Search: “mttr”

Intelligence-Driven SOC: Modernizing Threat Monitoring and Detection Engineering for Ultimate MTTR Reduction

ANY.RUN urges SOC teams and MSSPs to adopt intelligence-driven threat monitoring to reduce mean time to respond.

ANY.RUN published a vendor blog post arguing that threat monitoring is the connective tissue of modern security operations and that SOC teams and MSSPs must move from simple log collection to a proactive, intelligence-driven framework. The piece promotes ANY.RUN's Threat Intelligence offering as the solution for detection engineering and MTTR reduction. It is promotional content rather than a threat disclosure or research finding.

ANY.RUN·Aug 12, 07:58 UTC · Aug 12, 2026Industry

GraphProfiler: Source-Linked Sensitive Attribute Inference via Personal Knowledge Graphs

GraphProfiler links LLM attribute inferences to source posts via personal knowledge graphs, enabling targeted redaction of privacy-leaking content.

GraphProfiler represents a user's post history as a source-linked personal knowledge graph where nodes and edges trace back to originating posts, making LLM-based attribute inference auditable. It reaches 86.7% attack success rate on the eight-attribute SynthPAI benchmark and 84.6% on PANDORA, within two points of strong text-only baselines, while citing supporting evidence for over 98% of predictions. Ablation experiments show removing cited posts reduces attack success substantially more than removing random posts, supporting targeted privacy mitigation.

arXiv cs.CR·Sep 11, 05:13 UTC · 5d agoResearch1

6 Months on Alert: Get H1 2026 Cyber Risk Report for SOCs and MSSPs

ANY.RUN's H1 2026 report details 15 threat trends including 437% growth in fake CAPTCHA phishing and 90.7% rise in Adobe infrastructure abuse.

The report draws on interactive sandbox submissions from over 700,000 analysts and 16,000 SOC teams between January and June 2026. Attacks abusing Adobe infrastructure grew 90.7% versus H2 2025 while RMM-related attacks rose 26.5%, and custom fake CAPTCHA phishing grew 437% from Q1 to Q2 2026. ANY.RUN argues static IOCs are losing effectiveness as dead drop resolvers hide the final C2 until execution.

ANY.RUN·Sep 15, 09:05 UTC · 1d agoResearch

Technical Manual for a Toolkit for Measuring Contextual Individuation in Transformer Language Models

An open methodology toolkit measures whether transformer language models contextualize fixed word forms across domains using bridge forms and layer-wise silhouette analysis.

The manual documents an open toolkit built around 'bridge forms' - identical written words recurring across two or more subject domains with a different sense in each - to test whether transformer language models individuate word occurrences by context beyond the embedding layer. It covers declarative specification of bridge forms, Wikipedia corpus acquisition, occurrence localization, layer-wise representation extraction, domain-pairwise silhouette measurement, and visualization, justifying each choice against failure modes such as sense contamination and subword-tokenization misalignment. It is a methodological and implementation reference and reports no empirical results.

arXiv cs.AI / cs.LG / cs.CL·Sep 4, 16:38 UTC · 12d agoAI research

N0va Phishkit Targets US and EU Businesses: A New Challenge for Identity Security

N0va phishkit targets US and EU organizations with trusted-brand lures, capturing tokens via legitimate authentication flows to gain SSO access to corporate resources.

The N0va phishing kit targets organizations in government, technology, consulting, and healthcare across North America and Europe with lures impersonating Microsoft Teams, SharePoint, OneDrive, DocuSign, Google Drive, Dropbox, Zoom, and Adobe Sign. Victims are guided through legitimate authentication flows, including device code phishing, after which N0va captures access and refresh tokens and abuses token-exchange or device-registration mechanisms to establish SSO access. ANY.RUN tracks the campaign via a characteristic /api/verification/init URL pattern and demonstrates detection in its interactive sandbox.

The Hacker News·updated Sep 16, 14:54 UTC · 11h ago·first Sep 16, 11:58 UTC · 14h agoPhishing & fraud in the wild 9 sources2

New TCG guidance gives buyers a way to test PQC-ready TPM claims

Trusted Computing Group published PTP 1.07-based requirements defining PQC-ready and PQC-upgradable TPM designations to guide buyers assessing quantum-safe hardware.

The Trusted Computing Group published requirements establishing PTP 1.07, based on TPM 2.0 Library Specification Version 1.85, as the baseline for a 'TCG PQC-ready TPM', and defined a second designation, 'TCG PQC-upgradable TPM', for chips that can be upgraded to that support. The guidance lets buyers request vendor evidence that TPMs meet end-to-end post-quantum requirements for identities, attestation, and hardware-anchored trust rather than single-algorithm compliance. TCG also announced plans to extend its certification programs to certify TPMs meeting PTP 1.07, noting that 90% of businesses still lack a formal PQC roadmap.

Help Net Security·Aug 25, 00:00 UTC · 23d agoTools

New N0va Phishkit Targets North America and EU: A Growing Identity Risk for SOCs

ANY.RUN researchers uncovered the N0va phishkit targeting government, technology, consulting, and healthcare organizations across North America and the EU via device code phishing.

The N0va phishkit uses lures imitating Microsoft Teams, SharePoint, OneDrive, DocuSign, Google Drive, Dropbox, Zoom, and Adobe Sign to draw victims into a device code authentication flow. After the user completes legitimate authentication, N0va captures access and refresh tokens and abuses token-exchange and device-registration mechanisms to establish persistent SSO access to corporate resources. Because the flow relies on real Microsoft authentication, it can evade MFA-focused detections, and token access can outlive takedown of the phishing page. ANY.RUN says it observed the campaign in sandbox sessions, with targeting spanning government, technology, consulting, and healthcare sectors.

Cyber Security News·Sep 9, 16:26 UTC · 7d agoPhishing & fraud in the wild

ToolHive: The open-source way to run any MCP server securely

Stacklok's open-source ToolHive runs Model Context Protocol servers in isolated containers with per-request identity enforcement, audit logging and a signed registry.

ToolHive, shipped under Apache 2.0, containersizes MCP servers locally via Docker or Podman or in clusters through a Kubernetes operator, applying permissions, network filtering, and secrets management. The platform includes a Registry Server implementing the official MCP Registry API with signing and provenance verification, a Virtual MCP Server gateway with OIDC/OAuth single sign-on and OpenTelemetry traces, and a desktop Portal for one-click installs. The browser-based cloud UI is retired, so the desktop app and CLI are the supported rollout paths.

Help Net Security·Sep 7, 00:00 UTC · 10d agoAI tools & infra1

Kali365 Weaponizes Microsoft Authentication Against US Companies: New Enterprise Risk

The Hacker News·Aug 5, 11:43 UTC · Aug 5, 2026Malware1

Studying Image Tokenizers as Visual Languages in Unified Multimodal Models

A controlled autoregressive testbed shows validation losses must be analyzed per task, and image tokenizer choice affects joint multimodal text modeling.

Researchers built a pure-autoregressive testbed to study image tokenizers as the 'visual language' of unified multimodal models, tracking task-specific validation losses during multimodal continual pretraining across text, image, text-to-image (T2I), and image-to-text (I2T) prediction. They found that losses exhibit distinct scaling behavior per task and rank tokenizers differently, and that I2T loss over a shared text vocabulary gives a more consistent loss–performance signal than T2I loss. Better reconstruction does not necessarily yield lower task-specific losses or stronger downstream performance, and tokenizer choice can affect text modeling under joint optimization. Case studies examine the discriminator, semantic supervision, and vocabulary size design axes.

Hugging Face daily papers·Sep 7, 20:00 UTC · 9d agoAI research1

Stellar Cyber's Auto-Triage AI matches human analysts 99.7% of the time

Help Net Security·Aug 5, 00:00 UTC · Aug 5, 2026Ransomware

Google Research Releases ToolGrad: Answer-First Framework Hits 99.8% Pass Rate for Tool-Use Data Generation

Google Research and partners introduce ToolGrad, a verified tool-chain-first data generation framework reaching 99.8% pass rate and boosting Gemma-3-12B to 83.1 on BFCL.

Researchers from Google, the University of Tokyo, RIKEN AIP, and Tohoku University released ToolGrad, which inverts query-first tool-use data generation by executing and verifying API chains before annotating them with user queries. On the ToolBench database of 16,000+ APIs, ToolGrad raised generation pass rate from 63.8% to 99.8% while increasing tool uses per sample from 2.1 to 3.4 and cutting tool-use steps from 34.3 to 20.0. Fine-tuning Gemma-3 at 1B, 4B, and 12B parameters on the 500-sample ToolGrad-500 dataset lifted ToolGrad-12B to 83.1 on the Berkeley Function Calling Leaderboard, near Gemini 2.5 Pro at 83.2 and ahead of GPT-5 at 74.4. Code is Apache-2.0, with the dataset, PyPI package, and models available on Hugging Face.

MarkTechPost·Sep 11, 06:01 UTC · 5d agoAI research1

Mirage2FA Surge Hits 4,500 US and EU Companies, Abusing Microsoft 365 Login Flows

Mirage2FA phishing-as-a-service campaign hit ~4,500 organizations, mostly US, stealing Microsoft 365 passwords and session cookies to bypass 2FA.

ANY.RUN research links the Mirage2FA phishing-as-a-service toolkit to 4,532 unique organization email domains between 2024 and 2026, with the US accounting for 63.7% of victims. The kit uses adversary-in-the-middle login flows to harvest credentials and session cookies, bypassing MFA on Microsoft 365 accounts. Researchers recorded more than 9,000 potential compromise events and estimated 48% of targeted email addresses were potentially compromised. Hijacked sessions extend to SSO-connected services, enabling impersonation, fraud and further compromise.

The Hacker News·Aug 25, 11:56 UTC · 22d agoPhishing & fraud in the wild

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

ANY.RUN & SentinelOne: One Workspace, Instant Context for Rapid Response

ANY.RUN integrates its interactive sandbox, IOC lookups, and STIX/TAXII threat feeds natively into SentinelOne for faster automated malware triage.

ANY.RUN and SentinelOne launched connectors that embed interactive sandbox analysis and threat intelligence into the SentinelOne console via Singularity Hyperautomation. Suspicious files and URLs from alerts are automatically submitted to the ANY.RUN sandbox, with behavioral verdicts and risk scores returned into alert notes. On-demand IOC lookups draw on sandbox history from 16,000 organizations and 700,000 analysts. A separate STIX/TAXII feed streams verified malicious IPs, domains, and URLs through the SentinelOne Marketplace TAXII Connect app.

ANY.RUN·Sep 16, 06:40 UTC · 19h agoTools

Apple's Siri AI Can Be Swapped Out for Claude, ChatGPT, Code Shows

Code analysis of iOS 27 and macOS Golden Gate frameworks shows Apple engineered Siri to deeply interoperate with third-party AI models like Claude and GPT-5.6.

Code sleuth 'pdfu' uncovered private frameworks in iOS 27 and macOS Golden Gate revealing a 'Model Delegation' mechanism that lets Claude act as a Siri extension like the built-in ChatGPT extension. A second 'Inference Providing' protocol in Model Manager Services can fully replace Siri's server-side model with models such as GPT-5.6, which then receives Apple's Siri planner prompt and tool definitions to perform system actions and process personal data. The EU Digital Markets Act, which requires Apple to give third parties effective access to iOS features, may have shaped this approach. The entitlement is not yet open to third parties and Claude is not yet available in the macOS 27 Golden Gate RC.

Hacker News · AI·Sep 14, 12:01 UTC · 2d agoAI industryHN 57↑ · 14 comments

LiteLLM Supply-Chain Attack - Technology, Banking and Healthcare the Most Affected

TeamPCP planted the SANDCLOCK credential stealer in LiteLLM PyPI releases, exposing credentials across 2,038 repositories at 898 organizations including Microsoft and NVIDIA.

Threat actor TeamPCP compromised LiteLLM maintainer credentials and published malicious versions 1.82.7 and 1.82.8 to PyPI around March 2026, creating an exposure window of several months. The SANDCLOCK credential stealer exposed full credential sets across 898 GitHub owners and 2,038 repositories, including Microsoft, Azure, IBM, NVIDIA, PayPal, Deloitte, Bosch, and S&P Global. Stolen material includes GitHub CI/CD identities, AWS/GCP/Firebase credentials, SSH keys, Kubernetes secrets, and OpenAI and Anthropic API keys; Resecurity acquired a 150GB archive with 2,146 credential records. Technology, banking/finance, and healthcare organizations are the most affected sectors, and victims must rotate all exposed credentials.

Security Affairs·Aug 17, 17:09 UTC · Aug 17, 2026Malware in the wild

ActReview: Rebuttal-Guided Training Data and Rubric Rewards for Actionable Peer Review Generation

ActReview post-trains Qwen3-8B-Base on OpenReview rebuttals to generate actionable peer-review feedback with grounded revision suggestions, benchmarked on 1,000 curated instances.

The paper defines Actionable Peer-review Generation as diagnostic claim generation plus revision suggestion generation and introduces ActReview, a rebuttal-guided post-training framework. From OpenReview review-rebuttal threads the authors build ActReview-40K, aligning reviewer weaknesses with author responses grounded in localized paper evidence, and post-train Qwen3-8B-Base with multi-task SFT followed by GRPO using weakness-specific rubric rewards. They also release ActReview-Bench, a human-curated 1,000-instance benchmark, on which ActReview outperforms prior specialized review-generation models on actionability and grounding while remaining competitive with strong prompt-based LLMs. Human evaluation confirms improved revision usefulness but identifies a remaining gap in technical accuracy.

Hugging Face daily papers·Sep 7, 20:00 UTC · 9d agoAI research

Investing Together: Wiz Defend and Google Security Operations

Wiz ships a Content Pack for Google Security Operations enabling shared investigations, Blue Agent AI analysis, bidirectional sync and cloud telemetry streaming.

Wiz announced deeper integration between Wiz Defend and Google Security Operations via a shared data model and an official Wiz Content Pack with out-of-the-box rules, dashboards, search queries, playbooks, and response policies. Wiz Blue Agent AI-powered threat investigations, correlating cloud context, runtime signals, and identity data, are now accessible directly within Google SecOps. Status, severity, and comments bidirectionally sync in real time, and sensor runtime events can be streamed into Google SecOps for hunting and retention.

Wiz Blog·Sep 15, 12:49 UTC · 1d agoTools1

Studying Image Tokenizers as Visual Languages in Unified Multimodal Models

A controlled pure-autoregressive testbed shows task-specific validation losses rank image tokenizers differently, with I2T loss the most consistent signal.

Researchers built a controlled pure-autoregressive testbed and tracked task-specific validation losses during multimodal continual pretraining across text, image, text-to-image (T2I), and image-to-text (I2T) prediction. They find losses should be analyzed per task because they exhibit distinct scaling behavior and rank tokenizers differently, and that the loss-performance relationship depends on the predicted token space. I2T loss, computed over a shared text vocabulary, correlates consistently with both generation and visual understanding performance after supervised finetuning. Case studies revisit the discriminator, semantic supervision, and vocabulary size as tokenizer design axes.

arXiv cs.AI / cs.LG / cs.CL·Sep 8, 17:57 UTC · 8d agoAI research1

ANY.RUN Secures Leader Status in G2’s Malware Analysis Rankings

ANY.RUN announced G2 Fall 2026 Grid Leader and Momentum Leader recognition in the malware analysis category, citing 700,000+ users.

ANY.RUN announced it was named both Grid Leader and Momentum Leader in G2's Fall 2026 malware analysis rankings, citing customer satisfaction and market momentum. The vendor states its Interactive Sandbox is used by over 700,000 security professionals and 16,000+ SOC and MSSP teams across Windows, Linux, macOS, and Android. It also cites claimed efficiency gains, including faster investigations for 95% of SOCs and MTTR reductions of up to 21 minutes per case.

ANY.RUN·updated Sep 16, 15:12 UTC · 11h ago·first Sep 10, 09:39 UTC · 6d agoIndustry 2 sources1

m-a-p/YuE2-3B — new model trending #30 on Hugging Face

M-A-P released YuE2-3B, an open music generation model that outperforms Suno v5 on WildSongBench and runs locally on a 24GB GPU.

The M-A-P (multimodal-art-projection) team released YuE2-3B, an open-weights music generation model that turns lyrics and a style prompt into full songs with vocals and accompaniment. It uses an AR-NAR Mixture-of-Transformers backbone with symbolic planning and flow matching through a VAE, and supports editable scores (melody and chords, including ABC notation) plus agentic editing workflows. On 192 WildSongBench prompts it reports a SongBench average of 6.9632 (best-of-8) versus 6.8721 for Suno v5, claimed as state of the art among evaluated open and proprietary models. It runs 48 kHz stereo inference locally on a single 24GB NVIDIA GPU without quantization, with companion releases including YuE2-Vae, MERT-v2 encoders, the WildSongBench dataset, and SheetSage2.

Hugging Face trending models·Sep 9, 18:30 UTC · 7d agoModel release1

Release Notes: Faster TI Investigations, Fresh Threat Research, and 650+ Threat Coverage Updates

ANY.RUN August release adds TI Lookup connections view, 81 behavior signatures, 16 YARA rules, 559 Suricata rules, and three new threat intelligence reports.

ANY.RUN released August product updates expanding its Threat Intelligence Lookup with a Connections block for pivoting between related observables (domains, IPs, URLs), JSON export for retrohunting and SIEM/NDR integration, and hidden whitelisted data by default. Detection coverage grew with 81 new behavior signatures, 16 YARA rules, and 559 Suricata rules covering malware execution, phishing, and C2 traffic. Three new Threat Intelligence Reports cover a US-focused RMM phishing campaign across 46 countries, the Mirage2FA phishing-as-a-service targeting Microsoft 365 (1,249 sandbox sessions, 9,332 potential compromise events), and a threat brief on OVERLORD RAT, CRPX0, and TRIBACK loader.

ANY.RUN·Sep 3, 09:11 UTC · 13d agoTools1

AgentGrad: Intervention-guided Prompt Optimization for Multi Agent Systems

AgentGrad introduces intervention-guided prompt optimization for LLM multi-agent systems, achieving state-of-the-art results with 2.5x faster optimization.

AgentGrad is a prompt optimization framework for LLM-based multi-agent systems that addresses limitations in textual gradient extraction and aggregation. It uses sequential intervention to identify the agent whose prompt modification resolves a given failure, then applies agent-level supervision and semantic gradient clustering to build generalized gradients. Experiments report state-of-the-art performance across five MAS benchmarks and a 2.5x average reduction in wall-clock optimization time versus the next-fastest baseline.

Hugging Face daily papers·Sep 7, 20:00 UTC · 9d agoAI research

Triage & Response Bottlenecks Eating into MSSP Margins: How to Remove the Friction

ANY.RUN says manual triage bottlenecks erode MSSP margins and promotes its Threat Intelligence Lookup and sandbox to cut Tier 1 workload by up to 20%.

Vendor blog post from ANY.RUN describes recurring MSSP triage and response bottlenecks: manual alert validation, unnecessary Tier 2 escalations, manual handoffs, outdated threat data, and disconnected tools. It cites a healthcare MSSP case study where the Tier 1 closure rate rose from 20% to 70% and false escalations dropped 34% after adopting its tools. The piece claims ANY.RUN can reduce Tier 1 workload by up to 20%, letting MSSPs absorb more clients without proportional headcount growth.

ANY.RUN·Sep 2, 07:18 UTC · 14d agoIndustry

Build Low-Latency Multilingual Voice Agents: Open Weights & Full Deployment Control with NVIDIA Magpie TTS

NVIDIA's Magpie TTS open-weight multilingual speech model enables low-latency voice agents with full deployment control.

Hugging Face's blog highlights NVIDIA Magpie TTS, an open-weights multilingual text-to-speech model designed for building low-latency voice agents. The open licensing gives developers full deployment control, allowing self-hosted multilingual speech for agentic applications. The post walks through building voice agents with the model.

Hugging Face Blog·Aug 10, 16:25 UTC · Aug 10, 2026Model release

How to implement a continuous offensive security testing program

Help Net Security·Aug 10, 12:13 UTC · Aug 10, 2026Exploit / PoC1

Product showcase: Trust Chain TPRM turns vendor compliance evidence into verified assurance

Help Net Security·Aug 10, 12:30 UTC · Aug 10, 2026Policy & legal

Cybersecurity jobs available right now: February 17, 2026

Help Net Security·Aug 6, 12:22 UTC · Aug 6, 2026Vulnerability

NCP-ArchPreview Technical Report: Moving towards Latent Space Language Models through Next Concept Prediction

An 8.9B-parameter latent-space language model using next-concept prediction matches OLMo-3-7B pretraining loss with only 51.3% of the training tokens.

NCP-ArchPreview augments next-token prediction with Next Concept Prediction over a product-quantized concept vocabulary built from hidden states, trained jointly end-to-end. The 8.9B model was trained on 5.73T tokens from the Dolma-3 dataset, the largest latent-space language model demonstration to date. It consumes 51.3% of the tokens to reach OLMo-3-7B's final pretraining loss and outperforms it by 2.45 points on the downstream macro-average, including a 5.99-point GSM8K gain. The learned latent space also enables lightweight domain adaptation via a 17M-parameter VQ module and improves speculative drafting accepted length by 4.17%.

Hugging Face daily papers·Sep 8, 20:00 UTC · 8d agoAI research1

Phishing Powers 80% of Attacks on US Companies: How SOCs Can Detect It Early

Phishing drives 80% of attacks on US companies; FBI recorded 158,436 BEC victims and over $20B in losses from 2013-2023.

A vendor-authored analysis claims phishing powers roughly 80% of attacks on US companies, citing FBI statistics of 158,436 business email compromise victims and more than $20 billion in reported losses between 2013 and 2023. It describes modern phishing using compromised infrastructure, redirect chains, dynamic pages, and adversary-in-the-middle techniques, including a recent campaign targeting tens of thousands of primarily US users. The piece promotes ANY.RUN Threat Intelligence Feeds and TI Reports, claiming 99% unique high-confidence IOCs, 21-minute faster MTTR, and 30% fewer Tier 2 escalations.

Cyber Security News·Sep 8, 17:54 UTC · 8d agoPhishing & fraud

LandingAI Releases Agentic Document Extraction Gen2 with DPT-3 Pro and DPT-3 Verity

LandingAI shipped Agentic Document Extraction Gen2 with DPT-3 Pro and DPT-3 Verity parsing models, adding usage-based billing, block-tree outputs, and word-level grounding.

LandingAI has generally released Agentic Document Extraction Gen2, rebuilt around two parsing models: DPT-3 Verity for deterministic transcription of digital documents with per-word bounding boxes and confidence scores, and DPT-3 Pro for layout-aware parsing of scans, handwriting, non-Latin scripts, and LaTeX math. Billing changes from a flat 3 credits per page to a page-plus-output-character model (Pro: 1 credit/page plus 0.5 credits per 1,000 output characters on priority; Verity: 0.3 plus 0.2), with an asynchronous standard tier at 0.5x price and vendor-claimed 25-80% cost reductions. Parse v2 returns a document-page-block tree with semantic IDs, normalized bounding boxes, and line- or word-level atomic grounding, replacing flat chunks; Gen1 client code will not run against Gen2 endpoints. Deployment options include US/EU cloud, VPCs on AWS, Azure, and Google Cloud, Snowflake, and air-gapped on-premises environments, with automated model routing planned for fall 2026.

MarkTechPost·Sep 10, 02:13 UTC · 7d agoAI tools & infra

15 Minutes Saved Per Alert: How a Lean German Manufacturer Protects 10,000 Endpoints with ANY.RUN

A five-person security team at a German manufacturer protecting 10,000 endpoints cut triage time by 15 minutes per alert after adopting ANY.RUN's cloud sandbox.

Philipp Z., Security Lead at a leading German manufacturer, described how a five-person team protects 10,000 endpoints and users using ANY.RUN's Interactive Sandbox in a private cloud. The firm previously relied on a single air-gapped forensic laptop running Flare VM, which caused 5-10 minute setup delays, single-user bottlenecks, and selective triage. The switch reportedly saved roughly 15 minutes per alert and reduced forced wiping and reimaging of user machines. ANY.RUN data cited in the piece puts manufacturing security workloads 22% above other major industries.

ANY.RUN·Sep 9, 07:47 UTC · 7d agoIndustry

HoneyRoute: Honeypot-Model Routing for Adversarial LLM Serving

HoneyRoute detects malicious LLM serving requests and diverts them to a honeypot model, reaching F1 0.911 with 38 ms median added latency.

HoneyRoute is an inference-serving layer pairing a streaming router (a frozen 0.8B embedding backbone with per-domain MLP heads) with a dual-implementation honeypot and an analysis loop that converts trapped interactions into attacker fingerprints for router retraining. On a production trace plus a seven-domain attack corpus it matches 96% of a two-tier guard-LLM cascade's F1 at 1/385th of its latency with 0% evasion under 13 adversarial transformations. Diverting malicious traffic cuts production token consumption under GCG-suffix flooding by 97.8%, and loop training raises detection F1 to 0.933.

arXiv cs.CR·Sep 8, 06:26 UTC · 8d agoAI safety & security

How to Keep Malware’s Rotating Infrastructure From Becoming a Detection Gap

ANY.RUN marketing piece argues SOC detection lags rotating malware and phishing infrastructure, citing a 46-country campaign and 3DBlast kit to promote TI feeds.

ANY.RUN describes how malware and phishing campaigns rotate domains and hosting, making single-IOC blocking ineffective for SOC teams. One investigated phishing campaign spanned 46 countries with 425 kit URLs across 240 hosts, 94% of which were seen for only a single day. A phishing kit dubbed 3DBlast impersonates Microsoft 365 and Google using BitB, AiTM, OAuth device-code phishing and DOM relay techniques. The article is primarily a promotion for ANY.RUN TI Feeds and TI Lookup products.

Cyber Security News·Sep 15, 15:36 UTC · 1d agoIndustry

Best Practices for Good Endpoint Hardening | Huntress

Huntress outlines endpoint hardening best practices, citing exposed RDP, RMM tool abuse, and ClickFix social engineering as common SMB intrusion paths.

Huntress published defensive guidance on endpoint hardening for small and mid-sized businesses, drawing on observations from its SOC. The post describes common intrusion vectors: internet-exposed RDP brute-forced by automated scanners, phishing emails delivering attacker-controlled remote monitoring and management (RMM) tools, with Huntress reporting a 277% spike in RMM abuse in 2025, and ClickFix attacks using fake CAPTCHA pages that trick users into running malicious commands. Recommended controls include scanning for exposed RDP, SSH, and VPN interfaces, removing unneeded local admin rights, enabling Windows Defender tamper protection, disabling SMBv1, and standardizing on one approved remote access tool, guided by CIS and NIST frameworks.

Huntress·Sep 10, 15:00 UTC · 6d agoAdvisory

When Should LLMs Abstain? Chain-of-Self-Questioning for Selective Risk Control

Chain-of-Self-Questioning prompting cuts LLM wrong-answer commitments 32% relative while raising answered accuracy, holding across eleven model families.

The paper introduces Chain-of-Self-Questioning (CoSQ), a prompt-only framework that makes LLM answer commitment conditional on an explicit assessment of the information required to answer. On an 817-item TruthfulQA multiple-choice set, Grounded-CoSQ at τ=0.90 reduced mean unconditional wrong-commitment rate from 13.1% under chain-of-thought to 8.9% (a 32.1% relative reduction), while raising answered accuracy from 86.9% to 89.7% at 87.6% coverage. Improvements held across eleven open-weight and hosted model families and at every evaluated threshold, with convergent evidence from a Natural Questions short-answer evaluation.

arXiv cs.AI / cs.LG / cs.CL·Sep 15, 17:52 UTC · 1d agoAI research

JustFit: 200K-Token LLM Serving on a 24 GiB Laptop with Just-in-Time State Management

JustFit MLX runtime serves 200K-token contexts for Qwen3.8-27B on a 24 GiB MacBook via just-in-time state management.

JustFit is an MLX-based inference runtime combining KVExec for compressed KV execution, PhaseSwap for component residency, and StateTrans for state-preserving serving transitions, independent of weight quantization. On a 24 GiB M4 Pro MacBook running Qwen3.8-27B MXFP4, it completed 196,608 input and 16,384 output tokens, raising single-request context from the mlx-vlm baseline's 30,720 positions to 212,992 (6.93x). Performance tests show 19.11 tokens/s on a 32K-input probe with a 16,374 MiB median peak footprint, and the runtime answered 29 of 30 AIME 2026 problems correctly.

arXiv cs.AI / cs.LG / cs.CL·Sep 15, 17:15 UTC · 1d agoAI research

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.CL·Sep 15, 17:42 UTC · 1d agoAI research

Cadence: Error-Bounded Lossy Compression of Demand Time Series with a Time-Series Foundation Model

Cadence pairs Google's 330M-parameter TimesFM-3 foundation model with adaptive arithmetic coding, gaining 13-28% on 2026 demand series over classical predictors.

Cadence is an error-bounded lossy compressor for numeric time series combining the 330M-parameter Google TimesFM-3 foundation model with an adaptive arithmetic coder, guaranteeing a per-sample error bound. On 49 EIA-930 balancing-authority demand series from 2026 it gains 13.3% over the best of six classical predictors and 28.3% on 50 MTA ridership series, winning all 297 series-tolerance pairs with a 21.4% median gain. The paper also reports negative results, including that foundation models add negligible value for lossless coding and that PyTorch predictions are not bit-identical across batch sizes.

Hugging Face daily papers·Sep 4, 20:00 UTC · 12d agoAI research1