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“OSI”
securityweek.comarticle🛡️ Security

Polymarket Returns $3.9 Million to Traders as DebugBear Cites DNS Hijack

Polymarket is returning $3.9 million to traders after an October 29, 2026 supply chain attack in which malicious JavaScript was served from a DebugBear address its site loaded. DebugBear says its DNS records were compromised through its domain registrar, and attackers appear to have targeted one customer. The script abused open trading sessions to sell positions or move assets; 726 wallets were affected, and about 5,500 wallets received $1.19 million for halt and forced-sale losses.

PolymarketJavaScript
DebugBearHudson RockDNS hijack+1

Supply Chain, Threat Intel· Published and added 2 Nov 2026

theblock.coarticle🛡️ Security

North Korean hackers launder $242 million of Binance loot via Tron as FBI presses THORChain: analyst

The Block reports that North Korean hackers (TraderTraitor) laundered about $242 million of funds stolen from Binance through Tron in two days, according to analyst SomaXBT. Relay, THORChain, Chainflip and Symbiosis moved about $140 million off Tron. Elliptic counts $2.85 billion frozen or seized, 44% of $6.43 billion stolen, and $3.25 billion in assets no issuer can freeze. The FBI urged THORChain and others to block listed addresses.

TronTHORChain
TraderTraitorDPRKBinance+6

Blockchain, Threat Intel· Published and added 1 Nov 2026

securityweek.comarticle🛡️ Security

$2 Million+ Drained From Polymarket Traders in New Supply Chain Attack

Polymarket halted trading on 29 October after users reported unauthorized withdrawals traced to malicious JavaScript served from DebugBear's real user monitoring snippet. Hudson Rock's sample abused the authenticated session to sell positions or send tokens to a hard-coded address, without exfiltrating credentials. Loss estimates range from $2.19 million to $2.71 million (GoPlus), and Polymarket pledged reimbursement.

PolymarketDebugBearJavaScript
Hudson RockGoPlusArkham+2

Supply Chain, Threat Intel· Published and added 30 Oct 2026

en.cryptonomist.charticle🧠 AI

Meta’s AI-distilled model detector catches 79% at a 0.1% false-positive rate

WM-Basin is a Meta method for detecting student LLMs distilled from watermarked teacher models, described in "Watermark Basins: Black-Box Detection of Distilled LLMs" (arXiv:2610.37266, 27 October 2026). It feeds a suspect model's API text into a clean probe model and checks gradient alignment with a key-specific watermark basin. It reached 79% true-positive rate at 0.1% false-positive rate across 1,200 students, versus 29% for the prior token-level detector. Clean-data fine-tuning and paraphrasing weaken it.

LLM
MetaMeta FAIRMeta Superintelligence Labs+3

research, training· Published and added 30 Oct 2026

bleepingcomputer.comarticle🛡️ Security

Hugging Face malware campaign returns to hijack GPUs for crypto mining

A campaign of 31 malicious Hugging Face repositories across seven accounts, found by Pluto Security, posed as small Qwen2 checkpoints and used trust_remote_code to run a cryptominer and reverse shell on GPU inference hosts. The top model reached 28,412 downloads and scanned clean, and a second wave returned within days of takedown. The article also covers a fake Solana starter kit on GitHub that installs NetSupport Manager via a PowerShell one-liner aimed at AI coding agents.

Hugging FaceQwenXinference+3
Pluto SecurityAir Securitytrust_remote_code+9

Supply Chain, AI Security, Threat Intel· Added 30 Oct 2026, published 29 Oct 2026

bleepingcomputer.comarticle🛡️ Security

Japan Airlines suffers third-party data breach exposing passenger info

Japan Airlines disclosed that unauthorized access to Amadeus's Passenger Service System, between 10 August 2023 and 23 September 2026, exposed data on about 92,000 customers. Roughly 2,000 also had emails, phone and passport numbers exposed. ANA, Asiana Airlines and Korean Air are also reported as affected. Amadeus had announced a booking display vulnerability on 1 October 2026 and has not given the full scope.

Amadeus Passenger Service System
Japan AirlinesAmadeusAll Nippon Airways+3

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

bleepingcomputer.comarticle🛡️ Security

Polymarket halts trading after crypto-draining script breach, $2.5M traced

Polymarket suspended trading for about an hour on 29 October 2026 after attacker-controlled JavaScript was served through DebugBear's Real User Monitoring script (cdn.debugbear[.]com/C7xcKKrjUnXK.js). The script hijacked logged-in trading sessions, sold positions and moved assets to an attacker wallet, with about $2.5M traced. The article gives the wallet address, the data.debugbear.com C2 endpoint and Hudson Rock's findings.

PolymarketDebugBearJavaScript+1
Hudson Rock0x3F99139F1C5854bb628c2e576Aaf638e99dFfc18cdn.debugbear.com/C7xcKKrjUnXK.js+3

Supply Chain, Threat Intel· Added 30 Oct 2026, published 29 Oct 2026

rekt.newswriteup🛡️ Security

Rekt - Duelbits - Rekt

Duelbits is a crypto casino that lost an estimated $6.07 million traced across Ethereum, BNB Chain, Tron, Solana and Bitcoin on 24 September 2026, which co-founder Joe put at about $7 million. The Ethereum hot wallet drained was the replacement named after a roughly $4.6 million theft in February 2024. Rekt lists addresses and transactions, deBridge and SunSwap conversions, and Tornado Cash deposits. Duelbits has not published a root cause.

EthereumBitcoinSolana+3
Duelbitsprivate key compromiseScam Sniffer+8

Blockchain, Threat Intel· Added 29 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

oversecured.comwriteup🛡️ Security

Introducing MavenGate: a supply chain attack method for Java and Android applications | Oversecured Blog

MavenGate is Oversecured's research on hijacking Maven dependencies by buying expired domains behind groupIds on Maven Central and JitPack. It found 6,170 of 33,938 checked domains (18.18%) vulnerable, and tested the attack with its own com.oversecured:hello-world library. It lists defenses (Gradle verification-metadata.xml, PGP signatures) and proposes changes to repository signing.

MavenGradleMaven Central+3
MavenGateOversecuredSonatype+2

Supply Chain, Mobile Security, AppSec· Added 29 Oct 2026

decrypt.coarticle🛡️ Security

Polymarket Pauses Trading as Prediction Market Investigates Security Incident

Polymarket put its exchange in cancel-only mode and paused relayer actions on 29 October 2026 after users reported funds withdrawn without permission. Hudson Rock says a DebugBear-hosted script holds wallet-draining code that sells a logged-in user's positions and hard-codes the suspected attacker's wallet (about $2.65 million in POL, per Arkham) as fallback recipient. Polymarket blames a compromised third-party provider and promises to make users whole. Root cause and victim count are unconfirmed.

DebugBearPolygonRelay
PolymarketHudson RockArkham+2

Supply Chain, Threat Intel· Published and added 29 Oct 2026

arxiv.orgpaper🛡️ Security

TextSeal2: Distortion-Free LLM Watermarking with Copula Mixing and Memoryless Races

TextSeal2 is a distortion-free LLM text watermark that merges two or more keys into one virtual key and couples it to fresh noise through AutoMark's Gaussian copula. Under speculative decoding it verifies keyed drafts with an extra exponential race, so output matches the target model and acceptance rate is unchanged. On three LLMs at a 10^-3 false-positive rate, it detects 8 to 22 points more 100-token answers than TextSeal.

TextSeal2TextSealAutoMark+2
Gumbel-max schemespeculative decodingGaussian copula+1

AI Security· Added 31 Oct 2026, published 29 Oct 2026

arxiv.orgpaper🧠 AI

TextSeal2: Distortion-Free LLM Watermarking with Copula Mixing and Memoryless Races

TextSeal2 is a distortion-free watermark for LLM output that keeps its signal under speculative decoding. It merges two or more keys into one virtual key coupled to fresh noise through AutoMark's Gaussian copula, and verifies drafts with an extra exponential race. On three LLMs it detects 8 to 22 points more 100-token answers than TextSeal at a 10^-3 false-positive rate.

LLMspeculative decodingGumbel-max watermarking+1
TextSeal2TextSealAutoMark+2

research, inference· Added 31 Oct 2026, published 29 Oct 2026

x.comthread🛡️ Security

Rohan Paul (@rohanpaul_ai) on X

Rohan Paul's X post summarizes a TikTok paper on MCP attacks. LLM agents often finish the requested task, then quietly call an attacker-controlled logging tool that records the user query, the server reply and the final answer. The attack works because agents read natural-language tool descriptions when choosing and ordering tool calls. The visible text is truncated and gives no success rates or defenses.

Model Context ProtocolLLM
tool poisoningdata exfiltration

AI Security· Added 30 Oct 2026, published 29 Oct 2026

microsoft.comarticle🛡️ Security

Tracking AI adoption in North Korean threat actor tradecraft

Microsoft Threat Intelligence report on how North Korean actors use AI, ranking Jade Sleet, Coral Sleet and Jasper Sleet as operationalized, Diamond Sleet, Onyx Sleet, Pearl Sleet and Sapphire Sleet as accelerating, and Emerald Sleet as expanding. It documents AI-generated lures (100+ Onyx Sleet PDFs, August and September 2026), AI-built tooling such as Coral Sleet's private GitHub repository trojanizing, and Onyx Sleet and Pearl Sleet use of the ARTEX agentic pentest framework. It ends with mitigation and Defender guidance.

CursorGitHubRust+2
Jade SleetOnyx SleetCoral Sleet+9

Threat Intel, AI Security· Published and added 29 Oct 2026

eprint.iacr.orgpaper🛡️ Security

A Quantitative Evaluation of Constant-Time Analysis Tools

AutoBench-CT is a modular benchmark for constant-time analysis tools, with manually verified per-violation ground truth across 64 cryptographic tasks. It scores seven tools on operational performance, detection quality and usability. The authors explain why CT-grind (2010) is still the most used tool, list recurring limitations across tools, and propose a composite pipeline for stronger constant-time guarantees. Accepted at ACSAC 2026.

AutoBench-CTCT-grind
ACSAC

Hardware & RF, AppSec· Added 31 Oct 2026, published 29 Oct 2026

arxiv.orgpaper🧠 AI

M-PFT: Angular Margin for Calibrated Prompt-Tuning of Fine-Tuned Vision-Language Models

M-PFT is an angular-margin method for prompt tuning of vision-language models that fixes base-to-novel dual miscalibration (underconfidence on base classes, overconfidence on novel classes). It adds two losses: minimizing the maximum cosine similarity between tunable class embeddings, and minimizing angular distance to each zero-shot embedding. Tests on 9 prompt-tuning methods and 15 datasets show lower novel-class calibration error at comparable accuracy and negligible overhead.

vision-language modelprompt tuning
arXiv

research, multimodal, training· Added 31 Oct 2026, published 29 Oct 2026

arxiv.orgpaper🧠 AI

Aligning Medical Vision Language Models at the Capability Boundary with Entropy Adaptive Rubric Grounding

MedCBR is a two-stage alignment framework for medical vision language models. Difficulty Weighted Boundary Preference Optimization (DBPO) trains on counterfactual preference pairs at the model's capability frontier. Entropy Gated Grounded Policy Optimization (EGPO) then applies distilled clinical rubrics according to prediction entropy. It reaches 61.2% average accuracy on six medical benchmarks, with the largest gains on differential diagnosis, and the two stages combine super-additively.

vision-language modelpreference optimizationreinforcement learning
MedCBRDifficulty Weighted Boundary Preference OptimizationEntropy Gated Grounded Policy Optimization+1

research, training, multimodal· Added 31 Oct 2026, published 29 Oct 2026

thedefiant.ioarticle🛡️ Security

Wagyu Confirms Data Leak Exposing Monero Swap Records

Wagyu, a Monero swap service, confirmed a data leak after Hudson Rock found 4,580 orders in exposed records. The records link public-chain deposit transactions to Monero destination addresses and client IP addresses, which undoes the privacy a swap into Monero is meant to give. The fetched page held only the headline and subhead, so the cause, timeline and remediation are not covered.

Monero
WagyuHudson Rock

Threat Intel· Published and added 29 Oct 2026

x.comthread🛡️ Security

Rohan Paul (@rohanpaul_ai) on X

Rohan Paul's X post summarizes the paper "Log-To-Leak: Prompt Injection Attacks on Tool-Using LLM Agents via the Model Context Protocol", from TikTok researchers. It reports that MCP-using agents often finish the requested task, then quietly call an attacker-controlled logging tool that records the query, server reply and final answer. The attack works through natural-language tool descriptions that agents read when choosing and ordering tools. The visible text is truncated.

Model Context ProtocolLLM
Log-To-Leakprompt injectionTikTok+1

AI Security· Added 29 Oct 2026, published 28 Oct 2026

unit42.paloaltonetworks.comarticle🛡️ Security

Unwrapping Retail Peak Season Cyber Threats

Unit 42 overview of cyber threats facing retailers in the 2026 peak shopping season, split into enterprise threats (ransomware, data extortion, DDoS, digital skimming) and guest threats (account takeover, gift card fraud, AI-powered attacks). It cites 565 retail ransomware victims claimed as of 30 September 2026 and past incidents such as CosmicSting (CVE-2024-34102) skimming and the Noname057(16) DDoSia attacks. Each threat has defensive recommendations.

CVE-2024-34102CosmicStingTGR-CRI-1173+22

Threat Intel· Added 30 Oct 2026, published 28 Oct 2026

simonwillison.netarticle🧠 AI

GitHub Octoverse 2026

Simon Willison's link post on GitHub Octoverse 2026, announced by GitHub EVP Jay Parikh at GitHub Universe on 28 October 2026. Merged pull requests tripled in under a year to 220 million per month, commits passed 7.38 billion per month, and new repositories doubled to 30.3 million per month. It quotes the methodology for country, industry (NAICS) and "interoperability or reverse engineering" repository counts. The post does not analyse the report beyond these figures.

GitHub
Simon WillisonJay ParikhOctoverse

ai-policy, coding-agents· Published and added 28 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

arxiv.orgpaper🧠 AI

Statistical Detection of Positional Bias in LLM-Based Recommender Systems: Methodologies and Empirical Evidence

The paper proposes POB-ICM (contingency matrix with Chi-square independence testing) and POB-UNBP (a uniformity null model under permutation-based evaluation) to detect positional bias in top-1, single-turn LLM recommenders. On a RAG product-recommendation testbed with 2000 queries and 20 permutations each (40,000 instances), both methods reject positional independence in all evaluated models. Three metrics quantify the effect, and the bias persists across candidate-set size and temperature. Code is public.

LLMRAG
arXivPOB-ICMPOB-UNBP

research, evals, llm-apps· Added 30 Oct 2026, published 27 Oct 2026

arxiv.orgpaper🧠 AI

Perceiving and Predicting Dynamic Sports: Spatiotemporal Intelligence in Vision Models for Egocentric Tennis Video

This paper presents an annotated egocentric tennis dataset (egocentric video synced with five allocentric views, global positions, contact times, stroke types) and evaluates image encoders, video encoders and vision-language models on seven prediction tasks. Best models reach 0.14 m player and 0.54 m ball localization error and 0.14 s contact-time error. Depth input helps 3D ball localization, and longer histories do not help forecasting. A conditional diffusion model generates whole-body tennis motion in real time.

vision-language modeldiffusion modelTransformer
arXiv

research, multimodal, evals· Added 28 Oct 2026, published 27 Oct 2026

arxiv.orgpaper🧠 AI

FeatureArc: Benchmarking Coding Agents on Evolving Features

FeatureArc is a benchmark for coding agents on evolving features, built by a pipeline that mines repository histories into multi-stage tasks across four programming languages. It has 73 manually reviewed tasks of 2 to 10 stages, validated with fail-to-pass and pass-to-pass tests. Across 10 agents, pooled success drops from 62.7% at the first stage to 28.5% for the whole feature. Inherited defects appear in 72.9% of failed later stages.

LLM
FeatureArcTencentHunyuan+1

evals, coding-agents, research· Added 28 Oct 2026, published 27 Oct 2026

thedefiant.ioarticle🛡️ Security

SlowMist Finds Data-Stealing and Address-Swapping Code in Udesk Scripts

The Defiant reports SlowMist's finding of data-stealing and address-swapping code in scripts served by Udesk. The payload can change both displayed and copied crypto deposit addresses. Its data collection reaches beyond the three site-specific targeting rules. The page text did not load past the standfirst, so IOCs, affected sites and timeline are not available.

Udesk
SlowMist

Supply Chain, Threat Intel· Published and added 27 Oct 2026

arxiv.orgpaper🧠 AI

DCKV: Learned Transform Coding of the Prompt KV Cache in Hybrid Language Models

DCKV is a differentiable codec for the prompt KV cache of hybrid language models, which keep a KV cache only in their few full-attention layers. Transform, quantizer and entropy models (conditioned on prompt tokens and already decoded neighbouring positions) are trained end to end against a frozen model. On Qwen3.5-2B it stores a token in over 55 times fewer bytes than bf16 with at most 1.8 points lost on three long-context benchmarks, and over 100 times fewer with at most 3.6 points lost.

Qwen3.5TransformerLLM
DCKVarXiv

research, inference· Added 28 Oct 2026, published 27 Oct 2026

coindesk.comarticle🛡️ Security

Ethereum fixes Glamsterdam bug that could have sent staking deposits to builders

Glamsterdam deposit bug: Ethereum developers merged a consensus-spec fix on 26 October 2026 for a flaw in the upcoming upgrade. An operator could register as a builder under a public key, then a staking service's validator deposit under the same key would be credited to the builder, so no validator is created and funds are unrecoverable. Pending deposits are now sorted by withdrawal credentials. No losses were reported, and client teams still have to implement the change.

EthereumLidoRocket Pool
GlamsterdamavsetsinJustin Traglia+3

Blockchain· Published and added 27 Oct 2026

arxiv.orgpaper🛡️ Security

What Victims Take Away: Attack Reuse and Defense Revision after Prompt-Injection Attacks

What Victims Take Away is an October 2026 arXiv paper analysing 563,349 Tensor Trust attack records to see how players reuse prompt injections they received. Within a week, targets reused 10.0% of attacks that broke their defense, versus 0.03% of matched attacks. Breaches were followed by defense changes that cut same-family attack success by 24.5 points. Received texts made up 23.2% of successful hijacks. The authors propose that AI security tools describe techniques without disclosing full attack text.

Tensor TrustLLM
prompt injectionpayoff-biased social learningvictim-offender overlap

AI Security· Added 28 Oct 2026, published 27 Oct 2026

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