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AI models don't kill people – people kill people
Register opinion argues AI extinction fears distract from present harms and proposes jailing executives whose unsafe models cause damage.
The Register opinion responds to Anthropic researcher Jacob Coxon's resignation over concerns AI 'could kill us all by the end of the decade,' a post that drew over 110 million views in under 24 hours. Anthropic science lead Evan Hubinger stated he believes there is a greater than 10 percent chance AI kills all humans within a decade and that Anthropic lacks a plan to solve superintelligence alignment. The author argues researchers ignore measurable present harms such as climate change, chatbot-linked suicides, autonomous vehicle failures, and AI-directed warfare. The piece proposes criminal liability for executives shipping unsafe models, citing Volkswagen emissions and Gree dehumidifier prosecutions as precedent.
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In Other News: InjectEave Attack, SIM Swapper Sentenced, Glasswing Findings Review
SecurityWeek weekly roundup covers exploited WordPress Super Forms flaw CVE-2026-14894, a $10M bounty on an Iranian cyber official, InjectEave attacks, and more.
SecurityWeek's weekly roundup aggregates short items across the threat landscape, including Microsoft's report of invisible Unicode tag characters used in financial phishing lures at up to 2.37 million messages per day, and active exploitation of critical WordPress Super Forms plugin flaw CVE-2026-14894 to deploy PHP webshells. Policy items include a $10 million US bounty for IRGC-CEC Cyber Operations Command lead Amir Yaryab, a 16-month prison sentence for ex-AT&T employee Kenneth Carter over SIM swaps with nearly $600,000 in intended losses, and the US arraignment of Russian Sergei Anatolyevich Filimonov over credential harvesting. Technical items include InjectEave electromagnetic side-channel attacks tested on 11 devices, an FBI warning on OAuth consent phishing, and VulnCheck's finding that only 202 of 26,153 Anthropic Project Glasswing findings were fixed.
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Model-Aware Schedules Improve Generation via Fiberwise Optimal Transport
Researchers introduce model-aware diffusion schedules via fiberwise optimal transport, cutting flow-matching FID on CIFAR-10 by 38.6% at 16 function evaluations.
The paper proposes constructing diffusion and flow-matching sampling schedules from a fiberwise prediction risk defined via optimal transport, combined with coefficient-path kinetic action, yielding a closed-form time allocation. Across DDPM and flow-matching experiments spanning targets, datasets, and architectures, the schedules beat model-agnostic baselines, including a 38.6% relative FID reduction for flow matching on CIFAR-10 at 16 function evaluations. Normalized fiberwise-risk profiles from independently trained models align closely, suggesting empirical universality, and a frozen analytic allocation template retains most of the gains.
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Feature Recovery for Object Understanding After Irreversible Fire Damage
TRACE benchmark with 21.4K scenes studies post-fire object understanding; a Feature Recovery Module improves degraded-image retrieval by 12.5% and material recovery by 20.1%.
The paper introduces TRACE, a transformation-aware benchmark with 21.4K real-image-grounded synthetic scenes, 499 object identities across 189 categories, and five tasks covering degraded-object detection, pristine-state recovery, material recovery, description generation, and functional reasoning. Existing models degrade sharply: RF-DETR mAP falls 71% relative from least to most severe level, and InternVL3.5 retrieval R@1 drops from 93.85 to 28.11. The proposed Feature Recovery Module maps degraded encoder features to pristine-aligned representations while keeping the host model frozen, averaging relative gains of 12.5% for retrieval and 20.1% for material recovery across VLM hosts and severity levels.
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Injected and Leaked: Actively Inducing Side-Channel Leakage Using Electromagnetic Injection and Hardware Nonlinearity
Researchers introduce InjectEave, using electromagnetic injection and hardware nonlinearity to induce side-channel leakage and eavesdrop on headphone audio from 30 meters.
An arXiv paper shows electromagnetic injection can actively amplify side-channel leakage: nonlinear hardware such as amplifiers, ADCs, and power converters modulates secret electrical signals onto an injected EM carrier, upconverting low-frequency secrets into measurable EM emissions. By tuning injection frequency and amplitude, an adversary can shape the effective spectrum and entropy of the resulting leakage. The InjectEave attack demonstrated eavesdropping on wired and wireless headphone audio from up to 30 meters and in through-wall scenarios using accessible RF equipment, plus leakage of smart home device power consumption and analog sensor inputs. Case studies show closed-loop eavesdropping and manipulation of landline phone conversations, and the paper discusses mitigations.
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Honeypot-Omaha and batch.py [Guest Diary], (Wed, Sep 2nd)
A SANS ISC guest diary describes batch.py, a Python tool that consolidates honeypot logs and enriches IOCs with threat intelligence data.
Written by a SANS.edu BACS intern, the diary explains analysis of the DShield Honeypot-Omaha sensor, which uses Cowrie to emulate SSH and Telnet and log attacker activity. The author's batch.py script implements a four-phase pipeline with SHA-256-generated master and guest authentication to consolidate JSON and log files, correlate data via external APIs, and produce MITRE, CVE, geolocation, threat-score and fingerprint enrichment for investigated indicators.
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Home & Small Office Wireless Routers Exploited to Attack Gaming Servers
Unit 42 details an updated Gafgyt botnet variant exploiting Zyxel, Huawei, and Realtek router vulnerabilities to recruit devices for DoS attacks on gaming servers.
Unit 42 researchers identified an updated Gafgyt variant derived from the JenX botnet that combines three remote code execution exploits: CVE-2017-18368 (Zyxel P660HN-T1A), CVE-2017-17215 (Huawei HG532), and CVE-2014-8361 (Realtek RTL81XX chipset). Shodan scans show more than 32,000 wireless routers worldwide potentially vulnerable to these exploits. The exploits act as droppers, pulling architecture-specific binaries from a malicious server (185.172.110.224), and the botnet performs denial-of-service attacks against gaming servers, most notably Valve Source engine servers.
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