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Hackers drain $320M in Bitcoin from Liquid Network, claim they’re the good guys

Hackers withdrew roughly 4,000 BTC (about $320M) from the Liquid Network federation wallet backing the Blockstream-developed Bitcoin sidechain, claiming benign intent.

Blockstream said Sunday that around 4,000 BTC, roughly $320 million, was withdrawn from the federation wallet backing Liquid Network, a Bitcoin sidechain used by exchanges and financial institutions. The attackers publicly claimed they were 'the good guys'; no attribution or recovery details are provided. The theft affects infrastructure that backs custodial Bitcoin transfers for exchanges.

DataBreaches.netSep 7, 16:37 UTC · 8d agoData breach in the wild

Hackers Drain $320 Million From Liquid Network, Then Return Most of It

Hackers exploited an Elements bug to drain about 4,000 BTC (~$320M) from Liquid Network's federation wallet, then returned 3,400 BTC after Blockstream patched.

An attacker drained roughly 4,000 of 4,200 BTC (~$320M) from Bitcoin's Liquid Network federation wallet on September 6 by exploiting a bug in Elements, the open-source code powering the sidechain, which allowed unbacked L-BTC tokens that were redeemed for real Bitcoin through SideSwap's authorized peg-out mechanism. The self-described white-hat attackers negotiated publicly via OP_RETURN on-chain messages, demanding the bridge nodes be patched before returning 3,400 BTC (~$262.6M) and keeping about 598 BTC (~$47M). Blockstream confirmed the affected bridge nodes were patched; the network remains paused while federation members complete security work, and experts debate whether the act legally constitutes extortion.

Security AffairsSep 8, 20:35 UTC · 7d agoExploit / PoC in the wild

Hackers Return $263 Million Stolen From Liquid Network

Hackers drain roughly 4,000 BTC (~$320M) from Liquid Network federation wallet, then return 3,400 BTC with ~598 BTC still outstanding.

Attackers withdrew about 4,000 Bitcoin (roughly $320 million) from the Liquid Network federation wallet, which held approximately 4,200 BTC, via the SideSwap Peg-out Authorization Key without that key being compromised. Blockstream disabled nodes and suspended transactions after disclosing the heist on Sunday. Alleged white-hat hackers returned 3,400 BTC (~$262.6M) on Monday, demanding the underlying bug be patched before releasing the remaining ~598 BTC (~$47M). The network remains paused while fixes and a safe restart are prepared.

SecurityWeekSep 8, 16:35 UTC · 7d agoData breach in the wild

Hackers drain $320M in Bitcoin from Liquid Network, claim they're the good guys

Attackers withdrew about 4,000 BTC (~$320M) from Liquid Network's federation wallet via a SideSwap peg-out, claiming whitehat status.

Hackers drained roughly $320 million in Bitcoin — about 4,000 BTC, 95% of holdings — from the federation wallet backing Blockstream's Liquid Network sidechain. The withdrawal occurred through SideSwap using its Peg-out Authorization Key (PAK), yet no PAK appears to have been compromised, and the exact mechanism remains under investigation. The actors embedded an on-chain message identifying themselves as whitehats, asked Blockstream to patch federation nodes first, and promised to return most of the funds after fixes are confirmed. Liquid disabled its bridge nodes and asked exchanges to suspend L-BTC deposits and withdrawals; other Liquid assets and the Bitcoin network were unaffected.

The Register · SecuritySep 7, 13:26 UTC · 8d agoExploit / PoC in the wild

‘White hat’ hackers take $47 million bounty after $320 million crypto theft

Hackers withdrew $320 million in bitcoin from Liquid Network, negotiated on-chain, returned $266.5 million and kept a $47 million reward.

Purported white-hat hackers withdrew 4,000 BTC (about $320 million) from Liquid Network's own wallet, one of the largest cryptocurrency thefts of 2026. Over roughly 12 hours of public on-chain negotiation with operator Blockstream, the hackers returned $266.5 million in bitcoin and kept 598.5 BTC (about $47 million), claiming it as a reward for uncovering a bug. Blockstream deployed updated software and paused deposits and withdrawals while experts traced the flaw to the Elements sidechain framework. April thefts of $290 million from Kelp and $280 million from Drift, attributed to North Korean hackers, were previously 2026's largest.

The RecordSep 8, 17:00 UTC · 7d agoData breach in the wild

Liquid Hackers Return 3,400 Bitcoin Taken via Elements Bug, Still Holding $47M in BTC

Hackers exploited an Elements bug to take ~4,000 BTC from Liquid Network, returned 3,400 BTC (~$265M), and still hold ~598.5 BTC (~$47M).

Attackers exploited a bug in Elements, the software behind Blockstream's Liquid Network Bitcoin sidechain, to create L-BTC and withdraw roughly 4,000 BTC (about $320 million) via SideSwap's Peg-out Authorization Key, roughly 95% of Liquid's reported reserves. They returned 3,400 BTC (about $265 million at ~$78,000 per BTC) to the federation address on September 7, keeping about 598.5 BTC (roughly $47 million). The group called itself white hats, demanded node patches before returning funds, and negotiated via on-chain and PGP-encrypted messages; Ledger CTO Charles Guillemet characterized the arrangement as extortion. Blockstream says the peg-out key and other keys were not compromised, updated software is deployed, and the network remains paused pending a coordinated restart.

The Hacker NewsSep 8, 14:54 UTC · 7d agoExploit / PoC in the wild

A hacker stole $340M in a crypto heist, then returned most of it

A hacker exploited a bug to steal about 4,000 BTC (~$340M) from Blockstream's Liquid Network, then returned roughly 3,400 BTC after the bug was fixed.

A hacker exploited a bug to withdraw roughly 4,000 bitcoins worth about $340 million from Liquid Network, a settlement service launched in 2018 by crypto firm Blockstream and used by several cryptocurrency exchanges. Liquid Network paused operations, and the hacker, described as a white hat, offered to return the funds once the bug was fixed. Former Blockstream executive Samson Mow said the bug was fixed and about 3,400 BTC (~$293M) returned, leaving roughly 600 BTC (~$47M) under the hacker's control pending further security improvements. Rekt's leaderboard ranks the heist among the largest cryptocurrency thefts to date.

TechCrunch · SecuritySep 8, 14:11 UTC · 7d agoExploit / PoC in the wild

HybridFLow: SDN-Orchestrated Client Partitioning for Hybrid Federated Learning

HybridFLow uses SDN topology visibility to partition federated-learning clients into sync/async groups, reaching 80% accuracy 33-40% faster than SmartFLow.

HybridFLow is a closed-loop, SDN-driven orchestration framework for hybrid federated learning that integrates network-layer intelligence into cross-silo training. It leverages the SDN controller's global topology view to generate calibrated per-client communication-time estimates, partitioning clients into synchronous and asynchronous groups while balancing round latency and update staleness, with measured times fed back after each round. Across multiple network topologies it reaches 80% target accuracy 33-40% faster than SmartFLow and cuts average round duration by 30-40 seconds, while FedAsync fails to reach target accuracy under non-IID data.

arXiv cs.AI / cs.LG / cs.CLSep 9, 16:23 UTC · 6d agoAI research

Fly Language Model (FLM) Wires the Full Fruit Fly Connectome Into a Frozen 1.2B LLM, and Its Own Controls Show the Wiring Does Not Help

Researchers wire the full fruit fly connectome (166,700 nodes) into a frozen LiquidAI LFM2.5-1.2B LLM, but controls show no fly-specific benefit.

The Fly Language Model (FLM) couples the complete MaleCNS v1.0 fruit fly connectome (166,700 nodes, 25,582,938 edges) to a frozen LiquidAI LFM2.5-1.2B-Instruct backbone, training only a 278,528-parameter readout (~0.0238% of backbone parameters). The fly readout improved NLL by 0.0222 nats/token (perplexity 3.98 to 3.90) on 32 SmolTalk dialogues, but a direct-input control without the graph beat it in all three seeds. Relabeling node identities removes the gain and the recurrence contracts state differences by 0.6 per token, so the connectome adds no long-range memory. The MIT-licensed code runs locally on Python 3.12, but study artifacts remain private, limiting independent reproducibility.

MarkTechPostSep 12, 18:51 UTC · 3d agoAI research1

LimiX-2: A Contextual Mechanism Network Towards General Structured-Data Intelligence

LimiX-2 scales Contextual Mechanism Networks pretrained via context-conditional masked modeling, beating tabular foundation models on TabArena, TALENT, and BCCO benchmarks.

LimiX-2 is a new tabular model in the LimiX family, developed through model and data scaling guided by previously established scaling laws under the Contextual Mechanism Networks (CMNs) paradigm. It is pretrained with Context-Conditional Masked Modeling (CCMM) on synthetic datasets generated by structural causal models spanning diverse graph structures, functional mechanisms, and observation processes. It outperforms dataset-specific models and tabular foundation models on TabArena, TALENT, and BCCO, and its feature attention encodes direct causal relationships, enabling accurate causal skeleton recovery.

arXiv cs.AI / cs.LG / cs.CLSep 15, 17:30 UTC · 16h agoAI research1

Meme Coin Factories: Uncovering Large-Scale Manipulations on pump.fun

Large-scale pump.fun study of 15 million meme coins identifies five manipulation classes including wash trading and a Market-Manipulation-as-a-Service ecosystem.

Researchers analyzed all 15 million coins launched on pump.fun over the last two years plus large random samples of transaction data, identifying five manipulation classes: wash trading, creator address obfuscation, coordinated sells, copycat coins, and social media manipulation. Strategic actors bypass the platform interface and implement strategies in a highly automated, low-latency way by interacting directly with the blockchain. The study also uncovers Market-Manipulation-as-a-Service (MMaaS) third-party tools that let non-technical users run these manipulations, and proposes mitigations for traders, pump.fun, and regulators.

arXiv cs.CRSep 9, 14:35 UTC · 6d agoResearch

Embedded Graph Flows for Categorical Graph Generation

Researchers propose Embedded Graph Flows, a generative model with learned categorical embeddings that beats DiGress and GruM on molecular graph benchmarks.

Embedded Graph Flows (EGF) learns continuous embeddings for node and unordered-edge categories and transports Gaussian noise toward these endpoints using a permutation-equivariant graph transformer. On QM9 it achieves the best result on all four reported metrics, with a Fréchet ChemNet Distance of 0.150 versus 0.717 for DiGress and 0.812 for GruM. On ZINC250k it retains the lowest NSPDK MMD, indicating close agreement with local substructures of reference molecules. Code is released on GitHub.

arXiv cs.AI / cs.LG / cs.CLSep 4, 16:26 UTC · 11d agoAI research1

Model-Aware Schedules Improve Generation via Fiberwise Optimal Transport

Researchers introduce model-aware diffusion schedules via fiberwise optimal transport, cutting flow-matching FID on CIFAR-10 by 38.6% at 16 function evaluations.

The paper proposes constructing diffusion and flow-matching sampling schedules from a fiberwise prediction risk defined via optimal transport, combined with coefficient-path kinetic action, yielding a closed-form time allocation. Across DDPM and flow-matching experiments spanning targets, datasets, and architectures, the schedules beat model-agnostic baselines, including a 38.6% relative FID reduction for flow matching on CIFAR-10 at 16 function evaluations. Normalized fiberwise-risk profiles from independently trained models align closely, suggesting empirical universality, and a frozen analytic allocation template retains most of the gains.

arXiv cs.AI / cs.LG / cs.CLSep 10, 17:30 UTC · 5d agoAI research

Show HN: LLM Attention Visualization

A developer released a browser-based tool that visualizes which past tokens influence each LLM output token using aggregated, value-weighted attention scores.

A Show HN project presents a React application built on Transformers.js that renders per-token attention influence by aggregating attention weights scaled by value-vector magnitudes across all attention heads and layers. To expose internal tensors, the author instrumented the ONNX computation graph, hosted a modified model on Hugging Face, and pre-generated prompts to avoid long model downloads in the browser. Demos with a 600-million-parameter model show how verbatim copying draws heavily on source tokens and how single outputs blend information from multiple phrases.

Hacker News · AISep 8, 16:59 UTC · 7d agoAI tools & infra HN 41↑ · 9 comments

Decentralized network congestion control for DAG-based distributed ledger system

Researchers propose node-specific variable proof-of-work to curb transaction spamming in DAG-based distributed ledgers, proving a Nash equilibrium enforces prescribed node behavior.

The paper proposes a variable, behavior-based node-specific proof-of-work model for DAG-based distributed ledger networks, where congestion is mainly driven by transaction spamming rather than user growth or token launches. The model grants equal opportunity to stakeholders regardless of computational resources and penalizes nodes issuing more than a prescribed number of transactions. System behavior is modeled as a non-cooperative game over finite network resources, and the authors prove existence of a Nash equilibrium enforcing the prescribed behavior.

arXiv cs.CRSep 9, 09:48 UTC · 7d agoResearch

Hackers have withdrawn ~4k BTC (~$320M) from the Liquid Federation wallet

Hackers reportedly withdrew about 4,000 BTC (~$320 million) from the Liquid Federation wallet, according to a widely shared report on X.

A post on X reports that hackers withdrew roughly 4,000 BTC — valued near $320 million — from the Liquid Federation wallet. The source text provides no details on the compromise method, attribution, custody impact, or any organizational response. The story drew heavy attention on Hacker News (125 points, 86 comments), indicating broad community concern.

Hacker News · securitySep 6, 22:43 UTC · 9d agoData breach in the wildHN 125↑ · 86 comments

Inside NVIDIA’s cuDNN Graph API: Fusion, Autotuning, and Plan Reuse with cuDNN Frontend

MarkTechPost tutorial walks through NVIDIA's cuDNN Frontend graph API, covering kernel fusion, autotuning, plan reuse, and CUDA graph capture on Colab GPUs.

The tutorial explains how to express GPU computations as operation graphs via the cuDNN Frontend graph API, running the five-step build pipeline of validate, build operation graph, create execution plans, check support, and build plans. It progresses from a single fused convolution with bias and ReLU to autotuning across engine configs, FP8-style epilogues, attention, plan serialization, dynamic shapes, and CUDA graph capture. Each kernel is benchmarked against a PyTorch reference on a single Colab GPU to verify correctness and measure cost. The piece also covers practical setup issues like making libcudnn.so visible to the frontend's dynamic loader.

MarkTechPostSep 15, 21:37 UTC · 12h agoAI tools & infra

Operation ASTERIX: Anatomy of a Crypto Fraud Pipeline

Rapid7 exposed infrastructure behind a cryptocurrency fraud pipeline using phishing panels, voice-dialing scripts, fake wallets, and AI coding assistants.

Rapid7 researchers identified an exposed web directory on infrastructure used to support a cryptocurrency fraud operation tracked as Operation ASTERIX. The server contained raw phone-number datasets, account-validation tools, enriched lead records, phishing panels, voice-dialing scripts, fake wallet applications, persistence mechanisms, and Telegram exfiltration code. Recovered prompts, shell history, and project files show the operator relied on AI coding assistants to package Electron applications, obfuscate code, troubleshoot builds, and modify phishing infrastructure.

Rapid7 BlogAug 17, 11:29 UTC · 29d agoPhishing & fraud

Distill Globally, Adapt Locally: Reasoning Distillation and Product-Type Test-Time Training for Scalable Trade-Up Recommendation

A distillation framework compresses LLM reasoning into a 15.5M-parameter trade-up recommendation model reaching AUC 0.941 with product-type test-time training.

The paper targets trade-up recommendation, which identifies higher-quality alternatives that preserve customer purchase intent. A retrieval-augmented few-shot LLM teacher generates labels and rationales that supervise a compact embedding-pair classifier; at inference the 15.5M-parameter student uses only two precomputed 768-dimensional embeddings with no LLM calls. On 8,352 annotated pairs, label-only training scored AUC 0.912, reasoning distillation reached 0.924, and product-type test-time training lifted it to 0.941 with average precision 0.940. The distilled student is roughly 5,000x faster and 10,000x cheaper than direct LLM inference on a 100K-pair proxy catalog.

arXiv cs.AI / cs.LG / cs.CLSep 4, 17:08 UTC · 11d agoAI research

MIT creates method to force AI to comply with safety rules

MIT researchers published HardFlow, a method enforcing hard safety constraints on flow-matching generative models' final outputs without retraining.

MIT researchers led by Zeyang Li and Navid Azizan developed HardFlow, a trajectory-optimization method that enforces strict, non-negotiable constraints on flow-matching generative models by checking rule satisfaction only at the final generation step. Published in IEEE TPAMI, it outperformed six rival projection and guidance methods on four simulated benchmarks including D3IL robotic manipulation, Maze2D, physical process control, and image editing. All results are simulation-only, with no independent reproduction yet reported.

Hacker News · securitySep 14, 16:18 UTC · 1d agoAI research HN 24↑ · 25 comments

Up to 3.2x Faster Inference with LFM2.5-DSpark

LiquidAI's LFM2.5-DSpark delivers up to 3.2x faster inference, announced via the Hugging Face blog.

LiquidAI announced LFM2.5-DSpark on the Hugging Face blog, claiming up to 3.2x faster inference. The release focuses on improved runtime performance for the LFM2.5 model family; further technical details were not available in the provided text.

Hugging Face BlogAug 20, 16:52 UTC · 26d agoAI tools & infra

Making Knowledge Distillation Cheap Enough to Run at Scale

Hugging Face blog by Multiverse Computing describes techniques making knowledge distillation cheap enough for large-scale training.

A Hugging Face blog post from Multiverse Computing (CAI) presents methods for reducing the cost of knowledge distillation so it can be run at scale. The post is aimed at practitioners compressing large models into smaller, cheaper ones for production use.

Hugging Face BlogAug 10, 10:05 UTC · Aug 10, 2026AI research

New infosec products of the week: August 21, 2026

Weekly product roundup covering NETSCOUT outbound DDoS mitigation, F5 AI Gateway enhancements, Intezer Workflows, and Tufin TOS 5.3.

NETSCOUT extended Adaptive DDoS Protection to automatically mitigate outbound attack traffic for service providers. F5 enhanced its AI Gateway and integrated it into the F5 AI Security Platform for unified AI access governance. Intezer launched Workflows, native automation and response inside its platform without a separate SOAR, and Tufin released Orchestration Suite 5.3 with AI-powered Segmentation Intelligence for multi-vendor environments.

Help Net SecurityAug 21, 00:00 UTC · 26d agoTools

Scalability Analysis of Distributed Kolmogorov-Arnold Network Training on High-Performance Computing Systems

An empirical study shows distributed Kolmogorov-Arnold Network training reaches 74.7% parallel efficiency at 8 A100 GPUs, with overheads driven by All-Reduce choices.

The study evaluates data-parallel Kolmogorov-Arnold Network (KAN) training on the FinisTerrae III supercomputer using up to 8 NVIDIA A100 GPUs across 4 nodes with PyTorch Distributed Data Parallel. Strong scaling yields 5.97x speedup and 74.7% parallel efficiency at 8 GPUs, comparable to conventional deep learning workloads, while communication overhead ranges from 1.3% to 6.1%, driven mainly by All-Reduce algorithm selection and inter-node latency rather than KAN's edge-wise gradient structure. Weak scaling shows an initial single-to-multi-GPU throughput drop followed by stability, and the parameter-to-memory ratio improves with model size even as training time scales unfavorably. The authors provide GPU topology and model-size deployment guidelines for KAN training.

arXiv cs.AI / cs.LG / cs.CLSep 7, 16:45 UTC · 8d agoAI research1

Anthropic Says Seven China-Based AI Labs Ran Industrial-Scale Claude Distillation Attacks

Anthropic disrupted industrial-scale unauthorized Claude distillation by seven China-based AI labs, including Alibaba, DeepSeek, Moonshot, and Z.ai.

Anthropic identified and disrupted six illicit distillation campaigns since February 2026 run by seven China-based labs: Alibaba, Moonshot, DeepSeek, Z.ai (Zhipu), MiniMax, Xiaomi, and SenseTime. The largest, GTG-16005, involved 151 million exchanges targeting Claude Opus 4.6/4.7 chain-of-thought transcripts, peaking at roughly 3 million exchanges per day from more than 3,500 fraudulent accounts. Labs used proxy/relay services with fictitious identities, fake or stolen credit cards, harvested API keys, and purchased conversation transcripts from third-party resellers. Anthropic is countering by banning reseller accounts, summarizing internal reasoning before responding, and introducing preserved thinking in Fable 5.1, which encrypts reasoning and prevents context edits before it.

The Hacker NewsSep 11, 16:15 UTC · 4d agoAI safety & security 10 sources

Recurrent GraphNeural NetworkswithSet-BasedAggregation

Paper proves two-directional equivalence between recurrent GNNs with set-based aggregation and Boolean closure of reachability/safety properties in modal mu-calculus, checkable from weights.

The authors study recurrent graph neural networks with set-based aggregation and identify sufficient conditions, checkable directly from network weights, for compiling networks into logical formulas and formulas into networks. They establish an effective two-directional equivalence with the Boolean closure of reachability and safety properties, the fragment BΣ°1 of the modal μ-calculus, shown to be the exact expressive level of stabilization over finite vocabulary. The correspondence needs no counting logic, external halting signal, or non-effective acceptance condition, yielding a verifiable path from weights to symbolic explanations.

arXiv cs.AI / cs.LG / cs.CLSep 14, 17:39 UTC · 1d agoAI research

How Does mHC Use Its Residual Streams? Selective Routing and Near-Identity Mixing

Analysis of DeepSeek-V4-Flash shows four-stream mHC residual blocks use only about two streams effectively, with late-layer mixing providing little benefit.

The study examines the four-stream residual pathway of DeepSeek-V4-Flash, finding typical attention or FFN sites effectively use about two streams and that residual mixing is modest, occurring primarily in early layers. Replacing late mixers with identity increases C4 perplexity by only 1.9% while replacing early mixers raises it by 41%. Retaining the three largest routing weights per token increases perplexity by at most 2.7%, showing the model uses only part of the flexibility afforded by the four-stream design.

arXiv cs.AI / cs.LG / cs.CLSep 4, 16:02 UTC · 11d agoAI research

🔬“We have foundation models for language, not for physics” — Anima Anandkumar, Bren Professor of Computing

Caltech professor Anima Anandkumar discusses Neural Operators and FourCastNet for physics modeling, arguing inductive biases beat pure token scaling.

Anima Anandkumar, Bren Professor at Caltech and co-founder of Accelerated Understanding, describes Fourier Neural Operators that learn in frequency and spherical-harmonic domains to model weather, fusion, and fluid or heat flow. Her team built FourCastNet 3, a global weather model competitive with physics-based simulations that runs on consumer-grade GPUs. She also introduced TorchLean, a framework for writing PyTorch-style networks inside the Lean proof assistant for formal verification, and was appointed to the United Nations Scientific Advisory Board. She argues physical domains resist scaling due to tiny datasets and context lengths in the hundreds of billions, so progress comes from built-in structure and physical priors.

Latent SpaceAug 26, 15:15 UTC · 20d agoAI research1

The Permanent Threat: Analyzing Aeternum’s Blockchain-Based C2 Operations and Communications

Unit 42 analyzes the Aeternum botnet loader, which uses Polygon blockchain smart contracts for resilient decentralized C2 and payload execution.

Palo Alto Unit 42 published a technical analysis of Aeternum, a botnet loader that leverages Polygon blockchain smart contracts as its command-and-control infrastructure. The decentralized C2 design makes takedown difficult, giving the threat a persistent, 'permanent' communication channel for payload delivery and execution. The analysis covers the smart contract mechanics and communication protocol used by the operation.

Palo Alto Unit 42Aug 10, 22:00 UTC · Aug 10, 2026Malware

OpenVDN/vdn-minimax-h3 — new model trending #12 on Hugging Face

OpenVDN releases VDN-H3, an open hybrid-attention video model on MiniMax H3 that renders a 14.4-second 768p clip in 11.23 seconds on 8 B200 GPUs.

VDN-Minimax-H3 (VDN-H3) adds a frame-wise linear attention branch plus two LoRA adapters to MiniMax H3, distilled into 8-step and 50-step variants. It generates 768p, 14.4-second clips in 11.23 seconds on 8 B200 GPUs (90.5 seconds on one H200) using 8 denoising steps. Weights (about 82 GB total, including the 72 GB H3 base), the optimized inference stack, and training code are fully open-source under the MiniMax H3 Community License, which excludes the EU, UK, Korea, and US.

Hugging Face trending modelsSep 2, 05:03 UTC · 14d agoModel release1