls for mandatory national AI safety regulation and backs four California AI safety bills as capabilities accelerate.

OpenAI argues the rapid pace of AI progress, including signs of AI-accelerated research, requires urgent policy action through mandatory, capability-based national regulation. The company endorses four California bills (SB 813, AB 1405, SB 1119, AB 1864) covering independent safety assessments, AI auditor standards, youth protections, and safeguards against AI-enabled biological threats. It also commits to industry-led frontier standards, international coordination, and strengthening internal safeguards such as universal trajectory monitoring and mandatory alignment-evaluation gates for its Astra model. The post references chief scientist Jakub Pachocki's warning about recursive self-improvement and Greg Brockman's "defenders window" concept.

OpenAI NewsSep 9, 13:00 UTC · 7d agoAI policy

Boardroom Battles 2026: ASD’s Cyber Priorities & AI Risk

Huntress analyzes the Australian Signals Directorate's 2026 board cyber priorities and frontier AI guidance on managing AI-era threats.

Huntress analyzed the Australian Signals Directorate's 2026 boardroom cyber priorities and its guidance on frontier AI risk. The piece argues that rapid AI adoption alone will not counter AI-era cyber threats and outlines what boards should focus on. No specific incident, vulnerability, or regulation change is described.

HuntressAug 30, 13:00 UTC · 17d agoPolicy & legal

Pelican-Sim 1.0: A General World Model Simulator for Embodied Intelligence

Pelican-Sim 1.0 predicts future observations from visual context and robot actions; four-step autoregressive rollouts yield 5.67x speedup and raise policy success from 70% to 93%.

Pelican-Sim 1.0 is a general world model simulator for embodied intelligence that predicts future observations from visual context and robot actions using a 28-dimensional unified action space valid across heterogeneous embodiments. Sparse mixture-of-experts layers reduce FVD by 6.530 versus the dense backbone, and causal adaptation with few-step distillation yields a four-step autoregressive simulator achieving a 5.67-fold speedup over the 35-step model. Trained on roughly one million real-world and simulated trajectories, PSNR improves over the strongest baselines by 4.636 on AgiBotWorld Beta, 2.080 on RoboMIND, and 10.343 on RoboTwin. Downstream on RoboTwin, adding 500 generated trajectories to 50 demonstrations per task raises policy success from 70% to 93%, and policy evaluation reaches a Pearson correlation of 0.994.

Hugging Face daily papersSep 9, 20:00 UTC · 6d agoAI research

Who gets to define the rules for AI?

Cohere CEO Aidan Gomez attacks big-lab antitrust exemption proposals as cartel behavior that lets incumbents write AI safety rules.

Cohere CEO Aidan Gomez argues that proposals from large AI labs—particularly Anthropic's roadmap requesting antitrust exemptions for safety coordination—amount to a cartel letting incumbents define rules for everyone else. He draws parallels to the 1975 SEC NRSRO credit-rating designations and the EU's 1985 Motor Vehicle Block Exemption, where safety justifications produced incumbent-protecting market structures. Gomez supports independent review of highly capable AI systems but disputes who writes the standards, who conducts review, and who participates. He also warns AI cyber offense is getting cheaper faster than defenses are improving.

Hacker News · AISep 14, 04:49 UTC · 2d agoAI policy HN 41↑ · 29 comments

Managing the cyber risk of agentic AI

UK NCSC guidance recommends safeguards, sandboxing, and active oversight to manage cyber risks of autonomous agentic AI systems.

The UK National Cyber Security Centre published guidance on managing the cyber risk of agentic AI systems. It recommends safeguards, sandboxing, and active human oversight to limit unintended autonomous activity while realizing the benefits of these systems. The publication is official national guidance for organizations deploying agentic AI.

NCSC UKAug 20, 12:00 UTC · 27d agoAdvisory

The EPA Is Planning to Scrap Public Review Rules for Data Center Pollution

The EPA plans to eliminate mandatory public review of air-pollution permits for AI data centers and their power plants, reducing community input.

The EPA proposes scrapping a federal requirement that states notify the public and accept comments before approving air-pollution permits for industrial facilities, including AI data centers and their power plants. A related proposal would let developers begin construction before permits are approved. Nearly 200 advocacy groups and more than a dozen states oppose the changes; critics cite health risks, rising utility bills, housing displacement, and disproportionate impacts on Black communities in the rural South, while the EPA argues the changes speed permitting. The proposal is expected to be finalized within the next year.

Hacker News · securitySep 11, 18:05 UTC · 4d agoAI policy HN 33↑ · 6 comments1

Understanding Operator Attitudes Toward AI-Supported Decision Making in Maritime Operations

A survey of maritime professionals finds positive but scenario-sensitive attitudes toward AI collision-avoidance assistants, urging design for calibrated reliance.

The study surveyed maritime stakeholders on attitudes toward AI-supported collision-avoidance assistants for Maritime Autonomous Surface Ships, measuring technology anxiety, trust in automation, and explanation quality with established questionnaires plus thematic analysis of open responses. Results show generally positive disposition, no clear age-related openness differences, stable trust across scenarios, and multidimensional, scenario-sensitive explanation ratings. Participants valued decision support and situation awareness but worried about AI reliability, over-reliance, and skill loss; the authors recommend designing for calibrated reliance with domain experts in the loop rather than maximizing automation or trust.

arXiv cs.AI / cs.LG / cs.CLSep 10, 16:57 UTC · 6d agoAI research1

FTC rescinds policy requiring health apps to notify customers after a breach

The FTC unanimously rescinded its 2021 policy statement that required health and fitness apps to notify users after health-data breaches.

The FTC voted to rescind a September 2021 Biden-era policy statement that extended federal health-data breach notification rules to health apps, fitness trackers, and connected devices, which had exposed violators to fines of $43,792 per violation per day. The 2021 statement, adopted in a divided 3-2 vote under then-chair Lina Khan, cited HIPAA coverage gaps for consumer health applications. The commission said the statement provided minimal benefit, was superseded by rulemaking, and aligns with the White House deregulatory agenda.

CyberScoopupdated Sep 10, 13:40 UTC · 6d agofirst Sep 9, 19:18 UTC · 6d agoPolicy & legal 2 sources