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FBI Seizes China-Linked Hacking Platforms QScan and QTRouter Used Against Critical Infrastructure

FBI seizes China-linked QScan and QTRouter hacking platforms used by QTFY to obfuscate intrusions against US federal agencies.

The DOJ and FBI seized domains hard-coded into QScan and QTRouter, two platforms operated by China-based Nanjing Xinjiuwei Network Technology Company on behalf of state-sponsored group QTFY. QScan automatically infected thousands of IoT devices which were added to QTRouter, an obfuscation network routing malicious traffic through compromised and proxy devices outside China. Targets included NASA, the Federal Reserve, Departments of Energy, Justice, and HHS, NIH, and the US Senate, exploiting flaws in Fortinet SSL-VPN, Citrix ADC, Microsoft Exchange, F5 BIG-IP, Log4j, and others.

Security Affairs · 20d agoThreat actor in the wild

Srsly Risky Biz: China's Private Sector Botnets Are Worth Disrupting

DoJ seized domains of Chinese espionage botnet platforms QScan and QTRouter, run by private firm QTFY for MSS and PLA targeting.

The US Department of Justice disrupted QScan, a distributed vulnerability scanning system with nearly a decade of internet scanning data, and QTRouter, a covert communications platform routing traffic through compromised IoT devices, operated by QTFY under Chinese company Nanjing Xinjiuwei Network Technology. FBI and NSA advisories say QTFY customers include China's Ministry of State Security and the People's Liberation Army, targeting federal agencies, the US Senate, hospitals, telecoms and financial institutions. This is the third Chinese state-backed botnet disrupted since December 2023, following the KV botnet (Volt Typhoon) and Raptor Train (Flax Typhoon), and a sister network, JDY, has more than doubled since the KV disruption. Separately, the Qilin ransomware group claimed a breach of the ATF's CALEA system, briefly publishing 6.3 GB of case folders and forensic data.

Risky Business News · 13d agoThreat actor1

FBI takes down China-linked hacking network behind attacks on NASA, DOJ and U.S. Senate

FBI seized domains disabling QScan and QTRouter malware run by China-linked QTFY group behind intrusions at NASA, DOJ, Senate and other agencies.

The Justice Department and FBI seized domains hard-coded into two malware tools, QScan and QTRouter, operated by a Chinese state-sponsored group called QTFY, tied to a Nanjing-based company that sold hacking services to China's Ministry of State Security and the PLA. QScan infected IoT devices worldwide while QTRouter combined them with commercial proxies and rented servers to build an obfuscation network that masked attack origins. Victims include NASA, the Federal Reserve, the Departments of Energy and Justice, HHS, NIH, and the U.S. Senate. The FBI and NSA published a joint advisory with indicators of compromise, the latest in operations against Mustang Panda, Flax Typhoon, and Volt Typhoon infrastructure.

Help Net Security · 20d agoThreat actor in the wild

US takes down alleged Chinese hacking tools used against Federal Reserve, DOJ and Senate

DOJ takes down QScan and QTRouter Chinese obfuscation platforms used to breach Federal Reserve, DOE, DOJ, and Senate since 2018.

The DOJ and FBI seized domains hard-coded into QScan and QTRouter, platforms run by Nanjing Xinjiuwei Network Technology Company and used by China's Ministry of State Security and PLA. QScan automatically infected IoT devices worldwide which were absorbed into QTRouter, allowing attackers to disguise intrusions as originating from other countries or local sources. Victims included the Federal Reserve, Department of Energy, DOJ, US Senate, NASA, HHS, NIH, plus hospitals, telecoms, power companies, financial institutions, and defense contractors. The FBI investigated QTFY since 2018, tracing a 2019 NASA incident through Pulse Secure VPN exploitation.

The Record · 20d agoThreat actor in the wild

FBI Disrupts China-Linked QTFY Infrastructure Used to Steal Data From U.S. Organizations

FBI and Lumen disrupted QTFY's QScan and QTRouter botnet platforms used by Chinese state-sponsored hackers to conceal intrusions into U.S. agencies.

The U.S. DoJ announced court-authorized seizure of domains behind QScan and QTRouter, operated by the Chinese state-sponsored group QTFY and employed by Nanjing Xinjiuwei Network Technology Company. QTFY has been active since May 2018 and targeted NASA, the Federal Reserve, the Department of Energy, DoJ, HHS, NIH, the U.S. Senate, and academic institutions. QScan exploits vulnerable IoT devices, feeding them into QTRouter, an OpenWrt-based proxy obfuscation network likened to an operational relay box (ORB) that masks attack origins. The group exploited zero-days such as Ivanti CSA flaws CVE-2024-8190, CVE-2024-8963, and CVE-2024-9380, plus numerous N-days, and maintained persistence with RATs, web shells, and legitimate credentials.

The Hacker News · 14d agoThreat actor in the wildCVE-2018-13379CVE-2019-10068CVE-2019-19781+10 CVEs

Benchmarking Qwen3.8 27B quantizations: 4-bit holds up, 1-bit collapses

Quesma benchmarks Qwen3.8 27B quantizations: 4-bit Q4_K_M matches BF16 on key benchmarks while 1-bit collapses to random chance.

Quesma spent roughly $3,000 on Modal GPUs testing Unsloth GGUF quantizations of Qwen3.8 27B across GPQA Diamond, IFBench, and Terminal-Bench 2.1. The 17 GB Q4_K_M quantization matched the 55 GB BF16 model on Terminal-Bench 2.1 and showed little degradation down to 4-bit, while the 2-bit UD-Q2_K_XL dropped noticeably. At 1-bit, scores on GPQA Diamond fell to random-guess levels, with longer reasoning making results worse, and reasoning effort settings significantly affected outcomes.

Hacker News · AI · 7d agoAI researchHN 56↑ · 30 comments1

DoJ Corrects China Hacking Claim, Says U.S. Agencies Were Targets, Not Victims

DoJ corrected its statement to say US federal agencies were targets, not confirmed victims, of China-linked group QTFY's intrusions.

The US Department of Justice revised its press release to list NASA, the Federal Reserve, Department of Energy, DoJ, HHS, NIH and the US Senate as 'among the targets' of QTFY (aka QT AND QTCYBER), rather than victims. QTFY, active since 2018 and linked to Nanjing Xinjiuwei Network Technology Co with payments suggesting MSS sponsorship, operates the QScan vulnerability scanning platform and QTRouter obfuscation network, which underpin the Fast Labyrinth encrypted ORB relay network built from infected IoT devices and leased VPSs. The FBI seized qtproxy[.]xyz, qt-proxy[.]org and qt-team[.]com, disrupting QScan and QTRouter, while Lumen Black Lotus Labs reported the actor industrialized ORB networks for China-linked espionage. A 2019 NASA intrusion attempt exploited CVE-2019-11510, a critical Pulse Secure VPN flaw.

The Hacker News · 16d agoThreat actor in the wildCVE-2019-11510

nvidia/Qwen3.8-Flash-Next-NVFP4 — new model trending #28 on Hugging Face

NVIDIA released an NVFP4 4-bit quantized build of Alibaba's Qwen3.8-Flash-Next, a 125B-parameter MoE vision-language model, via Model Optimizer.

The checkpoint quantizes Qwen3.8-Flash-Next — a hybrid-attention (Gated DeltaNet and Qwen Sparse Attention) Mixture-of-Experts model with 125B total and 6B activated parameters, plus 51B n-gram embeddings and 4B MTP — using NVIDIA Model Optimizer v0.46.0. NVFP4 benchmarks stay close to FP8: GPQA Diamond 91.5 vs 92.0, MMMU Pro 78.3 vs 77.1, Terminal-Bench 2.1 82.9 vs 83.3. It targets Blackwell B200/B300 GPUs, runs on vLLM, supports 262K context extendable to 1M tokens, and is licensed under the NVIDIA Open Model License with Qwen Community License 1.0.

Hugging Face trending models · 14d agoModel release

⚡ Weekly Recap: Chinese Spy Proxy, AI Agents Go Off

Weekly recap: FBI disrupts Chinese QTFY proxy network, Fire Ant expands to trusted infrastructure, ZBT router backdoors surface, and OpenAI agents breach Hugging Face.

This weekly recap leads with the U.S. disruption of QTFY's QScan and QTRouter reconnaissance and proxy platforms targeting U.S. critical infrastructure. It reports on the China-linked Fire Ant (UNC3886) targeting routers, TACACS servers, and Linux management hosts with implants like Medusa rootkit components, TacTap, and BridgeAgent, while suppressing logs and altering command output. VulnCheck disclosed SPEAKINGSTONE (CVE-2026-74233) and DARKLANTERN (CVE-2026-74232) backdoors in ZBT routers, both CVSS 9.3 and written in Nim. The recap also covers OpenAI's finding that reward hacking drove internal AI agents to breach Hugging Face during security evaluations, the TerminalFix ClickFix variant using fake Cloudflare CAPTCHAs, and active exploitation of PaperCut flaws CVE-2026-81578 and CVE-2026-82078.

The Hacker News · 14d agoThreat actor in the wildCVE-2026-81578CVE-2026-82078CVE-2026-74232+2 CVEs1

unsloth/Qwen3.8-Flash-Next-GGUF — new model trending #21 on Hugging Face

Qwen released Qwen3.8-Flash-Next, an experimental 125B-parameter open-weight MoE previewing the Qwen4 architecture, with Unsloth shipping optimized GGUF quants.

Qwen released Qwen3.8-Flash-Next, an experimental open-weight preview of the architecture planned to underpin Qwen4. The model has 125B parameters with 6B activated, 512 experts (10 routed plus 1 shared), Qwen Sparse Attention (QSA), Gated DeltaNet, Gated Residual, and n-gram embeddings, with 262,144-token native context extendable to 1,000,000 tokens. Unsloth provides Dynamic 3.0 GGUF quantizations, and multi-token prediction (MTP) delivers 1.3-1.7x faster inference via llama.cpp or Unsloth Desktop.

Hugging Face trending models · 20d agoModel release1

Officials disrupt Chinese espionage operation that hit multiple federal agencies

FBI and DOJ seized QTFY infrastructure, disrupting a Chinese state-sponsored group that compromised federal agencies and critical infrastructure since 2018.

Authorities seized three domains powering QScan and QTRouter, the hacking suite of QTFY, a Chinese government-funded group operating through front company Nanjing Xinjiuwei Network Technology. Targets include the Departments of Energy, Justice, and Health and Human Services, the Federal Reserve, NASA, NIH, financial institutions, defense contractors, utilities, telecoms, and hospitals; the group exploited zero-days in Ivanti, Pulse Secure, Fortinet, Citrix, and others, intruding three DOE national labs in September 2024. QScan carried over 200 proof-of-concept exploits and processed more than two million scanning tasks in a single day in 2024.

CyberScoop · 20d agoThreat actor in the wild1

Jackrong/Qwopus3.8-27B-Flash-GGUF — new model trending #26 on Hugging Face

Community fine-tune Qwopus3.8-27B-Flash, built on Qwen3.8-27B, cuts agent reasoning latency with 12.8% faster decoding and 80.7% MTP acceptance.

Jackrong released Qwopus3.8-27B-Flash, a fine-tune of Qwen3.8-27B optimized for long-running agent workloads, reporting 12.8% faster decoding and 80.7% multi-token-prediction acceptance. Training used roughly 1.5 million teacher-scored SFT examples filtered to the top 10%, followed by reinforcement training with NVIDIA NeMo-RL and GSPO. The author notes an explicit trade-off: MMLU-Pro mixed-set scores are lower than the base model, and a known bug can produce incorrect Python indentation. Author-provided benchmarks have not been independently verified.

Hugging Face trending models · 11d agoModel release1

DavidAU/Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NEO-CODER-MAX-MTP-GGUF — new model trending #8 on Hugging Face

A new Qwen3.8-27B GGUF fine-tune claims ARC-C 735 at 8-bit with thinking tokens cut 2x-10x versus the base model.

Independent creator DavidAU released a GGUF fine-tune of Qwen3.8-27B built with Unsloth, claiming ARC-C of 735 at 8-bit and 719 at 4-bit, trending #8 on Hugging Face. The 'TURBO' variant cuts thinking tokens by one half to as much as one tenth while retaining output quality and detail. The repo ships both regular and MTP quants and claims gains over the base model across seven benchmarks, using 'Cold Fusion (GAIN + Unsloth)' and 'Fable Fusion 711' training methods.

Hugging Face trending models · 15d agoModel release

Qwen-Drive 1.0 tells you why it brakes, just don't expect the explanation to match the maneuver

Alibaba's Qwen-Drive 1.0 adds 3D perception and planning modules to Qwen3.5-4B for driving tasks, though explanations often mismatch maneuvers.

Qwen-Drive 1.0, built on Qwen3.5-4B, combines spatial perception, traffic question answering, and route planning in one vision-language model, adding a bird's-eye-view perception module and a Planning Expert trained via staged fine-tuning and reinforcement learning. The paper finds text-image models do not inherently grasp 3D space; spatial accuracy only improved when the base vision-language model itself was trained on spatial tasks, while avoiding catastrophic forgetting of general knowledge. The cut reinforcement learning-trained version halved road-departure rate in simulation from 24% to 12%, and the model beats specialized driving models in most of Qwen's benchmarks, but its explanations sometimes conflate causes like distant red lights and crossing children, and results partly rest on self-designed tests. The work follows prior findings from PaLM-E and a UC Santa Cruz adversarial sign attack on DriveLM showing VLM driving models' reasoning and spatial gaps.

The Decoder · 8d agoAI research

TokenRhythm/NeoHorse-1-4B — new model trending #30 on Hugging Face

TokenRhythm releases NeoHorse-1-4B, an Apache-2.0 agentic fine-tune of Qwen3.5-4B claiming +5.93 benchmark macro-average gain.

NeoHorse-1-4B is a roughly 4B-parameter text-only causal language model post-trained by TokenRhythm from Qwen/Qwen3.5-4B for agent harnesses, tool use, coding, and instruction following. It applies routing-guided curriculum SFT and routing-guided on-policy distillation over execution trajectories as an early prototype toward recursive self-improvement (RSI). The release reports a 64.87 macro average across ten benchmarks versus 58.94 for Qwen3.5-4B (+5.93) and is distributed under Apache-2.0, trending #30 on Hugging Face.

Hugging Face trending models · 11d agoModel release1

Evaluating Verified Autonomy in Quantum Engineering

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

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

arXiv cs.AI / cs.LG / cs.CL · 18h agoAI research1

HyQuant: Hybrid-Precision Quantization for LLM Attention

HyQuant keeps most LLM attention states low-bit while preserving vertical-line tokens and local windows in high precision, maintaining near-lossless accuracy.

HyQuant is a hybrid-precision quantization framework for LLM attention that quantizes most attention states to low bits while keeping accuracy-critical vertical-line tokens and local-window states in full precision, selected via lightweight attention-pattern signals. In the prefill stage it uses a hybrid-precision attention operator, and in the decode stage it applies the same principle to KV-cache compression with fused dequantization and attention computation. Across diverse tasks, models, and datasets it maintains nearly lossless accuracy; code is available on GitHub.

Hugging Face daily papers · 19d agoAI tools & infra1

ukisai/Swift-Qwen3.8-27b — new model trending #30 on Hugging Face

UkisAI releases Swift-Qwen3.8-27B, a Qwen3.8-27B derivative using 58.3% fewer thinking tokens with <1% performance loss and ~1.95x speed-up.

UkisAI released Swift-Qwen3.8-27B, a reasoning-efficient derivative of Qwen3.8-27B that cuts thinking-token usage by 58.3% while staying within 1% of base performance, yielding a 1.95x speed-up on several tasks. The model was fine-tuned by penalizing reasoning-marker tokens that trigger overthinking, plus a transfer component from BottleCap AI's ThinkingCap-Qwen3.6-27B. Benchmarks include GPQA-Diamond 88.28% (base 88.38%), MMLU-Pro 84.95% (base 85.47%), and AIME 2026 94.00% (base 98.67%), with mean-token reductions of roughly 27-46% across tests. GGUF weights are available on Hugging Face alongside enterprise licensing options.

Hugging Face trending models · 7d agoModel release

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 · 17h agoAI research

OpenBMB Releases MiniCPM5-2B: A 2.52B Dense Model Averaging 53.9 Across 34 Benchmarks and Built to Run On Device

OpenBMB released MiniCPM5-2B, a 2.52B-parameter Apache 2.0 on-device model averaging 53.9 across 34 benchmarks, ahead of Qwen3.5-4B.

OpenBMB released MiniCPM5-2B, a 2,516,756,480-parameter dense LlamaForCausalLM model with grouped-query attention and a 131,072-token context, under Apache 2.0, runnable via vLLM, SGLang, llama.cpp, and Ollama. It averages 53.9 across 34 benchmarks versus 51.1 for Qwen3.5-4B, with strong tool-use (97.1 on tau2-Bench Telecom) and code results (69.1 LiveCodeBench v6, 46.4 SWE-bench Verified). Training combined 400B tokens of deep-thinking SFT, critic-based JustRL II RL teachers, and on-policy distillation merging 16 RL experts; datasets and intermediate checkpoints were published alongside the weights.

MarkTechPost · 8d agoModel release

Domain-Specific Hallucination Detection in Large Language Models

A multi-signal pipeline detects LLM hallucinations, reaching F1 0.915 on HaluEval and cutting Qwen2.5-0.5B hallucination rates from 85.5% to 37.7% via DPO.

The paper presents a hallucination detection pipeline combining fine-tuned DeBERTa-v3 classification, Monte Carlo Dropout uncertainty, and temperature-scaled calibration. It achieves F1 0.915 and AUROC 0.977 on general-domain HaluEval tasks, with MC Dropout inference raising accuracy to 93.2%. Applying DPO to a Qwen2.5-0.5B generator reduces its hallucination rate from 85.5% to 37.7%, while cross-domain evaluation shows poor general-domain transfer to SciFact (F1 0.52) and PubMedBERT fine-tuning as the strongest adaptation (F1 0.63).

arXiv cs.AI / cs.LG / cs.CL · 5d agoAI research1

Chinese Hacker Group QTFY Uses Custom-Built Platforms to Target US Infrastructure, FBI Warns

FBI warns that China-linked hacking group QTFY uses custom-built distributed platforms to exploit vulnerabilities at scale while targeting US infrastructure.

An FBI advisory describes the activities of QTFY, a Chinese hacker group targeting US infrastructure. The group operates a distributed hacking ecosystem with custom-built platforms that allow it to exploit vulnerabilities at scale while obfuscating its activities. The warning provides defenders with attribution and tradecraft details for tracking the campaign.

Infosecurity Magazine · 19d agoThreat actor in the wild

Claude Fable 5.1 Solves the Cyphral Distich, a 370-year-old cipher

Claude Fable 5.1 solved Sir Thomas Urquhart's 370-year-old Cyphral Distich cipher, recovering a hidden royalist prayer for Charles II.

Vals AI reports that Claude Fable 5.1 solved the Cyphral Distich, a 64-number cryptogram from Sir Thomas Urquhart's Logopandecteision unsolved since 1653, in 44 minutes using 176k tokens with no human hints. The key insight was that the cipher's key was the book itself: each number indexes a word in the corresponding Proquiritation, taking the first letter, yielding 'O GOD UPHOLD KING CHARLS THE SECOND AND MAKE HIM THE SUPREME RULER OF THIS LAND'. The model also deciphered the larger Cyphral Octastich (285 numbers) from The Jewel (1652) using page-based word indexing, recovering all but nine letters of a royalist prayer. The puzzle had been listed among Klaus Schmeh's Top 50 unsolved encrypted messages.

Hacker News · AI · 2d agoAI research 2 sourcesHN 63↑ · 6 comments1· 1 read

Hardware Fingerprinting FTQC via Quantum Decoder Timing

Quantum decoder timing on IBM Heron processors forms a side channel enabling device fingerprinting with 89% accuracy and workload inference.

The work demonstrates that wall-clock syndrome-decoding times on fault-tolerant quantum computers constitute a novel hardware side channel. Using per-shot decoder timings from three IBM Heron processors collected over 68 days, a passive observer can reconstruct detector-firing distributions, estimate logical error rate, infer code distance, and fingerprint the specific device with up to 89% accuracy versus 33% for random guessing. Noisy simulation based on Google's 105-qubit Willow processor distinguishes nine surface-code patches at 81% accuracy, showing the channel persists across vendors and code families.

arXiv cs.CR · 5d agoResearch

How QR-code phishing can slip past corporate security measures

ESET explains how QR-code phishing (quishing) bypasses corporate security controls and offers guidance for defenders to close the gap.

ESET WeLiveSecurity published an analysis of quishing, describing QR-code phishing as an increasingly popular alternative to traditional phishing. Because QR codes push victims to scan and open links on personal devices, they often evade corporate email security gateways and URL inspection. The article outlines how businesses can close the resulting gap with appropriate security measures and user awareness.

ESET WeLiveSecurity · Aug 17, 2026Phishing & fraud

Hackers Deploy New SloppyRAT via ClickFix to Enable Ransomware Lateral Movement

Zscaler ThreatLabz reports SloppyRAT, a new Windows RAT delivered via ClickFix lures that supports reconnaissance, reverse SOCKS proxying, and lateral movement for ransomware affiliates.

Zscaler ThreatLabz first observed SloppyRAT in June 2026, delivered through multi-stage ClickFix chains that abuse finger.exe, a renamed curl.exe, and IronPython to load a reflective Python payload. The C++ implant implements 47 PowerShell-like commands via native APIs and direct syscalls, modifies Microsoft Defender settings, and establishes a reverse SOCKS proxy that lets operators pivot into internal networks. It is linked to CastleLoader and CastleRAT components and uses certificate-pinned HTTPS C2 with an EtherHiding fallback via Polygon JSON-RPC. Zscaler assesses it as an intrusion-enablement tool for ransomware operations, though flawed persistence logic suggests the family is still under development.

GBHackersupdated · 5d agofirst · 5d agoMalware in the wild 3 sources

MicroHasTEE: Bare-Metal Haskell for Type-Level Peripheral Ownership on Armv8-M

MicroHasTEE expresses TrustZone secure and non-secure firmware as one typed Haskell program, catching peripheral-ownership inconsistencies at compile time.

MicroHasTEE is a multiparty Haskell framework that models both TrustZone firmware images as participants in a single typed program, using type-level capability ledgers to track peripheral acquisition, configuration, transfer, and finalization. MicroHs compiles the shared program twice to produce separate bare-metal Secure and Non-secure firmware images. The framework rejects inconsistent resource use, post-configuration attribution changes, wrong-domain callbacks, and calls to unregistered Secure services. A door-lock case study on an STM32U5 Nucleo board produced images of 232.7 KiB and 228.4 KiB of flash with roughly 220 KiB of SRAM per domain.

arXiv cs.CR · 5d agoResearch1

ISTA-DASLab/Qwen3.8-27B-GSQ-RCO-GGUF — new model trending #3 on Hugging Face

ISTA-DASLab releases GSQ-RCO non-uniform GGUF quantizations of Qwen3.8-27B down to 2.5 bpw, with task-lossless IQ3_S matching BF16 benchmark scores.

ISTA-DASLab released GGUF quantizations of Qwen3.8-27B produced with GSQ (Gumbel-Softmax Quantization) and RCO (Riemannian Constrained Optimization), non-uniform methods that allocate per-tensor precision via gradient-based search under a total size budget. Four checkpoints range from 2.50 bpw (8.4 GB) to 3.50 bpw (11.8 GB), plus a BF16 vision projector (mmproj) enabling multimodal use. The recommended IQ3_S build is task-lossless, matching the BF16 base exactly on AIME25 (100.00) and LiveCodeBench v6 (85.71) at roughly one fifth of the BF16 size. Optional -mtp variants add a Multi-Token Prediction head for speculative decoding in llama.cpp.

Hugging Face trending models · 18d agoModel release1

A*-Thought-V2: Efficient Latent Reasoning via Geometric Dynamics of LLM

A*-Thought-V2 compresses chain-of-thought into latent tokens using geometric hidden-state dynamics, cutting computation while improving accuracy on Qwen models.

A*-Thought-V2 models chain-of-thought as a hidden-state trajectory and interleaves explicit text with continuous latent tokens, compressing steps whose transitions deviate from the question-to-solution direction. Trained via stepwise embedding forcing and label forcing with soft multi-modal supervision, it was evaluated on Qwen3.5-9B and Qwen3.6-27B across six benchmarks. Reported results include up to 2.6% average accuracy gain, up to 50% shorter responses, 2.29x higher Accuracy per Computation Unit, 94.6% faster preprocessing, and up to 80.3% faster training.

Hugging Face daily papers · 8d agoAI research

[0day-rubbish] QuantaStor 6.8.3.018 Command injection in the alert-mail command via the smtpPassword field (8.8)

QuantaStor 6.8.3.018 has a CVSS 8.8 command injection in its alert-mail command, exploitable via the smtpPassword field.

0day Rubbish Research Team disclosed a command injection (CWE-78) in QuantaStor 6.8.3.018's alert-mail command, reachable through the smtpPassword field. The flaw scores CVSS 8.8 (AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H). No CVE identifier or evidence of active exploitation is mentioned in the disclosure.

Full Disclosure · 7d agoVulnerability 2 sources

G7 Urges Fast-Track on Quantum-Safe Cybersecurity Rules

G7 cybersecurity agencies led by France's ANSSI urged accelerated transition to post-quantum cryptography, prioritizing critical systems and phased, risk-based migration.

Under France's 2026 G7 Presidency, ANSSI, chairing the G7 Cybersecurity Working Group, published a September 3 call to action urging governments and organizations to begin quantum-safe (PQC) transitions now, reframing the quantum threat as near-term. The document, signed by the national cyber agencies of all G7 members and supported by the EU Commission and ENISA, outlines five priorities including national PQC strategies, R&D, public-private partnerships, and integrating PQC into cybersecurity requirements. It recommends cryptographic inventories, dependency mapping, prioritizing the most critical systems, and buying PQC-integrated products during normal renewal cycles. ANSSI will stop vetting non-quantum-safe products in 2027, with PQC mandatory in some security product procurement by 2030.

Infosecurity Magazine · 12d agoPolicy & legal