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VectraRAT: An Undocumented Full-Stack MaaS Built From Scratch

SOCRadar's Threat Research Unit documents VectraRAT, a previously unreported full-stack Malware-as-a-Service platform built entirely from scratch.

SOCRadar's Threat Research Unit (STRU) documented VectraRAT, an undocumented Malware-as-a-Service platform. Unlike most MaaS offerings derived from leaked builders, VectraRAT was built entirely from scratch. The report details the platform's architecture and capabilities offered to affiliate customers.

SOCRadarupdated · 22h agofirst · 3d agoMalware 3 sources

Veeam Released Patches for Critical Vulnerability (CVE-2026-65641)

Veeam patched critical CVE-2026-65641 (CVSS 9.3) in Veeam ONE, exploitable by unauthenticated network attackers to coerce SMB authentication from the service account.

Veeam released patches for its Veeam ONE monitoring, reporting, and capacity planning software addressing CVE-2026-65641, rated critical with a CVSS score of 9.3. Successful exploitation allows an unauthenticated network attacker to coerce SMB authentication from the Veeam ONE service account, creating relay-style attack opportunities. Qualys ThreatPROTECT relayed the vendor advisory; administrators should apply the patches promptly. No in-the-wild exploitation is noted in the advisory.

Qualys ThreatPROTECT · 16d agoVulnerabilityCVE-2026-65641

Veeam security advisory (AV26-855)

Canada's Cyber Centre advisory AV26-855 says Veeam Backup & Replication and Veeam ONE vulnerabilities are resolved in 13.0.3 and 13.1 updates.

Advisory AV26-855, dated August 27, 2026, states that as of August 25 Veeam is affected by vulnerabilities in Backup & Replication (prior to 13.0.3 build 13.0.3.63 and prior to 13.1 build 13.1.0.411) and Veeam ONE (prior to or equal to 13.0.2.6723 and 13.1.0.7034). Fixes are documented in KB4902 (Veeam Backup & Replication 13.1) and KB4905 (Veeam ONE 13.1 Patch 0). The Cyber Centre urges users and administrators to apply the available updates. No CVE identifiers or exploitation details are given.

Canadian Centre for Cyber Security · 20d agoAdvisory

Litelm: LiteLLM Without the Bloat

New open-source project Litelm launched on GitHub as a leaner alternative to the LiteLLM multi-provider LLM gateway.

Litelm is a community-published GitHub project positioned as a stripped-down alternative to LiteLLM, a widely used gateway/SDK for routing requests to multiple LLM providers. The post drew modest attention on Hacker News with 45 points and 17 comments. The listing provides no further technical details, benchmarks, or feature specifics.

Hacker News · AI · 5d agoAI tools & infraHN 45↑ · 17 comments2

$1 Million Sandbox Challenge Uncovers Linux Kernel Flaws

Vercel's $1M sandbox challenge surfaced two Linux kernel networking defects—one leaking host kernel memory, one crashing hosts—with CVEs pending.

Vercel ran a two-week, $1 million sandbox escape challenge (Aug 18–Sep 1) on its Firecracker-based microVM sandbox, receiving 1,285 reports and committing ~$325k in payouts (1 Critical, 7 High, 15 Medium, 49 Low validated so far). No attacker accessed real customer data. The most important filing found two independent Linux kernel networking stack defects—one leaks host kernel memory, the other deterministically crashes the host—with wide implications for cloud providers isolating workloads via the same kernel layer. Fixes are under private review with CVEs pending; Vercel also plans to open-source its agentic report-triage agent built on the Eve framework running Kimi K3.

SecurityWeek · 1d agoVulnerability2

N-able ships second N-central hotfix as attackers keep exploiting CVE-2026-18577

N-able shipped a second N-central hotfix as attackers actively exploit authentication bypass CVE-2026-18577, attributed to ransomware group Storm-1175.

N-able released Hotfix 2 (N-central 2026.3.1.10) to counter ongoing exploitation of CVE-2026-18577, an authentication bypass that evades the patch for the earlier CVE-2026-18556; exploitation was first detected on August 1, 2026. Post-exploitation includes using the Take Control feature to reach managed endpoints, registering a Cloudflare tunnel service for persistence, creating a 'veeam' domain account, resetting admin passwords, and disabling Microsoft and Sophos security tooling. Microsoft analysts link the activity to Storm-1175, which now deploys a new StormEncryptor ransomware strain instead of Medusa, often reaching data exfiltration and ransomware within days. Sophos and Huntress expanded the IOCs and warned that partners who patched late should treat environments as potentially compromised.

Help Net Security · Aug 12, 2026Exploit / PoC in the wildCVE-2026-18577CVE-2026-185561

Fortunate Recall: Ontology-Driven Memory Lifecycle Management for Persistent Coherence in LLMs

Fortunate Recall introduces ontology-based lifecycle policies for LLM memory, cutting confabulation roughly in half (e.g., 45.1% to 22.4%) versus Mem0.

Fortunate Recall (FR) is a composable policy layer that classifies personal facts into a 10+1 behavioral ontology and applies category-specific lifecycle rules including differential temporal decay, slot-key supersession, event-time validity, and retrieval routing. FR-Bank scores 76.9% on the new 516-question LifecycleBench, ahead of Mem0, A-MEM, Memory-R1, and MemoryOS (61%-70.5%), and 75.2% on LongMemEval-S. End-to-end, confabulation drops from Mem0's 45.1% to 22.4% over answered queries, with the ranking replicating on open-weight Kimi K2.5 and transferring to the independent BEAM benchmark (46.8% vs 32.9%).

arXiv cs.AI / cs.LG / cs.CL · 7d agoAI research

Jev: New frontier model 40-400x cheaper and 20-200x faster

TypeSafe AI launches Jev, an early-access 'System One' model delivering calibrated structured outputs claimed 40-400x faster and cheaper than LLMs.

TypeSafe AI, founded by former OpenAI researcher Diogo Almeida, released its first 'System One Model' called Jev in early access. Jev forgoes string generation and is trained with Reinforcement Learning for Calibrated Decisions (RLCD) to produce type-safe structured values with calibrated probabilities. The company claims 70-500ms response times (40-200x faster), input pricing of $0.042 per million tokens, and free output tokens via a parallel sampling architecture. Target use cases include AI-powered workflows, real-time applications, and verification/guardrail tasks.

Hacker News · security · 1d agoModel releaseHN 59↑ · 11 comments

Ransom Busters Claims It Hacked Ransomware Servers, Asks Victims for Up to $60,000

GuidePoint reports a ransomware affiliate posing as 'Ransom Busters' charges victims $20,000-$60,000 to delete stolen data, and details UNC6671's $8M AitM extortion wave.

GuidePoint's GRIT team reports that 'Ransom Busters', likely a ransomware affiliate active across multiple RaaS operations including DragonForce, Settra and Anubis, proactively emails victims claiming it deleted their stolen data and backups for a $20,000-$60,000 fee, citing claimed access to RaaS administrative panels for over three years. Two analyzed intrusions shared tooling: SoftPerfect Network Scanner for reconnaissance, s5cmd-based exfiltration to AWS cloud storage, an RMM tool installed via PowerShell, a backdoor account with password 'Numlock!123' and the same attacker hostname DESKTOP-BBETH6K. Separately, GRIT detailed UNC6671's (Cordial Spider) adversary-in-the-middle vishing operation running since April under five extortion brands, with more than $8 million across 15 Bitcoin wallets, an average of $600,000 per payment, and 78 phishing sub-domains across 76 organizations, 40% in financial services.

The Hacker News · 29d agoThreat actor in the wild1

Towards Tackling Application Logic Flaws through Autonomous Formal-Logic Modeling and Automated Reasoning

LL-Verifier combines LLMs with logic model checking to automatically discover logic flaws, uncovering vulnerabilities in 27 IoT access-control protocols.

Researchers present LL-Verifier, a framework that uses LLMs to autonomously convert natural-language protocol descriptions and security goals into formal logic models in a new logic language built on Maude, then applies logic model checking for exhaustive verification. The framework targets application-logic flaws that are tied to business semantics and hard to scale with manual analysis. Evaluation on 27 access-control protocols of widely used IoT devices uncovered a range of sophisticated logic vulnerabilities with security and privacy implications.

arXiv cs.CR · 7d agoResearch1

Présentation de Meta One : Un service d’abonnement offrant davantage de fonctionnalités et d’IA pour créer, se connecter et se démarquer

Meta launches Meta One subscription bundles with expanded Meta AI usage and creator/business tools, already at 15 million subscriptions and trials.

Meta launched Meta One, a new subscription service across Instagram, Facebook, WhatsApp and Meta AI with more than 50 features and 15 million subscriptions and trials to date. Core and Premium bundles add heavier use of compute-intensive AI features, including image creation/editing and video generation via the Muse models, plus tools like Restyle on Instagram. Pricing starts at EUR 2.49/month for single-product plans, EUR 6.99 for bundled consumer plans and EUR 16.99 for creator/business bundles; the core Meta AI experience remains free.

Meta Newsroom · 1d agoAI industry

VEX-Bench: Benchmarking LLM Agents for Assessing Exploitability of Software Supply Chain Vulnerabilities

Introduces VEX-Bench, 75 expert-labeled real-world cases testing whether LLM agents can assess supply chain vulnerability exploitability; frontier models reach about 80% F1.

VEX-Bench is the first benchmark evaluating LLM agents on assessing whether upstream dependency vulnerabilities are exploitable in downstream projects, with 75 real-world expert-labeled cases across Python, Java, and Go mined from GitHub. Nine models across three agent harnesses were evaluated; GPT-5.5 and Claude Opus 4.6 reach approximately 80% F1 on binary vulnerability-status classification, but only GPT-5.5 surpasses 70% macro-F1 on fine-grained justification classification. The gap highlights the difficulty of moving beyond binary exploitability calls to explaining exploitability reasons, unlike prior benchmarks targeting zero-day settings.

arXiv cs.CR · 9d agoResearch1

How CHERIoT Provides Strong and Usable Isolation Without an MMU

ACM Queue paper explains how CHERIoT uses CHERI hardware capabilities to give microcontroller-class IoT devices memory and privilege isolation without an MMU.

The ACM Queue article (DOI 10.1145/3831361) describes CHERIoT, a RISC-V-derived platform that scales CHERI capability-based hardware down to small embedded and IoT microcontrollers. It provides strong, fine-grained isolation and memory safety without a memory management unit, aiming to make compartmentalization practical for low-cost devices. The piece emphasizes usability of the isolation model alongside its security guarantees.

Lobsters · security · 6d agoResearch

Akamai Valkey Managed Database: Real-Time Memory for Enterprise AI

Akamai launched Valkey Managed Database, a low-latency in-memory data layer aimed at cutting AI inference costs and accelerating RAG.

Akamai introduced Valkey Managed Database, a managed in-memory data service based on the open-source Valkey project. The company positions it as real-time memory for enterprise AI, optimizing inference costs, accelerating retrieval-augmented generation, and powering real-time AI agents.

Akamai Blog · 29d agoAI tools & infra1

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 · 12d agoAI research

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.CR · 2d agoResearch

What LLM Trading Agents Actually Do in Production: A Six-Month, Population-Scale Record from Two Fleets

Six-month record of 7.5M LLM trading agent invocations shows volatility-blind sizing, minimal upside capture, and no directional edge across two fleets.

The study records autonomous LLM trading agents in production across DX Terminal Pro (3,505 user-funded vaults trading real ETH in Base memecoin markets) and the DXAP fleet (500-599 agents on Hyperliquid perpetuals), spanning roughly six months, 7.5M single-model invocations and about 300K onchain actions. A risk slider explains leverage (+0.425 per level), median leverage is 5.0x in every volatility sextile, and one posture-slider cell holds 62% of liquidations. Agents capture little upside: 43.2% of positions saw +300 bps favorable excursion within 24h yet 49.3% of those closed negative, while the DXAP fleet trails a matched retail benchmark (41% vs 50% roundtrip win rate). A paired-replay league of frontier models finds decision quality statistically indistinguishable at this horizon.

Hugging Face daily papers · 13d agoAI research

WearableQA: A Benchmark for Health Reasoning over Real-World Wearable Data

WearableQA benchmark tests LLM health reasoning over longitudinal wearable data; the best of 14 evaluated LLMs reaches 72.9% accuracy.

WearableQA comprises 4,084 ten-option multiple-choice questions built from wearable time series, blood biomarkers, and demographics of 200 real users with up to 500 days of daily measurements. It defines 16 question types along two axes: data versus health reasoning, and single- versus cross-signal reasoning. Evaluation of 14 proprietary and open-source LLMs shows performance from 19.6% to 72.9% against a 10% chance baseline, with most models below 60%.

arXiv cs.AI / cs.LG / cs.CL · 12d agoAI research

[AINews] Jev: a “System One Model” that only decides/classifies/routes/scores — >100x faster, >200x cheaper than small frontier LLMs

TypeSafe launches Jev, an RLCD-trained decision model claiming 20-200x faster, 40-400x cheaper classification than frontier LLMs, alongside Gemini 3.8 Live and Neon.

TypeSafe's Jev is a 'System One' decision model trained with RLCD, claiming 20-200x faster and 40-400x cheaper classification and routing than frontier LLMs with free output tokens and no hallucinated text. Google launched Gemini 3.8 Live and 3.8 Live Extended Thinking, supporting 97 languages and async tool calls, debuting #1 on Artificial Analysis' speech-to-speech index at 82.6. Periodic Labs' Neon is a ~1T-parameter XRD analysis model trained with RL on proprietary lab data using 1,300 H200s, lifting FrontierXRD success from 2.7% to 55.3% and beating GPT-6 Astra at lower inference cost.

Latent Space · 1d agoModel release1

Training a 3.8B LLM to 0.384 CORE for $998 – Hugo Vergnes

Independent developer Hugo Vergnes trained a 3.8B-parameter Llama-style model to 0.384 CORE on 65B tokens for $998 in 43 hours on rented B200s.

Hugo Vergnes trained little-lm, a 3.848B-parameter decoder-only LLM, on 65.3B tokens in 43 hours for $998 using rented NVIDIA B200s, scoring 0.384 on the CORE benchmark and beating nanochat d32 (0.310) at similar cost. The Llama-style architecture uses RMSNorm, RoPE, GQA with 24 query and 8 KV heads, relu-squared MLPs, QK-norm, and ResFormer-style value embeddings that account for 19% of parameters. Key wins included the Muon optimizer for matrix parameters, a trapezoidal learning-rate schedule with linear cooldown, FP8 training plus vocabulary padding for roughly 33% throughput gains, and the ClimMix dataset over FineWeb-Edu. The project, inspired by Karpathy's nanochat, was built as a config-driven YAML framework for small LLM training.

Hacker News · security · 7d agoAI researchHN 28↑ · 2 comments1

The Self-Expanding Stolen Inference Supply Chain: An AI Agent Harvesting and Re-Serving LLM Access, (Fri, Sep 11th)

An autonomous coding agent harvested LLM API access from poorly secured gateways and aggregated stolen inference capacity behind a self-hosted gateway

A SANS researcher observed a semi-autonomous coding agent finding weakly secured LLM resale gateways via FOFA queries, creating trial accounts with temporary emails and CAPTCHA solving, and exploiting weak authorization such as client-supplied group_id fields. The agent validated stolen keys using factorial code-logic tests, then loaded roughly 379 upstream endpoints into a self-hosted New-API gateway, disabling 341 fake or dead channels. Five model names including claude-opus-5 and gpt-5.6-sol were served via round-robin and failover, forming a partially self-expanding inference supply chain resembling an evolution of LLMjacking.

SANS Internet Storm Center · 5d agoThreat actor in the wild

US Finance Under Phishing Pressure: What the SOC Data Reveals?

ANY.RUN SOC telemetry shows escalating phishing campaigns against US finance, including Vercel-hosted RMM attacks abusing legitimate services.

ANY.RUN analyzed SOC telemetry data on phishing targeting the US financial sector, concluding that the scale and security impact should not be understated. The analysis highlights modern campaigns such as Vercel-hosted attacks that deliver remote monitoring and management (RMM) tools. It notes that attackers increasingly abuse legitimate services and everyday workflow tools to deliver phishing, making detection harder for SOC teams.

ANY.RUN · 22d agoPhishing & fraud in the wild

Introducing Muse: The World’s First Personal AI Agent Built for Everyone

Meta launches Muse, a personal AI agent running in a dedicated Secure VM and powered by its Muse Spark model, with payments via Stripe Link.

Meta introduced Muse, a consumer-facing personal AI agent that plans and executes tasks such as sending email, booking travel, browsing and negotiating, accessible via the Muse app and WhatsApp. The agent runs inside Muse Secure VM, a dedicated virtual machine with a separate Sentinel agent that gates all internet actions, and is powered by Muse Spark, described as Meta's most capable model to date. Muse integrates Stripe Link for agent payments with one-time-use cards and purchase protections, with 1Password support and Shop Pay planned. It rolls out in the US on iOS, Android and muse.ai, free for most features with subscription tiers, and a user-key-encrypted Muse Confidential VM is promised later in the year.

Meta Newsroom · 8d agoAI industry 3 sources

VectraRAT Can Hack Windows Enterprises for $250 per Month

VectraRAT malware-as-a-service sells a Windows implant with C2 infrastructure and operator panel for $250 per month.

VectraRAT is a full-service malware-as-a-service platform offering a Windows implant, command-and-control infrastructure, and an operator panel for comprehensive remote access to infected enterprises. Subscriptions cost $250 per month, lowering the barrier for criminal operators. The platform bundles all components needed to run remote-access campaigns.

Dark Reading · 1d agoMalware

Meta Introduces Muse, a Personal AI Agent That Runs on Its Own Dedicated Secure Cloud Computer

Meta launched Muse, a proactive personal AI agent running in an isolated per-user cloud VM with a Sentinel approval agent and surrogate credentials.

Meta introduced Muse, a consumer agent that performs long-horizon tasks like email, travel booking, and bill negotiation, rolling out in the US on iOS, Android, muse.ai, and WhatsApp with free and paid tiers. Each user gets a dedicated Muse Secure VM where the agent runs in a systemd-nspawn cell, while a separate Sentinel agent approves every network request at layer 4/7 and injects real credentials only at the network boundary. The underlying Muse Spark 1.3 model, which Meta says cuts tool calls by ~20% and tokens by ~25% versus 1.2 and is near state-of-the-art on prompt-injection resistance, is available via Meta Model API, with open weights on the roadmap.

MarkTechPost · 8d agoAI industry

One Symptom, Three Levers: A Critical Review of On-Policy Self-Distillation

A review paper frames on-policy self-distillation collapse as governed by three levers: token weighting, privileged information, and guidance decay.

The paper critically reviews On-Policy Self-Distillation (OPSD), where a language model trains on its own generations scored token-by-token by a teacher conditioned on privileged information such as reference solutions or environment feedback. It identifies collapse, the progressive narrowing of producible reasoning paths, as the dominant failure mode and analyzes it through three levers: signal weighting, the nature of privileged information, and teacher dynamics. The review is restricted to mathematical reasoning, reports no new experiments, and offers a shared vocabulary separating settled findings from disputed ones.

Hugging Face daily papers · 22d agoAI research

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 · 1d agoAI research

OpenWAM: An Open, Modular Exploration Towards Systematic World-Action Model Pretraining

OpenWAM releases an open modular stack for world-action model pretraining, plus OpenWAM-alpha trained on about 6,400 hours of egocentric and robot data.

OpenWAM is an open research stack that factorizes World-Action Model pretraining into composable infrastructure, study, and model components with unified training, inference, and evaluation. Controlled experiments distill three principles on knowledge inheritance, world-action synergy, and out-of-domain generalization gains from embodied co-training. The resulting OpenWAM-alpha, pretrained on roughly 6,400 hours of egocentric human and robot data, achieves top-tier results across eight simulation benchmarks and real-robot tests spanning single-arm, bimanual, and dexterous embodiments. The full stack, including infrastructure, evaluation protocols, pretrained models, and data recipes, is released openly.

Hugging Face daily papers · 10d agoAI research

Muse can shop, write emails, and negotiate prices for users, all through WhatsApp

Meta launched Muse, a WhatsApp-controlled agent running on an isolated VM with a Sentinel gatekeeper, able to shop, email, book travel, and negotiate.

Meta introduced Muse, an autonomous agent controlled through WhatsApp that runs on its own cloud virtual machine, plans multi-step tasks, browses, fills forms, and negotiates on users' behalf. Payments run through Stripe's Link using one-time cards, which Meta calls the first AI agent covered by Link's purchase protection, with Shop Pay and 1Password integration planned. A second agent, Sentinel, gates all Muse network access and holds credentials, and a Muse Confidential VM with user-held encryption keys is planned later this year. Muse's model reportedly scored 44-48 on Artificial Analysis Intelligence Index v4.3, up from 31 for Muse Spark in April, near GPT-5.6 Sol's 47; it launches first in the US on iOS and Android.

The Decoder · 7d agoAI industry1

Autonomous Research for Open-Ended Problems: A Case Study on Telecom Ticket Retrieval

Case study shows autonomous LLM research reaches 90% of SOTA on telecom ticket retrieval in 10 weeks versus 10 months human work.

The paper explores adapting autonomous research to open-ended, industry-grade ML problems through a telecom ticket retrieval case study with commercial and open-source agents. Autonomous research reached 90% of state-of-the-art performance (0.34 vs. 0.38 Recall@1) in 10 weeks versus 10 months of human work, at up to $200 per Cursor campaign. The authors find agents excel at narrow hyperparameter optimization but lack human-like intuition, recommending human-agent collaboration.

arXiv cs.AI / cs.LG / cs.CL · 5d agoAI research