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“Hype”
galaxy.comarticle🛡️ Security

Weekly Research Brief: Binance Hacked; Hyperliquid on Wall Street; Prediction Markets’ Brazil ‘Miss’

Galaxy Research's weekly brief of 30 October 2026 opens with the Binance hack: about $6.3b drained across 10 blockchains, attributed by TRM Labs and Chainalysis to TraderTraitor (Lazarus-linked). The attacker used EIP-7702 with 50 pre-signed authorizations on Ethereum, and Binance's preliminary finding is a compromised DevOps laptop reaching a service container. Tether and Circle froze about $2.6b. The other sections cover Hyperliquid and prediction markets.

EthereumBitcoinUSDC+2
TraderTraitorLazarus GroupEIP-7702+7

Blockchain, Threat Intel· Added 30 Oct 2026

x.comthread🧠 AI

Artificial Analysis (@ArtificialAnlys) on X

Hyperflow H3 is Video Rebirth's 8-step LoRA for MiniMax H3, released in September 2026 with open weights. Artificial Analysis ranks it #5 on AA-Video-I2V v2.1 and #3 on AA-Video-Editing v1.1, ahead of MiniMax H3 (768p). It costs $9.00 per minute for Image to Video and $18.00 for Video Editing at 1088p, under half of Dreamina Seedance 2.5 ($34.12 and $40.82). The thread gives per-capability and per-use-case breakdowns.

LoRAdiffusion model
Artificial AnalysisVideo RebirthHyperflow H3+6

models, evals, multimodal· Added 31 Oct 2026, published 30 Oct 2026

rekt.newsarticle🧠 AI

Rekt - News or Hype?

Rekt's "News or Hype?" compares four October 2026 AI headlines with their sources. These are Justin Drake's "bunker mode" ECDSA warning, Anthropic's Claude tracing NIST curve seeds to lrand48, OpenAI's agent spending Sepolia ETH from a default-mnemonic account, and Wiz's "AI uncovers" PHP RCE claim of 70% reach. It ends with four questions for checking claims.

ECDSABitcoinEthereum+11
CVE-2026-109618CVE-2026-109619Justin Drake+17

ai-safety, agents, ai-policy· Added 30 Oct 2026

rekt.newsarticle🛡️ Security

Rekt - NEAR Intents - Rekt

Rekt News analysis of the 30 September to 1 October 2026 theft of 3.865 million USDT from the NEAR Intents HOT Bridge treasury on BNB Chain, paid out in five signed withdrawals after a 10 USDT deposit. It uses Bitquery's trace of laundering via Chainflip, THORChain, KuCoin and Hyperliquid, and a NEAR regression test showing an oversized refund log made mt_resolve_deposit fail. NEAR said the funds were returned but published no report.

BNB ChainHOT BridgeNEAR+3
NEAR IntentsBitqueryZachXBT+13

Blockchain, Threat Intel· Added 29 Oct 2026

rekt.newsarticle🛡️ Security

Rekt - News or Hype?

Rekt News piece tracing three AI-security headlines back to their sources. It covers Justin Drake's October 2026 "bunker mode" warning about AI breaking ECDSA, OpenAI's report of a model spending Sepolia test ETH from a default developer mnemonic, and Wiz's "70% of the Internet" claim for PHP CVE-2026-109618 and CVE-2026-109619. It closes with four questions for checking a claim.

OpenAIPHPEthereum+3
CVE-2026-109618CVE-2026-109619Justin Drake+13

AI Security, other· Added 29 Oct 2026

bleepingcomputer.comarticle🛡️ Security

Fake crypto app on Apple App Store linked to $23K wallet theft

Padre Terminal, a fake Solana meme-coin trading app on the Apple App Store, drained about $23,000 from 27 Solana wallets after users imported private keys, per blockchain investigator Iris. The app has been removed, but the drainer stayed active; Iris traced funds to two attacker wallets holding 229.66 SOL. Iris also flagged four other suspected impersonator apps (Axiom Pro: Focus, GMGN AI, HyperTerminal-HyperliquidTools, Pump Fun Tokens & Analitycs).

iOSSolanaUSDC
Padre TerminalIrispump.fun+8

Mobile Security, Threat Intel· Added 30 Oct 2026, published 29 Oct 2026

en.cryptonomist.charticle🧠 AI

Hygentra: AI Trading Agents Kept 97.5% of Losses by Value Within Limits

Hygentra's six-month study (February to August 2026) audited four AI trading agents, each with its own strategy and preset risk rules, through a 29% Bitcoin decline and recovery. 97.5% of losses by value stayed within limits, 23 of 24 runs respected the five-open-trade cap, and no agent raised risk after a loss. Trade records and testing tools were published; 18% of positions were real Hyperliquid orders, the rest simulated with a 0.09% round-trip fee.

Hyperliquid
HygentraSaad NajaBitcoin+2

evals, agents· Published and added 28 Oct 2026

cisa.govadvisory🛡️ Security

Mitsubishi Electric and Mitsubishi Electric Iconics Digital Solutions GENESIS, GENESIS64, ICONICS Suite, Hyper Historian, AnalytiX, MobileHMI, IoTWorX, MC Works64, GENESIS32 and BizViz

CISA advisory ICSA-26-300-02 covers four vulnerabilities in Mitsubishi Electric and Mitsubishi Electric Iconics Digital Solutions GENESIS64, ICONICS Suite, Hyper Historian, AnalytiX, MobileHMI, IoTWorX, MC Works64, GENESIS32 and BizViz. CVE-2023-4863 (CWE-787 out-of-bounds write) allows code execution; CVE-2026-16436 and CVE-2026-16437 allow authentication bypass; CVE-2026-16438 allows arbitrary operations. Fixes are 10.97.3, 10.98 and GENESIS 11.04. MC Works64, GENESIS32 and BizViz are affected in all versions, and MC Works64 has no fix planned. Mitigations include removing the ClientChallengeTable.dat or SecurityChallenges.dat file contents. The fetched text was truncated, so later content is not covered.

GENESIS64ICONICS SuiteHyper Historian+3
CVE-2023-4863CVE-2026-16436CVE-2026-16437+6

Offensive Security, Identity & Access· Added 29 Oct 2026, published 26 Oct 2026

blog.alexewerlof.comarticle🧠 AI

The rise and fall of Jev hype

The essay uses the launch of Jev, TypeSafe AI's "System One" general-purpose classifier trained with RLCD, to dissect AI marketing patterns: name dropping, jargon, ambiguity, inflated claims and drama. The author argues Jev's hype peaked and collapsed within about 33 days, while open-weight rivals beat it on cost and performance. It also lists incremental advances since ChatGPT and four unsolved problems: abstract reasoning, continuous learning, embodiment and the economics of running AI.

JevLLMModel Context Protocol+4
TypeSafe AITypeSafeOpenAI+14

ai-policy, models· Added 24 Oct 2026, published 23 Oct 2026

protos.comarticle🧠 AI

Inside AMD and Anthropic’s ‘chips for shares’ deal

Protos reports on the AMD and Anthropic compute deal: Anthropic commits to buy over $20 billion of AMD AI compute, up to 2 GW of Instinct MI450 chips from the first half of 2027, while AMD commits up to $5 billion to Anthropic stock through fiscal 2028. It cites WSJ reporting that hyperscalers cap Anthropic's compute, and Anthropic's 2025 operating loss above $8 billion on $4.6 billion revenue.

AMD Instinct MI450Claude
AnthropicAMDTom Brown+1

ai-policy, inference· Published and added 21 Oct 2026

en.cryptonomist.charticle🧠 AI

Mastercard’s agent payments enter early access after nine-payment demo

Mastercard's Agent Pay for Machines lets AI agents buy digital services with signed payment vouchers inside a business-set task, permission and budget. On 20 October 2026 it opened Early Access with buyer and seller APIs, a toolkit, sandbox and docs. In the Hype demo at Cloudflare Connect, an agent made nine payments to two providers over the x402 protocol. The system is still at pilot stage.

x402CloudflareAmazon Bedrock AgentCore+5
MastercardCoinbaseBVNK+4

agents, ai-infra· Published and added 20 Oct 2026

bankless.comarticle🛡️ Security

ZachXBT Flags $20M XMR Laundering Ring on Hyperliquid

FreedomXMR (FXMR) is a centralized Monero swap service on Hyperliquid Core that appeared in August 2026. ZachXBT's manual review of every inflow above $10K traced over $20M, more than 87% of volume, to ransomware, malware and exploits including DCENT Wallet, Fetch.ai, Axelar and Gamdom. The admin sources XMR liquidity from KuCoin and won the FXMR ticker in an auction for 518.28 HYPE.

HyperliquidMonero
ZachXBTFreedomXMRDCENT Wallet exploit+7

Threat Intel, Blockchain· Published and added 20 Oct 2026

eprint.iacr.orgpaper🛡️ Security

Zippel: A Language, Compiler, Runtime, and Analyzer for ZK Proof Systems

Zippel is a language, compiler, runtime and analyzer for writing zero-knowledge proof systems in syntax close to the pseudocode in cryptography papers. Its type system and runtime remove vulnerability classes by construction, for example by synchronizing transcripts automatically and applying the Fiat–Shamir transform correctly. Its static analyses check completeness and soundness from the protocol description alone. The authors implemented 35 proof systems, including Sumcheck, Schnorr, KZG, Groth16, Bulletproofs, Hyrax, Spartan, Dory, HyperPlonk, DeKART and Pari. They report performance competitive with hand-written multi-core implementations.

ZippelRust
Fiat–Shamir transformSumcheckSchnorr+9

AppSec· Added 17 Oct 2026, published 16 Oct 2026

x.comthread🛡️ Security

Dark Web Informer (@DarkWebInformer) on X

Social post from DarkWebInformer announcing a released proof-of-concept for CVE-2026-82957, a server-side request forgery in Hyperledger FireFly. The flaw lies in webhook subscriptions, where a user-controlled webhook URL is accepted without validating the destination host. The PoC is hosted in a hakaioffsec GitHub repo.

Hyperledger FireFlyGitHub
CVE-2026-82957SSRFCWE-918+1

AppSec, Offensive Security· Added 15 Oct 2026, published 14 Oct 2026

blog.talosintelligence.comadvisory🛡️ Security

Microsoft Patch Tuesday for October 2026 — Snort rules and prominent vulnerabilities

Talos summarises Microsoft's October 2026 Patch Tuesday, which covers 1415 vulnerabilities, 85 of them critical (67 RCE). One bug is exploited in the wild: CVE-2026-96951, a Windows Shell security feature bypass. The post groups the CVEs by Microsoft's exploitability rating (more likely, less likely, unlikely) with CVSS and CWE for each, covering Windows, Office, Exchange, Hyper-V, DHCP, QUIC, Outlook and Azure. It also lists the Snort 2 and Snort 3 rule ID ranges that detect exploitation attempts.

WindowsSnortMicrosoft Exchange Server+3
CVE-2026-96951CVE-2026-70229CVE-2026-70571+191

Blue Team & Detection· Published and added 13 Oct 2026

x.comthread🛡️ Security

Dark Web Informer (@DarkWebInformer) on X

Social post announcing a PoC (hakaioffsec) for CVE-2026-82957, an SSRF in Hyperledger FireFly ≤ 1.5.0 (CVSS 8.5). Webhook subscriptions accept a user-controlled URL without validating the destination host, letting the FireFly node reach internal services, private networks, loopback and cloud metadata endpoints. The PoC creates a malicious webhook subscription, triggers a matching event and confirms the request originates from the FireFly server.

Hyperledger FireFlyCVSS
CVE-2026-82957SSRFCWE-918+1

AppSec, Offensive Security· Published and added 13 Oct 2026

deadsimpletech.comarticle🧠 AI

There's little that's "inevitable" about AI

An essay arguing that LLM adoption is not "inevitable". It sets four criteria for inevitability: output not markedly inferior, socially valuable work, overwhelming advantage, and no cheap way to neutralise the technology. Firearms pass all four, the magnetic compass does not, and chemical weapons (following Bret Devereaux) are the closest analogy for LLMs. The author concludes LLMs fail the two criteria that matter most. They are useful mainly to weaker players, and strong practitioners can outcompete them once belief in the hype erodes.

LLM
Bret DevereauxArca

ai-policy· Added 11 Oct 2026, published 10 Oct 2026

huggingface.copaper🧠 AI

TokenRouter: Efficient Serving System for Token-Level LLM Routing

TokenRouter is a serving system for token-level routed LLM inference, where different tokens of one request are routed across different models. It uses request-centric programming with model-centric execution: developers write routing logic per request while the runtime launches a subserver per LLM and dispatches requests asynchronously. Each subserver uses a delayed-batching scheduler whose hyperparameters come from a mathematical throughput model, fixing step desynchronization and batch admission delays and giving 2.01-64.15x higher decoding throughput than existing systems.

TokenRouterHugging FaceGitHub

inference, research· Added 9 Oct 2026

arxiv.orgpaper🧠 AI

Multi-Stream Attention Residuals

Introduces Multi-Stream Attention Residuals (MSAR), which extends Attention Residuals (AttnRes, single-stream softmax attention over network depth) to multi-head depth attention. Heads get diverse views through multi-scale causal convolutions over sequence context, and the retrieved streams are merged by lightweight adaptive mixing into a single-stream layer input, so no persistent multi-stream storage or communication is needed. It beats standard residuals, manifold-constrained Hyper-Connections (mHC) and AttnRes on 12 downstream benchmarks at 18B and 28B MoE scales, with an iso-loss compute speedup of 1.58x versus 1.35x for AttnRes and 1.30x for mHC.

TransformerMixture of Experts
Multi-Stream Attention ResidualsAttention ResidualsHyper-Connections+1

research· Added 12 Oct 2026, published 9 Oct 2026

exploitreversing.comarticle🛡️ Security

Exploit Reversing #10: iOS Security Research (Part 1)

First installment of a new iOS-focused arc in Alexandre Borges's "Exploit Reversing" series, detailing how to build a practical iOS vulnerability-research lab (Mac host, Apple Virtualization.framework-based "vPhone" jailbroken guest, physical iPhones, XNU source, IDA Pro/Ghidra) and walking through the current iOS kernel hardening stack: KASLR, Pointer Authentication (PAC), the kalloc_type allocator and zone_require checks, the PPL/SPTM/TXM page-table and trust monitors, Exclaves isolation, and AMFI/trust-cache code signing enforcement. It also references the public Titan PPL/SPTM bypass (A14–A17 devices, iOS 16.1–17.4 beta 3) as the state of the art for defeating these protections. Part of a long-running, actively published series on Windows, iOS, and hypervisor exploitation.

iOSmacOSXNU+2
PACKASLRSPTM+9

Mobile Security, Offensive Security· Added 8 Oct 2026

arxiv.orgpaper🧠 AI

A Closer Look at Agentic BBO: Benchmarking LLM Agents for Black-Box Optimization

Introduces AgenticBBO-Bench, a benchmark for LLM-agent black-box optimization across synthetic functions, hyperparameter optimization, database tuning, chip design and molecular design under a unified finite-budget protocol. Agentic BBO beats direct LLM methods in all five domains and the best numerical optimizers in four. Ablations show extra numerical tools don't consistently help, task semantics help more than specific priors, and numerical optimizers can absorb gains from agent-found trajectories. A five-task frontier challenge evaluates seven LLMs under the Codex agent harness, with GPT-6 Astra and DeepSeek-V4.1-Flash on the performance-cost Pareto frontier.

Codex
AgenticBBO-BenchGPT-6 AstraDeepSeek-V4.1-Flash

evals, agents, research· Added 9 Oct 2026, published 8 Oct 2026

github.comrepo🧠 AI

microsoft/mxc

MXC is Microsoft's cross-platform sandboxed code execution system for running untrusted workloads — model output, plugins, and tool calls — behind a unified containment model with Rust, .NET, and Node SDKs. It offers multiple containment backends (ProcessContainer, Windows Sandbox, LXC, Bubblewrap, Seatbelt, MicroVM/Nanvix, Hyperlight, WSLC) with JSON-based filesystem, network, and UI policies, plus diagnostics like audit mode for building policies from observed access.

Rust.NETNode.js+1
Microsoft

ai-infra· Added 8 Oct 2026

github.comrepo🛡️ Security

microsoft/mxc

MXC is Microsoft's cross-platform sandboxed code execution system for running untrusted code such as LLM/agent output, plugins, and tools, offering containment backends ranging from OS-native process sandboxes (Windows Sandbox, LXC, Bubblewrap, Seatbelt) to full VMs (Nanvix MicroVM, Hyperlight). It exposes a unified JSON-based policy model for filesystem, network, and UI access controls, with Rust, .NET, and Node SDKs plus diagnostic tooling (audit mode, access-denied logging) to help developers tune containment rules for agentic workloads.

WindowsLinuxmacOS+3

AI Security, Offensive Security· Added 8 Oct 2026

arxiv.orgpaper🧠 AI

Training Parallel Speculative Draft Models by Directly Minimizing Expected Decoding Rounds

The paper formalizes speculative decoding as a Markov reward process and derives the Expected Decoding Rounds (EDR) objective, a hyperparameter-free training loss equal to the expected number of decoding rounds, addressing cross-round coupling that prior block-local surrogate objectives ignore. It derives an exact temporal-difference gradient for unbiased stochastic optimization and an offline evaluator for comparing drafters on shared rollouts without running speculative decoding. Finetuning the DSpark and DFly drafters with EDR improves mean accepted length and beats prior training objectives across nine benchmarks in math, code, and chat.

DSparkDFly

research, training· Added 8 Oct 2026, published 7 Oct 2026

interconnects.aiarticle🛡️ Security

The Cyber Risk Discourse is Broken

Nathan Lambert's Interconnects op-ed argues the "open weights are a cyber weapon" discourse ignores that documented real-world AI-enabled cyber incidents to date trace mostly to closed-API models (OpenAI), not open-weight ones, and critiques Anthropic's GLM-5.3 offensive-cyber risk report for not engaging with the trade-offs of banning open models. It also unpacks how Chinese labs (e.g., Kimi K3, GLM-5.3) assess and register AI risk differently from US labs, and contrasts the hype around Claude Mythos's hypothetical leak with GLM-5.3's actual open release having caused no comparable societal disruption.

GLMKimi K3Anthropic+2
GLM-5.3MythosJoshua Saxe+2

AI Security, GRC· Published and added 6 Oct 2026

endorlabs.comarticle🛡️ Security

Sandboxes Explained: What Each Type Actually Isolates | Blog | Endor Labs

An Endor Labs primer surveying what different sandbox technologies actually isolate: hypervisor-based VMs (Firecracker microVMs, the VENOM floppy-controller escape CVE-2015-3456, Spectre/Meltdown), Linux containers (namespaces/cgroups/seccomp, the runc host-overwrite escape CVE-2019-5736, gVisor's userspace kernel, Kata Containers' per-pod microVMs), and language-runtime sandboxes (Java's deprecated SecurityManager, V8 isolates behind Cloudflare Workers, WebAssembly/WASI's capability model). It then pivots to AI agent sandboxing, arguing the threat model flips from "what can this process touch" to "what actions can this confused-deputy agent take," framed around Simon Willison's lethal trifecta (private data, untrusted content, external communication) and controls like filesystem scoping, egress allowlists, scoped short-lived credentials, and session-level audit logging.

FirecrackerQEMUgVisor+3
CVE-2015-3456CVE-2019-5736Spectre+6

AI Security, Cloud & Infra· Published and added 2 Oct 2026

huggingface.corepo🧠 AI

Tencent Hy4 preview (open-weight MoE + Sherry quantization)

Hy4 preview is Tencent's new flagship Mixture-of-Experts language model: 770B total parameters with 49B activated per token across 78 layers (256 routed experts + 1 shared expert, top-8 routing), plus a native 10B multi-token-prediction layer for speculative decoding. It introduces Gated DeepSeek Sparse Attention with IndexCache for cross-layer index reuse and identity Hyper-Connections (iHC) on the residual path, and in blind internal evaluations on engineering tasks it edges out GLM-5.3 and Kimi K3. Weights (including an FP8 variant) are released under Apache 2.0 on Hugging Face with vLLM/SGLang deployment recipes and AngelSlim-based quantization/compression tooling.

Hugging FacevLLMSGLang+4
TencentTencent Hy4 previewHy3 preview+17

models· Added 2 Oct 2026

arxiv.orgpaper🧠 AI

Multimodal Flow: Unified Flow Modeling of Language and Vision in Embedding Spaces

Multimodal Flow (MF-1) is a fully continuous generative model that unifies language and vision modeling by representing both text and images as ordered continuous "hyperchunks" processed through a single shared chunk-causal Flow Matching backbone, avoiding the visual quantization bottleneck of discrete-token unified models and the mismatched objectives of hybrid discrete/continuous approaches. Joint attention handles cross-modal interaction while modality-specific feed-forward networks process each modality, and the model predicts multiple chunks in parallel during training but generates sequentially at inference. Pretrained at 0.6B/1.2B/1.6B scales on 150B tokens, MF-1 scores 82.8 average on GenEval/DPG-Bench and 75.3 on VQAv2/MMBench/POPE, remaining competitive with unified models trained on far more data and outperforming matched hybrid/discrete baselines; code and model are publicly released.

flow matchingvision-language model
GenEvalDPG-BenchVQAv2+2

research, multimodal· Added 1 Oct 2026, published 30 Sep 2026

arxiv.orgpaper🧠 AI

EvE: An Alternate Optimizer to Adam

EvE (Evolutionary Explorer) is a steady-state, population-of-four differential evolution optimizer with a targeted Adam fallback, proposed as an alternative to Adam for cheap, early ranking of configurations in hyperparameter and architecture search. Under a fixed evaluation-cost-matched budget it wins or ties Adam on 76% of 70 (problem, dimension) cells across seven scalable benchmarks up to one million variables, and on real tasks (MLP on MNIST, LoRA fine-tuning of a 1.5B-parameter LLM on two datasets) it finishes 1.7-3.9x faster at a modest quality cost. Inside successive halving on UCI Adult it completes hyperparameter/architecture searches 3.1-3.5x faster while ranking configurations nearly as consistently as Adam does with itself across seeds (Kendall's tau 0.66-0.69).

LoRA
MNISTGSM8KUCI Adult

training, research· Added 29 Sep 2026, published 28 Sep 2026

github.comrepo🧠 AI

zhuyansen/awesome-claude-video-skills

Awesome-list repository cataloguing 183 open-source 'skills' and toolkits that let Claude Code, Codex and other coding agents generate or edit video: rendering frameworks like HyperFrames and Remotion, motion graphics, explainer/promo video generators, short-form editors, digital avatars and music videos. Each entry is read and security-graded by a companion service (Agent Skills Hub / agentskillshub.top), with a machine-readable JSON feed and category breakdowns (frameworks, promos, explainers, editing, shorts, avatars, animation, motion graphics, scripts, music videos).

Claude CodeCodexModel Context Protocol+10
Claude Opus 5.5

coding-agents, multimodal· Added 29 Sep 2026, published 27 Sep 2026

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