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Cortex Xsoar

Palo Alto Networks Unit 42 hosts content under its Cortex XSOAR category, covering the vendor's security orchestration, automation and response platform.

The item is a Unit 42 category page for Cortex XSOAR rather than a standalone article; no article text was available. Cortex XSOAR is Palo Alto Networks' security orchestration, automation and response (SOAR) platform used for incident management and playbooks. No incident, vulnerability, or research findings could be extracted from the available information.

Palo Alto Unit 42·Aug 17, 12:20 UTC · Aug 17, 2026Tools

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Cortex XSIAM Archives

Palo Alto Networks positions Cortex XSIAM as a purpose-built platform to simplify security operations and speed incident remediation.

The Unit 42 blog page is a product category archive for Cortex XSIAM. The platform is marketed as purpose-built for SecOps transformation, simplifying security operations and accelerating incident remediation against current and future threats. No research findings, incidents, or vulnerabilities are discussed.

Palo Alto Unit 42·Sep 8, 14:27 UTC · 8d agoIndustry 6 sources

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Threat Assessment: Ryuk Ransomware

Unit 42 assesses Ryuk ransomware amid a CISA/FBI/HHS alert on threats to U.S. healthcare, including the UHS attack that disrupted hospital operations.

A joint CISA, FBI, and HHS alert on October 28, 2020 warned of an imminent threat to U.S. healthcare from operators deploying Trickbot and Ryuk ransomware. Universal Health Services reported a Ryuk attack that disrupted all U.S. UHS sites for weeks, with similar incidents at hospitals in Oregon and New York. Ryuk typically arrives after Trickbot or BazaLoader infections delivered via malicious email, and operators enumerate networks with PowerShell and WMI before encryption. The Trickbot Anchor_DNS module performs DNS tunneling for C2, using connectivity checks to benign domains like ipinfo.io and checkip.amazonaws.com.

Palo Alto Unit 42·Aug 17, 13:18 UTC · Aug 17, 2026Ransomware in the wild

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Threat Brief: Maze Ransomware

Unit 42 issued a threat brief on Maze ransomware, noting an uptick across industries and exploitation of Pulse VPN and IE flaws for initial access.

Palo Alto Networks detected an uptick in Maze ransomware samples across finance, healthcare, government, and other sectors. Maze, a variant of ChaCha ransomware first seen in May 2019, is distributed via weaponized Word/Excel attachments and the Spelevo exploit kit leveraging CVE-2018-15982 and CVE-2018-4878, and has used CVE-2019-11510 (Pulse VPN) and CVE-2018-8174 (Internet Explorer). Operators establish a foothold, escalate privileges, move laterally, and exfiltrate files before encryption for extortion leverage.

Palo Alto Unit 42·Aug 17, 13:17 UTC · Aug 17, 2026Ransomware in the wildCVE-2018-15982CVE-2018-4878CVE-2019-11510+1 CVEs

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Cortex Xdr

Stub page for Palo Alto Cortex XDR with no article text, offering no news content for classification.

The item contains only the title 'cortex xdr' and no article body. It references Palo Alto Networks' Cortex XDR product but provides no event, release, or threat information. Classified as general vendor/industry content with minimal relevance.

Palo Alto Unit 42·Sep 8, 14:27 UTC · 8d agoIndustry

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Threat Brief: OWASSRF Vulnerability Exploitation

Unit 42 details active exploitation of Exchange SSRF flaws CVE-2022-41080/CVE-2022-41082 via the OWASSRF method, deploying the SilverArrow PowerShell backdoor.

Unit 42 documents OWASSRF, an exploit chain combining CVE-2022-41080 and CVE-2022-41082 to achieve remote code execution on Microsoft Exchange via Outlook Web Access, bypassing Microsoft's ProxyNotShell mitigations. Active exploitation was observed in late November and early December 2022, affecting at least eight organizations. Attackers authenticated to Exchange, then executed a PowerShell backdoor tracked as SilverArrow to create admin accounts, install AnyDesk, tunnel RDP via PuTTY Link, and dump LSASS memory for credential theft. Exploitation is isolated rather than mass-scale because authentication is required.

Palo Alto Unit 42·Aug 17, 10:58 UTC · Aug 17, 2026Exploit / PoC in the wildCVE-2022-41080CVE-2022-41082CVE-2022-410401

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Threat Brief: Ongoing Russia and Ukraine Cyber Activity

Ukrainian government sites were defaced via OctoberCMS CVE-2021-32648 while Microsoft-attributed actor DEV-0586 deployed destructive WhisperGate malware.

Unit 42 tracks January 2022 attacks against Ukrainian government websites that were defaced or made inaccessible, which Ukraine attributed to Russia. Attackers exploited CVE-2021-32648, an account takeover flaw in OctoberCMS prior to 1.0.472, by submitting a boolean true as the password reset code to bypass validation. Separately, WhisperGate malware, attributed by Microsoft to DEV-0586, disabled Windows Defender and corrupted files across multiple Ukrainian organizations, using a ransomware-appearing first stage and an HTTPS-beaconing second stage with LOLBINs and anti-analysis techniques.

Palo Alto Unit 42·Aug 17, 12:20 UTC · Aug 17, 2026Threat actor in the wildCVE-2021-326481

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Modality-Autoregressive World-Action Models

ModAR autoregressively denoises multiple future modalities (point tracks, DINO features, depth) before predicting actions, beating prior world-action models at all data scales.

ModAR is the first world-action model (WAM) to autoregressively denoise multiple future modalities before predicting actions, letting each prediction condition on previously generated modalities. Training from scratch shows WAMs benefit from predicting point tracks, DINO features, and depth maps, while future RGB adds no consistent benefit. ModAR's sequential generation outperforms existing WAM formulations with the highest average success rate at all evaluated data scales. It slightly beats video-model-initialized Flex-π (75% vs 72% success) using roughly 20x fewer training FLOPs and no pretraining, and wins on three real-world bimanual tasks.

Hugging Face daily papers·Sep 14, 20:00 UTC · 1d agoAI research

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Threat Assessment: Egregor Ransomware

Unit 42 assesses Egregor ransomware, a Sekhmet variant likely run by former Maze affiliates using Qakbot, IcedID, and Ursnif for initial access.

Since September 2020, Unit 42 observed Egregor ransomware attacks across the U.S., Europe, Asia-Pacific, and Latin America, coinciding with the Maze team's shutdown announcement. Egregor, a variant of the Sekhmet family, uses commodity malware such as Qakbot, IcedID, and Ursnif for initial access, followed by scripts that modify firewalls, enable RDP, and deploy Cobalt Strike for reconnaissance and exfiltration. A PowerShell script uninstalls the McAfee agent and downloads the Egregor DLL via BITS, executed with the -passegregor10 key. Victims face a three-day ransom deadline before exfiltrated data is published on the Egregor Hall of Shame leak site.

Palo Alto Unit 42·Aug 17, 13:16 UTC · Aug 17, 2026Ransomware in the wild

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Abyssos: Technical Analysis of a New Modular RAT

Zscaler ThreatLabz analyzes Abyssos, a new modular C++ RAT offering credential theft, file exfiltration, and VNC-based remote access.

Zscaler ThreatLabz identified a new malware family tracked as Abyssos in late June 2026. Abyssos is a modular remote administration tool (RAT) written in C++ that supports credential theft, file exfiltration, and remote access via VNC. The malware is under active development, with multiple version numbers and obfuscation passes designed to evade security products. The analysis covers its core features, configuration, obfuscation, and network communication protocol.

Zscaler ThreatLabz·Aug 10, 15:51 UTC · Aug 10, 2026Malware

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Threat Brief: 3CXDesktopApp Supply Chain Attack (Updated)

Trojanized 3CXDesktopApp installers for Windows and macOS loaded RC4-encrypted shellcode delivering a backdoor to hundreds of thousands of 3CX users.

Threat actors compromised the 3CXDesktopApp build process, adding malicious libraries (ffmpeg.dll, d3dcompiler_47.dll) to installers downloaded from the developer's website. The Windows MSI loader decrypts embedded shellcode with RC4, sleeps for a random 1-4 weeks, then beacons to C2 hidden in icon files on GitHub. Unit 42 fingerprinted 247,277 IPs across 199 countries using 3CX applications and blocked shellcode execution at 127 Cortex XDR customers between March 9-30, 2023.

Palo Alto Unit 42·Aug 17, 12:18 UTC · Aug 17, 2026Threat actor in the wild1

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The Semantic Bottleneck: Leveraging Semantic Representations for Non-Invasive Speech Decoding

Researchers introduced Brain2Semantics2Text, decoding sentence meaning from non-invasive MEG brain recordings via a semantic bottleneck, improving on prior Brain2Text methods.

The paper proposes Brain2Semantics2Text, a non-invasive speech decoding method that maps sentence-level magnetoencephalography (MEG) responses into a semantic embedding space and inverts those embeddings into natural language. Motivated by evidence that high-level semantic representations are distributed across cortex and evolve on slower timescales, the approach targets meaning rather than phonemes or words, avoiding the need for word-level alignment. The authors report improved sentence-level results compared to prior non-invasive Brain2Text methods despite the low signal-to-noise ratio of neural recordings.

Hugging Face daily papers·Sep 8, 20:00 UTC · 7d agoAI research2

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CVE-2022-22965: Spring Core Remote Code Execution Vulnerability Exploited In the Wild (SpringShell) (Updated)

Attackers actively exploit Spring Framework RCE CVE-2022-22965 (SpringShell, CVSS 9.8) to deploy webshells; patches 5.3.18/5.2.20 shipped March 31, 2022.

CVE-2022-22965 enables unauthenticated remote code execution in the widely used Spring Framework (CVSS 9.8), which Unit 42 has observed being exploited in the wild. The flaw stems from getCachedIntrospectionResults exposing the class object during parameter binding, letting attackers manipulate the class loader to modify Tomcat logging and upload a JSP webshell. Public PoCs require JDK 9+, Tomcat, WAR packaging, and spring-webmvc or spring-webflux dependencies on Spring 5.3.0-5.3.17, 5.2.0-5.2.19, or older. Fixes shipped in Spring Framework 5.3.18 and 5.2.20; the related Spring Cloud Function flaw CVE-2022-22963 was patched March 29, 2022.

Palo Alto Unit 42·Aug 17, 10:58 UTC · Aug 17, 2026Exploit / PoC in the wildCVE-2022-22965CVE-2022-22963CVE-2010-16221

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RenderFormer-V2: Neural Rendering with Heterogeneous Scene Primitives

RenderFormer-V2 is a transformer-based neural renderer handling caustics, volumetric scattering and out-of-distribution materials without per-scene training or specialized code.

RenderFormer-V2 is a learned transformer-based neural rendering model that models global light transport as a sequence-to-sequence transformation, handling caustics, volumetric scattering, environment lighting, textured and displaced surfaces and out-of-distribution materials. It uses a two-stage process: a view-independent stage resolving primitive-to-primitive transport, and a view-dependent stage converting the neural scene representation into pixels. Improvements include combined windowed-attention with a rendering-informed attention sink for scalability, support for heterogeneous primitives like environment maps and participating media, and a surface-reflectance-independent neural material encoding, validated across diverse scenes with extensive ablations.

Hugging Face daily papers·Sep 3, 20:00 UTC · 12d agoAI research

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Cybersecurity jobs available right now: September 8, 2026

Help Net Security's September 8 job roundup lists open cybersecurity roles at AudioCodes, Garmin, Scotiabank, GDIT, Subway and others across seven countries.

The listing includes a CISO role at AudioCodes in Israel, a Combat Systems Cyber Engineer at Johns Hopkins Applied Physics Laboratory, cloud security engineering at Garmin, and an IAM Architect at Scotiabank. Other openings span penetration testing for NATO exercises at Spektrum in Belgium, SOC analyst at Orro Group in Australia, threat hunting at GDIT, and security operations at Subway in the USA. Roles range from CISO and architect to SOC analyst and penetration tester.

Help Net Security·Sep 8, 00:00 UTC · 8d agoIndustry

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The 2026 PNPL Competition: Word Classification and Efficient Cross-Subject Generalisation in LibriBrain100

The 2026 PNPL competition releases LibriBrain100, a MEG speech dataset with 32 extra subjects, targeting word classification and cross-subject BCI generalization.

The 2025 PNPL competition on non-invasive speech decoding from MEG achieved F1-macro scores of 95.6% for speech detection and 73.6% for phoneme classification, built on LibriBrain's ~50 hours of single-subject data. The 2026 edition extends this with LibriBrain100, adding 32 subjects (~40 minutes each) plus ~80 hours of within-subject data. Two tracks target within-subject word classification at scale and cross-subject generalization with subject-specific fine-tuning shrinking from ~40 to ~20 to ~10 minutes, aiming at clinically feasible non-invasive BCIs for people with profound paralysis.

Hugging Face daily papers·Sep 2, 20:00 UTC · 13d agoAI research

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Another Apache Log4j Vulnerability Is Actively Exploited in the Wild (CVE-2021

Apache Log4j 2 RCE CVE-2021-44228 is actively exploited via crafted JNDI lookups; Palo Alto Unit 42 urges upgrading to Log4j 2.17.1.

Unit 42 details CVE-2021-44228, an easily exploited remote code execution flaw in Apache Log4j 2 where attacker-supplied JNDI lookups in logged user input cause payload download and execution. Massive internet-wide scanning for unpatched systems has begun, with observed follow-on activity including information stealing, Cobalt Strike delivery, and coinmining. The recommended fix is Log4j 2.17.1, which also patches CVE-2021-45046, CVE-2021-45105, and CVE-2021-44832. Widely used Java software including Apache Struts, Solr, Druid, Flink, ElasticSearch, and Spring Boot is affected.

Palo Alto Unit 42·Aug 17, 12:20 UTC · Aug 17, 2026Exploit / PoC in the wildCVE-2021-44228CVE-2021-45046CVE-2021-45105+3 CVEs

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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 papers·Sep 9, 20:00 UTC · 6d agoAI research

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Can AI make threat intelligence easier? One platform thinks so

Feedly positions its AI-driven Threat Intelligence platform to cut CTI collection time, scanning 10,000+ sources with 1,000 AI models and reporting 70% time savings.

A profile of Feedly Threat Intelligence describes how CTI teams such as RH-ISAC reduced threat data collection from 10 hours weekly to 2-3 hours after adoption. The platform scans over 10,000 open web sources using 1,000 AI models to extract TTPs, CVEs and IoCs into a real-time Threat Graph queryable via an Ask AI LLM with source citations. Customers including GreyNoise, Sopra Steria, gematik and GISA reported measurable gains such as 20 hours saved daily and earlier vulnerability flagging than CISA 65% of the time. The product integrates with Anomali ThreatStream, Cortex XSOAR, Microsoft Sentinel and OpenCTI, plus a REST API and GitHub scripts.

Help Net Security·Aug 24, 04:21 UTC · 23d agoTools

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Omni Interaction Agent Technical Report

Researchers release Gander, an end-to-end omni interaction model with full-duplex streaming across video, speech, and text plus agentic capabilities.

Gander is an end-to-end model unifying omni perception, realtime interaction, and agentic capabilities in a single framework, accepting continuously streaming video, speech, and text. It uses a Cerebellum-Brain architecture where the Cerebellum handles realtime conversation and the Brain handles reasoning and agentic tasks, built on a streaming Thinker-Talker design with chunk-level token streams. Internal human evaluations report spoken dialogue on par with SOTA open source models and competitive omni interaction; the models, code, and data are released publicly.

Hugging Face daily papers·Sep 7, 20:00 UTC · 8d agoModel release

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Multiple Vulnerabilities Discovered in a SCADA System

Unit 42 details five vulnerabilities (CVSS 7.0-7.8) in ICONICS Suite SCADA software enabling privilege escalation and DoS.

Unit 42 discovered five vulnerabilities (CVE-2024-1182, CVE-2024-7587, CVE-2024-8299, CVE-2024-8300, CVE-2024-9852) in ICONICS Suite versions 10.97.2 and earlier for Windows during a 2024 security assessment. The flaws, rated CVSS 7.0-7.8, allow DLL hijacking, privilege escalation, information disclosure, denial-of-service and potentially full system compromise. ICONICS Suite is a SCADA solution with hundreds of thousands of installations in over 100 countries, widely used in critical infrastructure, and several dozen servers are internet-exposed per Unit 42 telemetry. ICONICS released patches and advisories with workarounds in 2024.

Palo Alto Unit 42·Aug 19, 12:04 UTC · 28d agoVulnerabilityCVE-2024-1182CVE-2024-7587CVE-2024-8299+2 CVEs1

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DeCAL: Towards Physically-Grounded Dexterous Vision-Language-Action Models via Contact-Aware Latent Co-Imagination

DeCAL, a contact-aware dexterous vision-language-action model with visuo-tactile fusion, reports 71% average task success.

DeCAL is a physically-grounded dexterous vision-language-action (VLA) model built on a Mixture-of-Transformers architecture with specialized experts for understanding, imagination, and action generation. It introduces Adaptive Visuo-Tactile Fusion with contact-aware gating and Visuo-Tactile Latent Co-Imagination to jointly model visual and tactile dynamics. It reports state-of-the-art results with a 71% average success rate and 83.4% progress success rate, plus generalization to unseen scenarios.

arXiv cs.AI / cs.LG / cs.CL·Sep 8, 17:47 UTC · 7d agoAI research1

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Cortex Xpanse

Palo Alto Unit 42 Cortex Xpanse product-category page published with no article text available for classification.

The URL resolves to the Cortex Xpanse product category listing on the Unit 42 blog rather than a research article. No article body was provided, so no vulnerabilities, incidents, or research findings can be extracted.

Palo Alto Unit 42·Aug 19, 12:04 UTC · 28d agoIndustry

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FreeFlow: A Bias-free Hierarchical Transformer for Optical Flow Estimation

FreeFlow is a bias-free hierarchical transformer achieving state-of-the-art optical flow results on Sintel, KITTI-2015, and Spring benchmarks.

FreeFlow replaces task-specific inductive biases like correlation volumes and iterative warping with a single feed-forward encoder-decoder combining window, shifted-window, and reduced-resolution global attention. It reaches 0.68/1.48 EPE on Sintel Clean/Final, 3.23 Fl-all on KITTI-2015, and 3.192 1px on Spring. The architecture scales consistently from small to large variants and remains memory efficient at 1080p inference.

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

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CoRA-NAS: Coarse Ranking and Anchor-Residual Refinement for Neural Architecture Search

CoRA-NAS combines zero-cost proxy ranking with low-cost learning-curve refinement, achieving the best worst-space Spearman correlation across NAS benchmarks.

The paper proposes CoRA-NAS, a two-stage neural architecture search framework pairing a static ranking prior (CoRA-Rank) with learning-curve refinement (CoRA-Refine) that extrapolates early validation curves for sampled anchors and propagates residual corrections with an ExtraTrees model at about 1% of full training cost. Across NAS-Bench-201, NAS-Bench-101, TransNAS-Bench-101, and NATS-SSS it achieves mean Spearman correlations of 0.946, 0.715, 0.786, and 0.894 respectively, with the best worst-space correlation of 0.715 among compared methods. On NAS-Bench-201/CIFAR-100 its selected architecture reaches 73.32% accuracy versus a 73.37% ground-truth best.

arXiv cs.AI / cs.LG / cs.CL·Sep 10, 17:49 UTC · 5d agoAI research

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ModaLens: Measuring Image Sensitivity in Report-Conditioned Medical VLMs

ModaLens image-swap audit shows report availability cuts MedGemma-27B image sensitivity on MIMIC-CXR from 20.94% to 4.26% answer changes.

ModaLens is a paired image-swap audit measuring how report availability affects image sensitivity in report-conditioned medical VLMs. On MedGemma-27B across 3,199 paired MIMIC-CXR cases from 293 patients (14 questions per case), generated answers changed on 4.26% of image-swap trials with the report versus 20.94% without it, a 16.7-point paired difference (95% CI 15.6-17.7). The original prompt with a lowercase first-token readout gave 4.70% versus 17.07%, and the direction replicated in two further model lineages. Labels derived from reports limit conclusions about visual correctness; code, prompts, and run records are publicly released.

Hugging Face daily papers·Sep 13, 20:00 UTC · 2d agoAI research

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Cross-modal learning for SAR target recognition using optical vision foundation models

Frozen DINOv3 optical prototypes supervise SAR target recognition without EO/SAR pairs, improving classification on the heavily imbalanced UNICORNv2 dataset.

The framework aligns SAR embeddings to class-level prototypes built from a frozen DINOv3 electro-optical encoder, requiring no strict EO/SAR image pairs. At inference the SAR model operates independently without access to optical imagery. On UNICORNv2, a civilian vehicle dataset with heavy speckle and severe class imbalance, EO prototype alignment improves accuracy over frozen DINOv3, SAR-only finetuning, and unpaired distribution alignment baselines.

arXiv cs.AI / cs.LG / cs.CL·Sep 7, 16:54 UTC · 8d agoAI research

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Cortex Cloud Archives

Palo Alto Networks publishes a product category page for Cortex Cloud, an AI and automation platform for cloud and SOC workflows.

This item is a product category archive page for Palo Alto Networks' Cortex Cloud, describing it as an AI and automation-driven platform spanning code to cloud to SOC. The page contains no new research, incident, or product release details beyond marketing language about prioritizing risks and accelerating remediation.

Palo Alto Unit 42·Aug 20, 19:01 UTC · 26d agoIndustry

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XHToken/Spark-X2.5-4B-GGUF — new model trending #30 on Hugging Face

XHToken released GGUF weights of Spark-X2.5-4B, a compact model with 1M-token context and 200+ language support, under Apache 2.0.

The Hugging Face repository provides BF16 GGUF conversions of Spark-X2.5-4B, a compact general-purpose language model for conversation, writing, translation, reasoning, coding, tool use, and agentic workflows. The model uses a hybrid attention architecture, supports a native context length up to 1M tokens, and covers more than 200 languages. Local inference is supported through Ollama and LM Studio via an XHToken llama.cpp fork, with a --think=false flag to disable thinking mode for faster responses. Released under Apache License 2.0; it was trending #30 on Hugging Face at publication.

Hugging Face trending models·Aug 28, 11:26 UTC · 19d agoModel release

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MaxKernel: Agentic Kernel Generation for TPUs

Researchers open-source MaxKernel, a multi-agent LLM system that generates and optimizes TPU kernels matching expert hand-tuned baselines on JaxBench.

MaxKernel is a multi-agent system offering three paradigms for TPU kernel development: human-in-the-loop collaborative design, a fully autonomous metric/trace-driven optimization loop, and graph-based autonomous search for global exploration. All paradigms draw on a shared pool of specialized sub-agents for planning, implementation, self-debugging, testing, and hardware profiling. Evaluated on JaxBench's 50 diverse TPU kernel tasks and real-world workloads from open-source models, it consistently matches expert hand-tuned baselines. The system is open-sourced via the AI-Hypercomputer GitHub repository.

Hugging Face daily papers·Sep 2, 20:00 UTC · 13d agoAI tools & infra

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SlipSense: Multimodal Tactile Learning for Low-Latency and Generalized Slip Detection

SlipSense fuses a 32x32 piezoresistive array and MEMS accelerometer to detect robotic grip slips within 23.1 ms, generalizing zero-shot across platforms.

SlipSense is a multimodal tactile slip-detection framework built on TacV5, a sensor combining a 32x32 piezoresistive array at 240 Hz and a 3-axis MEMS accelerometer at 8 kHz. It performs modality-specific encoding, intra-sensor fusion, and cross-modal attention with causal temporal prediction at 240 Hz. On a 1.4-million-frame dataset spanning 37 objects it achieves 96.7% Macro F1 with a false-positive rate below 1.6%, detecting 76% of slip events within 23.1 ms. Trained solely on UMI data, it transfers zero-shot to a Tesollo dexterous hand across unseen objects, sensor units, and platforms.

arXiv cs.AI / cs.LG / cs.CL·Sep 14, 17:23 UTC · 1d agoAI research

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SyncWorld: Visual Calibration Enables World Models as Zero-Shot Simulators

SyncWorld is an action-conditioned world model acting as a zero-shot robotics simulator across unseen environments via visual calibration.

Researchers propose SyncWorld, an action-conditioned world model that simulates robot action outcomes in unseen environments without additional training. It uses a visual calibration episode of paired frames and actions to establish the setup-specific Action-Visual Mapping in context. Experiments show accurate simulation of action outcomes in novel settings and that simulated rollouts enable test-time policy improvement without training.

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

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Omni-Streaming Thinking

Omni-Streaming Thinking fixes premature cross-modal commitment in streaming omni-modal models via pending claims verified against modality-specific evidence, beating baselines by over 10%.

The paper identifies 'premature cross-modal commitment', where streaming models keep relaying early visual interpretations even after audio contradicts them. OST generates evidence-linked pending claims with future verification intervals, stores audio and visual evidence separately, and refutes claims when contradictory evidence appears. Built on a frozen Qwen3-Omni-30B-A3B-Instruct backbone with lightweight adaptation, it outperforms open baselines by more than 10% relative on five streaming and audio-visual benchmarks. On the new OST-DiagBench it reaches d-prime 2.95 versus at most 1.38 for open baselines, while reducing vision-induced auditory hallucinations.

Hugging Face daily papers·Sep 13, 20:00 UTC · 2d agoAI research1

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The Pelican comparison grid for Astra is pretty interesting

Simon Willison's pelican SVG comparison shows GPT-6 Astra producing markedly better images than GPT-5.6 Sol, Terra, and Luna across reasoning levels.

Willison generated pelicans-riding-bicycles SVGs with newly accessed GPT-6 Astra at low through max reasoning levels and rendered them in a comparison grid against GPT-5.6 Sol, Terra, and Luna. Astra's outputs were markedly more coherent, while even the best GPT-5.6-Sol images remained largely abstract shapes. Astra does not support a reasoning=none setting, so all comparisons involved reasoning-enabled runs.

Simon Willison·Sep 4, 23:59 UTC · 11d agoAI research

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Cortex XDR™ Detects New Phishing Campaign Installing NetSupport Manager RAT

Cortex XDR threat hunters uncovered a phishing campaign delivering the NetSupport Manager RAT via a fake password-protected NortonLifelock Word document.

Unit 42 identified a January 2020 phishing campaign using a Microsoft Word document disguised as a password-protected NortonLifelock file. Enabling macros triggered an obfuscated command that built alpaca.bat in the temp directory, which used msiexec to download an MSI payload from quickwaysignstx.com, filtered on the Windows Installer user-agent string. The payload installed a PowerShell script and the campaign, which has delivered NetSupport Manager RAT since at least 2018, showed related activity dating back to early November 2019.

Palo Alto Unit 42·Aug 17, 13:18 UTC · Aug 17, 2026Phishing & fraud in the wild1

42

Pre-Whitening and BCJR Posterior Distillation for Bi-LSTM Detection in Faster-than-Nyquist Signaling

Study shows nested-window Bi-LSTM architectures do not improve faster-than-Nyquist detection; pre-whitening plus BCJR distillation cuts bit error rates.

Across roughly 260 controlled trainings, processing nested intersymbol-interference windows in separate recurrent branches never significantly beat a plain Bi-LSTM at matched parameter budgets. The authors attribute the limitation to the observation model rather than architecture, and instead pre-whiten inputs and distill BCJR soft posteriors into the network. With 3.4% more parameters, the method reaches 1.05x the BCJR bit error rate at compression factor 0.8 and 1.89x at 0.7, improving to 1.47x with a wider whitened window.

arXiv cs.AI / cs.LG / cs.CL·Sep 7, 17:07 UTC · 8d agoAI research

10

ThinkPrior: Zero-Rollout Difficulty Priors for Cold-Start Prompt Selection in RLVR

ThinkPrior builds zero-rollout difficulty priors via an offline verifier-anchored pass, halving silent groups in RLVR and cutting wasted rollouts on Qwen2.5-Math-7B.

In GRPO-based RLVR, groups where all rollouts are correct or all are wrong yield zero advantages and consume about 39% of a run's rollouts under uniform sampling. ThinkPrior initializes a Beta posterior from an external anchor pass's verifier-scored pass rate, selecting prompts by expected learnability before any target-policy rollout, without changing the loss or optimizer. On Qwen2.5-Math-7B across sixteen seeds it more than halves early silent groups and cuts wasted rollouts through step 30 by nearly a fifth, with no detected final-accuracy difference. The ThinkPrior+DAPO composition reduces generated rollouts by 10.6% at an equal 3,840-rollout update budget.

arXiv cs.AI / cs.LG / cs.CL·Sep 8, 17:30 UTC · 7d agoAI research1

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You Can't Prefer Emotions You Don't Sample: Intensity Undershoot in DPO-Tuned LLMs

Study quantifies DPO-tuned LLMs undershooting requested emotional intensity, tracing the gap to candidate-pool extremity rather than conditioning format.

Conditioning an instruction-tuned LLM on continuous valence-arousal targets yields gain of only 0.26 for valence and 0.13 for arousal on Llama-3.1-8B, far below faithful control of 1.0. The authors attribute undershoot to neutral-heavy preference corpora like EmoBank and candidate pools lacking extreme affect, leaving DPO without extreme exemplars. Uniform target coverage with a hotter candidate pool raises valence gain to 0.40 on Llama-3.1-8B and 0.44 on Qwen3-8B, with modest in-distribution cost; arousal gains remain unstable across seeds.

arXiv cs.AI / cs.LG / cs.CL·Sep 7, 17:47 UTC · 8d agoAI research

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Anthropic caught Russia-linked spies using Claude in hacking operations

Anthropic disrupted Russia-linked APT29 using Claude in espionage against 20+ organizations, including Ukrainian government targets and a military drone maker whose vision SDK was stolen.

Anthropic's threat report covering December 2025 to August 2026 attributes the campaign to Midnight Blizzard (APT29/Cozy Bear, Storm-2945), which it links to Russia's SVR. The group compromised hotel Wi-Fi providers, altered DNS records to redirect travelers, accessed mailboxes at two drone-component manufacturers, and stole a proprietary SDK for a drone vision system, which it reverse-engineered using Claude. The group also used Claude to monitor whether security products detected its implants and to modify and redeploy flagged artifacts. The report also covers ShinyHunters affiliates using AI for credential scanning and extortion, a Chinese-speaking group's autonomous zero-day research, and a French-speaking hacktivist.

The Record·updated Sep 12, 10:30 UTC · 4d ago·first Sep 11, 12:45 UTC · 5d agoThreat actor in the wild 15 sources

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Anatomical Grounding and Leakage-Aware Multimodal Contrastive Learning for Alzheimer's Disease Classification from Structural MRI

Study of label leakage and anatomical grounding in multimodal MRI models for Alzheimer's staging shows cognitive-score fusion accuracy of 87.3% is leakage-driven.

The authors train a ResNet18 slice-based encoder with a one-layer Transformer on 1,075 ADNI-1 T1 MRI scans, using FastSurfer segmentations and YOLOv8 localization (mAP_50 above 0.96) as anatomical reference. Grad-CAM shows the image-only classifier often attends to skull and background rather than disease-relevant structures. A CLIP-style image-tabular contrastive framework organized along a label-leakage spectrum yields 87.3% three-way accuracy with cognitive scores versus 73.0% with regional volumes, and cropping to the medial temporal lobe raises image-only accuracy from 58.7% to 65.1%. Results come from single runs on a small balanced test set with reported confidence intervals.

arXiv cs.AI / cs.LG / cs.CL·Sep 14, 17:13 UTC · 1d agoAI research1

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