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“Abel”

securityweek.comarticle🛡️ Security

Chainalysis Discloses Data Breach Following AI-Driven Cyberattack

Chainalysis disclosed a data breach in which an external actor used offensive agentic techniques to access its systems and internal tools. Exposed data was non-sensitive customer data: user IDs, full names, email addresses and organization role labels. Investigation content, query results and credentials were not compromised. The company says the incident is unrelated to the $6.36 billion Binance heist, and advises users to enable MFA and watch for password reset prompts. Scope and victim count are undisclosed.

LLM

ChainalysisBinanceDPRK

Threat Intel, AI Security· Published and added 2 Nov 2026

en.cryptonomist.charticle🛡️ Security

Chainalysis security breach exposed customer data and raised phishing risk

Chainalysis confirmed on 30 October 2026 that an external actor accessed its systems and internal tools earlier in October, using what it called "offensive agentic techniques". Exposed data covers some customers' names, emails, job titles, user IDs and role labels. Chainalysis says it has found no access to passwords, session tokens or investigation content so far, and customers should expect targeted phishing. Affected-customer counts and the exploited vulnerabilities are not disclosed.

Chainalysisoffensive agentic techniquesphishing+1

Threat Intel· Published and added 31 Oct 2026

theblock.coarticle🛡️ Security

Chainalysis says hacker using ‘offensive agentic techniques’ accessed its customers’ names and emails

Chainalysis disclosed on 30 October 2026 that an attacker using "offensive agentic techniques" accessed customer names, emails, user IDs and role labels, but no investigation content, passwords or session tokens. The report also covers the Binance hack (about $6.3-6.4 billion, attributed to North Korea), where funds loop through addresses and JustLend lending on Tron is used to dodge stablecoin freezes. About $2.8 billion is frozen.

TronJustLend

ChainalysisBinanceDPRK

Threat Intel, Blockchain, AI Security· Published and added 31 Oct 2026

bleepingcomputer.comarticle🛡️ Security

Chainalysis discloses data breach from "offensive agentic techniques"

Chainalysis disclosed a security incident in which an external actor used "offensive agentic techniques" to access internal systems and tools earlier in the month. Exposed customer data includes user IDs, names, email addresses and organizational role labels. The company reports no evidence of access to investigation content, credentials or session tokens, and has shared no details on the vulnerabilities, tools or dwell time. The article also lists other incidents from the same week.

ChainalysisBinanceARTEX+3

Threat Intel, AI Security· Published and added 31 Oct 2026

microsoft.comwriteup🛡️ Security

Inside a malicious Claude Code-branded extension and its persistent SSH backdoor

Microsoft Threat Intelligence analyzes a tampered anthropic.claude-code extension (versions 2.1.312 and 2.1.313) served on the Cursor marketplace on 29 October 2026. The injected JavaScript loader adds an SSH key labeled svc-telemetry, opens a reverse SSH tunnel and sets up persistence on macOS, Linux and Windows. The post gives cleanup steps, IOCs and Defender XDR hunting queries. VS Code Marketplace and npm were not affected.

CursorClaude CodeSSH+3

Protocol Tunneling (T1572)reverse SSH tunnelMicrosoft Threat Intelligence+2

Supply Chain, Blue Team & Detection, Malware & RE· Published and added 31 Oct 2026

securityweek.comarticle🛡️ Security

Bulletproof Host Virtualine Loses Its Domain to FBI Seizure

Virtualine is a Russian-speaking bulletproof hosting provider whose domain virtualine.net the FBI seized. Court documents say it hosted phishing, spam, carding and illicit content, and ignored abuse reports. TRM Labs says it received more illicit-attributed cryptocurrency than any other labeled bulletproof host, mostly from a Russian drug marketplace (44%) and a sextortion scheme (17%), and later from StealC operators in 2026.

Bitcoin

VirtualineStealCFBI+3

Threat Intel· Published and added 30 Oct 2026

bleepingcomputer.comarticle🛡️ Security

Binance probes potential $5.8 billion crypto-theft hack

Binance is investigating a possible breach involving up to $5.8 billion in assets, which would exceed the $1.5 billion Bybit theft of February 2025. Tether froze over $1.567 billion in USDT and Circle over $800 million in USDC. Arkham tracks 55 addresses labelled "binance-hacker-1029" holding about $5.78 billion, but that is wallet holdings, not a confirmed theft amount. The cause is not yet known.

BinanceEthereumTether+2

BybitChangpeng ZhaoCircle+5

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

arxiv.orgpaper🧠 AI

VeriCUA: Verification as Agentic Exploration for Computer-Using Agents

VeriCUA is a platform-agnostic framework that verifies task completion by computer-using agents through agentic exploration. The verifier inspects context to build a task rubric, then picks GUI or code checks for unresolved requirements and combines findings under explicit evidence rules. It works online and offline across desktop, mobile and web, and agrees better with reference labels than the baselines, with the largest offline gains on hard cases.

vision-language modelVeriCUA

arXiv

agents, evals, research· Added 31 Oct 2026, published 29 Oct 2026

arxiv.orgpaper🧠 AI

On the Fundamental Limits of Prefill-Based Decoding Length Prediction

This paper derives bounds on predicting an LLM's decode length from prefill-phase hidden states, a primitive for inference scheduling. It models decoding as a stopped stochastic process, giving per-prompt normality and an affine variance law σ=αμ+b. Predictors (MLP, EGTP, FiLM, soft-label, Mixture-of-Experts) converge near the bound, so system error dominates model error. It proposes fixing model, data and sampling in benchmarks and reporting relative-error metrics.

LLMMixture of ExpertsMLP+2

arXiv

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

arxiv.orgpaper🧠 AI

Retrieving from Your Own Mistakes: On-Policy Training of Embedding Models for Multi-Hop Retrieval-Augmented Generation

On-policy state training is a method for training chain embedding retrievers for multi-hop RAG on the states the encoder itself reaches, including those built on its own wrong retrievals. Each state is labeled with the earliest missing gold passage, other missing gold passages are masked, and updates are contrastive and mixed with teacher-forced states. It retrieves complete evidence more often than teacher-forced training and graph-based systems, and transfers to harder long-chain HotpotQA and 2WikiMultiHopQA variants the authors built.

RAGon-policy state trainingcontrastive learning+1

HotpotQA2WikiMultiHopQAarXiv

research, llm-apps, training· Added 31 Oct 2026, published 29 Oct 2026

wiz.ioarticle🛡️ Security

The Rise of Agentic Attacks: What They Look Like, and Where They're Headed

Wiz Research reviews agentic attacks seen in its incident response work and in public reports (ARTEX, Hermes, JPCERT/CC, CrowdStrike, DIVD). It derives fingerprints of AI-agent activity from three intrusions: machine-paced iteration, natural-language reasoning in scripts, self-correcting script versions, checklist enumeration, noisy lateral spraying, probe-before-payload and self-labeled scratch files. It also covers effects on attribution, attack scale and defense.

KubernetesAWSClaude Code+2

ARTEXHermesStorm-0940+12

Threat Intel, Blue Team & Detection, AI Security, Cloud & Infra· Published and added 29 Oct 2026

arxiv.orgpaper🧠 AI

PCM: A Proof-Carrying Message Protocol for Verifiable Multi-Agent Communication

Proof-Carrying Messages (PCM) is a protocol where each claim an AI agent sends carries evidence: records from an append-only Merkle log the agent cannot write, with inclusion proofs, signed by an attesting signer. Receivers verify offline in about 1 ms and label each claim proven, unproven or failed. On 926 messages (712 lies in 16 classes), PCM accepts 10 lies (1.4%), all stale evidence; plain A2A accepts all 712.

A2AModel Context ProtocoluAgents+5

arXiv

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

arxiv.orgpaper🧠 AI

inkGround: Benchmarking Fine-grained Visual Grounding Capabilities of Vision-Language Models for Cursive Handwriting in the Wild

inkGround is a benchmark for stroke trajectory recovery from full-page, camera-captured handwriting, with 537 page images (about 18.7k words, 163k strokes) and pseudo-label references filtered by OCR agreement and human raters. Models get a page image and a target word, then output ordered stroke points. Seven VLMs were scored with DTW, Chamfer distance, readability and style similarity. Closed-source models, notably Gemini-3.1-Pro, did best, and the ranking held on IAM-OnDB.

vision-language model

inkGroundIAM-OnDBGemini-3.1-Pro+1

evals, multimodal, research· Added 30 Oct 2026, published 28 Oct 2026

x.comthread🧠 AI

John Rood (@johnroodepic) on X

Nemotron 3.5 Transcribe is an open-weight, promptable 30B mixture-of-experts model from NVIDIA with about 4B active parameters, built for recorded audio. One checkpoint transcribes 40 languages, handles recordings up to an hour, labels up to eight speakers, translates, and answers questions about English audio. Weights and a demo are on Hugging Face. The post gives no benchmarks.

Mixture of ExpertsHugging Face

NVIDIANemotronNemotron 3.5 Transcribe+1

models, multimodal· Added 29 Oct 2026, published 28 Oct 2026

akamai.comarticle🛡️ Security

TLP Is Not a Suggestion

Akamai SIG argues that vendors and researchers broke embargo norms during the October 2026 PHP2Shell leak, when the PHP fix shipped a day early on 21 October. It explains the four TLP 2.0 labels, tabulates past embargo breaks (Heartbleed, a collapsed six-month kernel-era embargo, the Alibaba Cloud and MIIT case), and lists seven practices, such as no proofs of concept or CVE numbers before the fix.

PHP

CVE-2026-111497PHP2ShellTraffic Light Protocol+11

other· Added 30 Oct 2026, published 28 Oct 2026

arxiv.orgpaper🛡️ Security

Structured Security Explanations for Learning-Based Vulnerability Detection

VulnLens is a framework for binary C/C++ function-level vulnerability detection. A label-blind LLM Explainer describes each function from seven fixed perspectives (data flow, memory access, object lifetime, arithmetic and type behavior, control flow, shared state, external-resource access). A supervised Detector fuses code and explanations by cross-attention. It reaches 75.5% accuracy and 77.2% F1 on a 526-function PrimeVul test set, and 66.4% accuracy on SecVulEval without retraining.

LLMCC+++2

VulnLensPrimeVulSecVulEval

AI Security, AppSec· Added 30 Oct 2026, published 27 Oct 2026

arxiv.orgpaper🧠 AI

Exploring the Limits of the Delta Learning Hypothesis: Single-Pair Generator Biases in Direct Preference Optimization

This paper tests the delta-learning hypothesis for Direct Preference Optimization (DPO), where the larger model's response in a single model pair is treated as preferred. It shows, in theory and experiments, that fixed pairs add generator-specific biases that predict the preference label regardless of quality. DPO on single-pair data underperforms multi-model data, consistent with reward hacking, and adding more generators partly fixes it without an external judge.

LLM

arXiv

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

en.cryptonomist.charticle🛡️ Security

Clean-label poisoning attack can fool vehicle-network AI without changing labels

Clean-label poisoning attack on federated learning for Basic Safety Message anomaly detection in vehicular networks, from Sagduyu, Erpek, Stephenson and Xue (paper submitted 23 October 2026). The attack perturbs benign samples toward malicious feature extremes without changing labels, which lowers class separability, and it grows stronger with more compromised nodes. A feature-consistency relabeling defense keeps attack success low without hurting clean accuracy, and a game-theoretic model shifts equilibria against attacking.

Basic Safety Messageclean-label poisoningfeature-consistency relabeling+4

AI Security· Published and added 26 Oct 2026

mcpmarket.comtool🧠 AI

Search Search results for "security" | MCP Market

MCP Market search results for "security" in its skills category, listing about 20 installable AI agent skills. Entries cover OWASP Top 10 audits, secure PHP coding, STRIDE threat modeling, CVE remediation, security header analysis, misconfiguration finding, bug bounty triage and AgentShield scans of Claude Code configurations. One is a paid D365FO security-role XML skill. Each entry has only a one-line description and a category label.

Claude CodeAgentShieldPHP+1

MCP MarketOWASPSTRIDE

coding-agents, prompting· Added 25 Oct 2026

mcpmarket.comother🛡️ Security

Search Search results for "security" | MCP Market

MCP Market search results for "security" in the skills category, listing about 20 installable AI agent skills. Entries cover OWASP Top 10 audits, secure coding for PHP, STRIDE threat modeling, CVE remediation, security header analysis, misconfiguration finding, bug bounty triage and AgentShield scans of Claude Code configurations. One is a paid D365FO security-role XML skill. Each entry has only a one-line description and a category label.

Claude CodeModel Context ProtocolPHP

OWASPSTRIDEAgentShield+1

AppSec, AI Security· Added 25 Oct 2026

arxiv.orgpaper🧠 AI

Detecting and Understanding Ambiguity in Visualizations with Role-Playing Vision Language Model Interpretations

This paper presents a pipeline that uses persona-conditioned vision language models to surface interpretive ambiguity in visualizations. Topic-relevant personas elicit candidate takeaways, and a Target-Pattern-Scope framework lets LLM judges label statement pairs as Compatible, Disjoint or Opposing, which are summarized in an ambiguity profile. The authors benchmark VLMs on chart comprehension per chart type, check labeling against human annotations, and run case studies on public visualizations and a redesign.

vision-language modelLLM

arXiv

research, multimodal, evals· Added 27 Oct 2026, published 25 Oct 2026

arxiv.orgpaper🧠 AI

TriageRAG: Failure-Aware Self-Training for Adapting Retrieval-Augmented Language Models to Specialized Domains

TriageRAG is a failure-aware self-training method for adapting retrieval-augmented LLMs to specialized domains using only an unlabeled corpus. It probes the student on self-generated questions with and without retrieved context, sorts them into known, solved, context-failure and retrieval-failure, and routes each to a matching training signal. With Qwen3-4B on 11 datasets it beats budget-matched SimRAG by 1.2-6.4 points and recovers closed-book accuracy where retrieval hurts.

RAGLLMQwen3

TriageRAGSimRAGCS-Bench+1

research, llm-apps, training· Added 27 Oct 2026, published 24 Oct 2026

en.cryptonomist.charticle🛡️ Security

Bitget hacker fund movements top $78.01 million in transfers to new addresses

Bitget breach tracking report: Arkham recorded $19.48 million moved in 52 transfers between 21 and 24 October 2026, and $78.01 million to new addresses from 15 to 23 October. About $242.54 million remains in labeled holdings. It recaps September laundering via Wasabi CoinJoin, THORChain and Chainflip, and Bitget's stated root cause, a flaw in a third-party security product.

BitcoinEthereumTHORChain+3

BitgetArkhamAMLBot+5

Blockchain, Threat Intel· Published and added 24 Oct 2026

arxiv.orgpaper🧠 AI

VulDec: Vulnerability Detection in Binaries via Neural Decompilation

VulDec is a dataset and framework for detecting vulnerabilities in binaries by first decompiling them with fine-tuned LLMs. The dataset aligns real-world Assembly-C function pairs across several architectures with CWE labels. Tuned neural decompilers reach about 56% Edit Similarity and 43% BLEU-4, and tuned detectors reach a macro F1 of 0.61 on a realistic test set with a 3% vulnerability rate.

LLM

VulDecCWE

research, training· Added 28 Oct 2026, published 24 Oct 2026

arxiv.orgpaper🛡️ Security

VulDec: Vulnerability Detection in Binaries via Neural Decompilation

VulDec is an arXiv paper (October 2026) presenting a dataset of real-world Assembly-C function pairs with CWE labels across multiple architectures, plus an end-to-end framework that uses neural decompilation for binary vulnerability detection. Tuned LLM decompilers reach about 56% Edit Similarity and 43% BLEU-4. Tuned LLM detectors reach macro F1 of 0.61 on a realistic test set with a 3% vulnerability rate.

LLM

VulDec

Malware & RE, AI Security· Added 28 Oct 2026, published 24 Oct 2026

eprint.iacr.orgpaper🛡️ Security

Extraction meets Distillation - cryptanalytic techniques to recover parameters from a Deep Neural Network

Extraction meets Distillation is an IACR ePrint paper (2026/2628, CSCML 2026) that combines cryptanalytic weight-direction extraction with label-based distillation to reconstruct fully connected neural networks. It adapts a ReLU extraction implementation to Leaky ReLU and proves conditional bounds for direction recovery and sign selection. On CIFAR-10 victims, reconstructed models reach 56.40% and 58.29% agreement. It separates label requests from privileged access and notes that prediction agreement does not identify parameters.

ReLULeaky ReLU

CSCML 2026CIFAR-10

AI Security· Added 26 Oct 2026, published 24 Oct 2026

decrypt.coarticle🛡️ Security

Bitcoin Core Rewrites Its Security Policy for a World Where AI Finds Bugs in One Prompt

Bitcoin Core merged a security policy update in October 2026 that adds an "easily discoverable" label for vulnerabilities findable with a simple LLM query. Such bugs get a fix backported to all maintained releases and a new version shipped fast with an advisory, instead of a long embargo. Antoine Poinsot of Chaincode Labs proposed the change after a spike in reports. The article also recaps the Bitcoin Red Team and AI-found Lightning and wallet bugs.

BitcoinLLM

Antoine PoinsotChaincode LabsBitcoin Red Team+1

AI Security, GRC· Published and added 23 Oct 2026

arxiv.orgpaper🧠 AI

OWLS: Open-world Language-conditioned Speed Fields for Zero-Shot Rover Navigation

OWLS (Open-world Language-conditioned Speed Fields) plans directly over a vision-language model's dense feature map. A text prompt describing traversable terrain scores every pixel of one camera image. The scores are projected onto the ground, adapted with two morphological operations, and used as the speed field of a minimum-time planner. It needs no training, terrain labels, map or extra sensors. It was tested on a physical rover, on simulated Mars and Moon rovers over 200 procedural terrains, and on real planetary imagery.

vision-language model

OWLSarXiv

research, multimodal· Added 26 Oct 2026, published 23 Oct 2026

eprint.iacr.orgpaper🛡️ Security

For Thirty Pieces of Silver: Overcoming Non-IID via ZKP Sample-Level Auditing and Forcing Attackers into Traitors in Federated Learning

Preprint (IACR ePrint 2026/2611) proposing a federated learning defense that replaces gradient detection with zero-knowledge-proof sample-level audits of client datasets. A staking mechanism forces attackers to train on discovered poisoned samples with flipped labels, producing counter-gradients that remove the backdoor without knowing the trigger. ZKP commitments lock the training data, which blocks adaptive attacks and gradient tampering. On CIFAR-10 under Dir(0.1) non-IID, it holds attack success rate below 6% against distributed backdoor and edge-case attacks.

distributed backdoor attackedge-case attackadaptive attack+3

AI Security· Added 24 Oct 2026, published 23 Oct 2026

x.comadvisory🛡️ Security

lee1981 (@lee1981b) on X

CVE-2026-111497 is an out-of-bounds write in the Brotli decoder bundled with Node.js (CWE-787, CWE-252), rated CVSS v4.0 9.2 by the Google CNA and High by the Node.js project. Fixes are Node 22.23.4, 24.22.1 and 26.12.1, and 24.22.1 still labels Brotli 1.2.0 because the fix was cherry-picked. The post gives a SOC trail from input to worker, with remediation and verification steps. It notes no KEV match, CISA SSVC exploitation none, and only unverified public lab claims.

Node.jsBrotliCVSS+3

CVE-2026-111497CWE-787CWE-252+3

Blue Team & Detection, AppSec· Added 26 Oct 2026, published 23 Oct 2026

arxiv.orgpaper🛡️ Security

Agentic AI Defense: Decode-Time Provenance Binding for MCP

Decode-time provenance binding is a defense for Model Context Protocol agent pipelines against cross-modal trust laundering, where instructions hidden in an email-signature image via LSB, DCT or metadata steganography get decoded by a tool. It combines HMAC-authenticated trust labels, a provenance ledger and context-taint tracking to block critical tool calls. On a real MCP testbed with 24 adaptive payloads, attack success fell from 100% undefended (62.5% for keyword and regex filters) to 0%.

Model Context Protocol

indirect prompt injectioncross-modal trust launderingsteganography+5

AI Security· Added 26 Oct 2026, published 22 Oct 2026

dfir.chwriteup🛡️ Security

Field Notes: Reconstructing an Attacker’s Workflow

Field note from dfir.ch on PowerShell scripts an attacker left on disk during an incident: a file-watching command runner using Invoke-Expression, a fake Windows credential prompt that validates passwords via Start-Process and logs them in cleartext, AD computer enumeration with WMI access testing, and SMB share discovery via NetShareEnum. It notes where script labels overstate what they prove and lists hunting strings for Event IDs 4104, 4103, 4688 and Sysmon 1.

PowerShellActive DirectoryWMI+2

TerminalFixClickFixMicrosoft+7

Blue Team & Detection, Offensive Security· Added 24 Oct 2026, published 22 Oct 2026

deepmind.googlearticle🧠 AI

Beyond tree cover: mapping the world’s forest types

Forest Typology (ForTy) 2020 is a global 10-meter map from Google DeepMind, Google Research, WRI and IIASA. It sorts land into six FAO-based classes: primary, naturally regenerating, planted, plantation, tree crops and agroforestry, and other land. The model uses AlphaEarth Foundations embeddings and about 1.7 million labeled locations. It was 90% accurate on forest type across 9,000+ expert-checked sites. An early 2017 to 2025 time series is on Earth Engine.

Google Earth EngineAlphaEarth Foundations

Google DeepMindGoogle ResearchWorld Resources Institute+7

models, multimodal· Published and added 22 Oct 2026

arxiv.orgpaper🛡️ Security

SecLog-Bench: A Scalable Benchmark for Cybersecurity Log Reasoning with Scaffolded Evidence

SecLog-Bench is a benchmark for LLM reasoning over cybersecurity logs, built on an extensible simulation platform covering 29 attack scenarios. It combines audit logs, application logs and network traffic, with attack-stage labels and step-by-step evidence chains. Eight LLMs were tested on post-detection attack-progress reasoning: the best reached 44.44% accuracy, 45.07% Macro-F1 and 54.87% Stage Reasoning F1.

LLM

SecLog-BenchStage Reasoning F1

AI Security, Blue Team & Detection· Added 23 Oct 2026, published 22 Oct 2026

arxiv.orgpaper🧠 AI

SecLog-Bench: A Scalable Benchmark for Cybersecurity Log Reasoning with Scaffolded Evidence

SecLog-Bench is a benchmark for LLM reasoning over cybersecurity logs. A simulation platform with 29 attack scenarios generates audit logs, application logs and network traffic, with attack-stage labels and step-by-step evidence chains. Eight mainstream LLMs were tested on attack-progress reasoning: the best reached 44.44% accuracy, 45.07% Macro-F1 and 54.87% Stage Reasoning F1. Correct predictions often came with flawed reasoning.

LLM

SecLog-BencharXiv

evals, research· Added 23 Oct 2026, published 22 Oct 2026

eprint.iacr.orgpaper🛡️ Security

A Spectral Theory of Local-to-Global Leakage

A Spectral Theory of Local-to-Global Leakage is an IACR ePrint paper (2026/2571) by Rei Ueno on additive secret sharing over finite abelian groups. It gives an exact leakage decomposition into local leakage magnitude and spectral alignment across shares. Two channels with identical local mutual information can have exponentially different global leakage. Automorphism-weighted sharing can cancel leakage perfectly with finitely many shares, which ordinary additive sharing cannot do.

Rei UenoMöbius inversion

Hardware & RF· Added 21 Oct 2026, published 20 Oct 2026

eprint.iacr.orgpaper🛡️ Security

Post-quantum commutative group actions from oriented superspecial abelian varieties

Castryck, Dina, Kutas and Lorenzon study the Shimura class group action on superspecial principally polarized abelian varieties with a CM orientation, as a post-quantum commutative group action for key exchange. They give tools to represent CM orientations through higher-dimensional isogenies and to compute the action in the restricted effective group action model. These rely on a CM version of Kani's lemma and a trick that often halves the ambient dimension. The paper also gives a method for finding oriented superspecial abelian surfaces and analyses obstructions to transitivity.

Shimura class group actionKani's lemmaIbukiyama-Katsura-Oort correspondence+5

other· Added 20 Oct 2026, published 19 Oct 2026

arxiv.orgpaper🧠 AI

EpiLens: Lightweight RAG for Epidemiological Knowledge Acquisition

EpiLens is a lightweight, open-source retrieval-augmented generation package that applies a fixed retrieval template and output schema to every paper in a corpus and links outputs to source passages. On a corpus of 2,297 epidemiology papers, its classifier matches or exceeds supervised and zero-shot baselines on four tasks without task-specific training data, scored against 214 manual labels. It also compares model-based study shares across parameters and pathogens.

RAGLLM

EpiLensSARS-CoV-2mpox+1

llm-apps, research· Added 21 Oct 2026, published 19 Oct 2026

arxiv.orgpaper🧠 AI

SHADE: Shrinkage via Halted Alpha-stable Denoising for Heavy-Tailed Probabilistic Time Series Forecasting

SHADE is an alpha-stable Lévy diffusion forecaster that stops denoising at a nonzero depth, so the leftover stable kernel shrinks the predictive distribution instead of leaving it overconfident. It offers a scale-calibrated halt and a context-amortized halt that needs no noise labels. Median CRPS improves 21.7% on synthetic data with alpha at or below 1.3, with tail calibration gains on cryptocurrency and foreign-exchange series.

diffusion model

arXiv

research· Added 21 Oct 2026, published 19 Oct 2026

eprint.iacr.orgpaper🛡️ Security

Blind-DLPA: An End-to-End Framework for Profiled Blind Side-Channel Analysis

Blind-DLPA is a multi-task deep-learning framework for profiled blind side-channel analysis, where the attacker has labeled profiling traces but only leakage traces from the target, with no plaintexts or ciphertexts. It estimates Hamming-weight posteriors of an input and a key-dependent intermediate, then ranks key hypotheses against theoretical joint distributions. It recovers keys on unprotected AES and Kyber and on ASCAD (guessing entropy zero with 39 traces), and the authors report collisions in a prior Ascon Hamming-weight model.

AESKyberAscon

Blind-DLPAASCADprofiled blind side-channel analysis+4

Hardware & RF· Added 20 Oct 2026, published 19 Oct 2026

eprint.iacr.orgpaper🛡️ Security

Computing endomorphisms of superspecial principally polarized abelian surfaces in time and memory $p^{4/5+o(1)}$

Computing endomorphisms of superspecial principally polarized abelian surfaces is a cryptanalysis preprint by Marc Houben and Lam L. Pham, posted to IACR ePrint in October 2026. It gives an algorithm that, under a standard heuristic, finds a non-scalar polarized endomorphism in expected time and memory p^(4/5+o(1)). The abstract is the only text available, so the method and its effect on specific schemes are not described.

Marc HoubenLam L. Pham

other· Added 19 Oct 2026, published 18 Oct 2026

eprint.iacr.orgpaper🛡️ Security

Superspecial points on Humbert surfaces and the probability of (N,N)-splitting

Determines, to first order in 1/p with an explicit error bound, the proportion of superspecial principally polarized abelian surfaces over F_p-bar (p ≡ 3 mod 4) that are optimally (N,N)-split. The expected number of optimal splittings is exactly 5N·J_2(N)(p-1)/(p²+1), so the split proportion is about 5N·J_2(N)/p. This gives the stationary target density of the splitting-detection attack on the dimension-two superspecial isogeny problem, which was previously known only from experiments or a one-sided bound. The proofs use Siegel's mass formula and genus averages over refined Humbert invariants, and every formula was checked by exact computation at twelve primes.

splitting-detection attackRichelot random walkHumbert surfaces+3

other· Published and added 17 Oct 2026

x.comthread🧠 AI

Bikash | Hermes Super-Intel (@BkashJosi) on X

Post by Bikash (@BkashJosi) ranking open GitHub issues labelled type/feature for the Hermes agent framework by thumbs-up reactions. The query returned 5,349 issues and only one has more than 50 votes. The top three are Claude Agent SDK with subscription OAuth (57), remote Hermes with local tool execution (41) and Mistral provider support (32), followed by backups, configuration sync and context reduction. The full top 10 is only in the video.

HermesClaude Agent SDKOAuth+2

Nous Research

agents· Added 20 Oct 2026, published 17 Oct 2026

arxiv.orgpaper🧠 AI

FedRAPP: Federated Test-Time Prompt Tuning for Vision-Language Models with Reliability-Aware Prompt Personalization

FedRAPP is a personalized aggregation framework for Source-Free Federated Test-Time Prompt Tuning (SF-FTTPT), where clients adapt CLIP soft prompts on unlabeled, non-IID test streams without source data. It combines reliability-aware client assessment, a server-side exponential moving-average prompt memory and similarity-guided peer aggregation. Across nine CLIP benchmark datasets and ImageNet variants it beats standard federated aggregation baselines while keeping prompt-only efficiency.

CLIPvision-language model

FedRAPPImageNetarXiv

research, multimodal· Added 20 Oct 2026, published 16 Oct 2026

arxiv.orgpaper🛡️ Security

Ignored, Not Refused: What Low Prompt-Injection Success Rates Hide in Small Tool-Using Agents

Ignored, Not Refused audits 33,000 trajectories from eleven open-weight agents (2-14B parameters) on InjecAgent and AgentDojo. A label-blind LLM judge finds most failed indirect prompt injections are ignored (76-80%) rather than refused (17-18%). Changing only the system prompt moves InjecAgent ASR from 0.2% to 43%. The Attack-Task Outcome Matrix (ATOM) shows 21.7% of AgentDojo episodes were safe but left the user task incomplete. The corpus, judge prompt and code are released.

LLM

indirect prompt injectionAgentDojoInjecAgent+2

AI Security· Added 20 Oct 2026, published 16 Oct 2026

arxiv.orgpaper🧠 AI

Ignored, Not Refused: What Low Prompt-Injection Success Rates Hide in Small Tool-Using Agents

"Ignored, Not Refused" audits 33,000 trajectories from eleven open-weight agents (2-14B parameters) on InjecAgent and AgentDojo. A label-blind LLM judge finds 76-80% of failed injection attacks were ignored and 17-18% refused. Changing only the system prompt moves InjecAgent attack success rate from 0.2% to 43%, and the ATOM matrix shows 21.7% of AgentDojo episodes were safe but left the user's task undone. The corpus, judge prompt and code are released.

LLM

InjecAgentAgentDojoATOM+1

evals, ai-safety, agents· Added 20 Oct 2026, published 16 Oct 2026

endorlabs.comarticle🛡️ Security

PWN Request Threat: A Hidden Danger in GitHub Actions | Endor Labs

Explains the "pwn request" weakness in GitHub Actions. A workflow triggered by pull_request_target runs with write permissions and secrets, so if it checks out and builds PR head code, attacker-controlled code runs in a privileged context. It gives a three-step check for susceptible workflows: the trigger, a checkout of the PR head SHA or ref, and build steps such as npm install or make. It also covers mitigations: switch to pull_request or workflow_run, gate on a label, and never pass secrets to scripts that run on PR code. It notes safe patterns and cites the puppetlabs finding.

GitHub ActionsGitHub

pwn requestpuppetlabs

DevSecOps· Published and added 16 Oct 2026

arxiv.orgpaper🧠 AI

Knowledge That Is Linear Before Fine-Tuning Generalizes After It

The paper separates "active" facts (which support generalization beyond recall) from "inert" ones. It builds new relations by relabeling answers of existing ones (Paris -> Europe becomes Paris -> crab), fine-tunes on a subset of subjects, and tests held-out subjects that share an answer (Munich -> crab). Across eleven language models and 70 relations, linear readability of the original relation in pretrained representations correlates with this generalization at Pearson r = 0.87 to 0.92 within each model. The result holds after controlling for fine-tuning set size, original-relation accuracy and subject frequency in pretraining text, and it survives re-randomized labels, seeds and thirteen other label categories. Mapping wrong labels back to the original answers shows the fine-tuned model's errors follow the linear probe's top answers.

LLM

arXiv

research, training· Added 17 Oct 2026, published 15 Oct 2026

arxiv.orgpaper🧠 AI

GraphACM: Graph-based Action Consistency Measurement for Uncertainty Quantification in LLM Agents

GraphACM merges equivalent states from multiple rollouts of the same LLM agent task into a directed, action-labeled graph, so actions can be compared at shared decision states. It defines node-level and global action-consistency measures, and uses the global score as an uncertainty proxy to predict task success or failure. Across three LLMs on ALFWorld, AppWorld and WebShop it reaches an average AUROC of 0.811, above the strongest behavioral-consistency baseline (0.729) and verbalized confidence (0.769). It also shows where in a trajectory agent actions diverge.

LLM

GraphACMALFWorldAppWorld+2

research, agents, evals· Added 17 Oct 2026, published 15 Oct 2026

arxiv.orgpaper🛡️ Security

Approve What, Exactly? Effect Binding and Trusted Rendering for Agent Step-Up Authorization

Paper arguing that agent human-approval prompts count as a second factor only if agent compromise cannot forge them and they name exactly what is approved. It defines twelve attacks on the approval path (decoy recipients, amount cancellation, card padding, label spoofing, enrollment squatting, threshold splitting) and tests them against AP2, Microsoft's action-bound approval design and MCP elicitation. An audit of ten agent runtimes found no third-party verifiable approval record and post-approval argument changes in four of eight. The reference defense ASU (Agent Step-Up) renders the card from a canonical effect descriptor, has a phone passkey sign its digest, and executes only the stored call. It is proven in Tamarin (53 lemmas) and evaluated on 27,108 scripted trials and 609 AgentDojo pairs. The trusted card cut forged-narration attack success from 0.54 to 0.03 and raised LLM-approver catch rate from 0.30 to 0.98.

Model Context ProtocolMicrosoft

Agent Payments ProtocolAgent Step-UpASU+6

AI Security, Identity & Access· Added 17 Oct 2026, published 15 Oct 2026

github.blogarticle🧠 AI

GitHub’s model for secret detection: Finding more secrets with less noise

GitHub describes replacing an LLM-based generic secret detection pipeline with a two-stage system. High-recall regex generates candidates, then a fine-tuned ModernBERT classifier scores each one using its file path, secret type, variable names and surrounding context. Training data mixes product feedback, weak-labelled regex candidates and synthetic hard negatives, with splits grouped by repository, value or example. The model is exported to ONNX with 8-bit quantization. Reported results against the LLM pipeline: 13% higher precision, 19% higher recall, 11.5% higher owner-weighted acceptance in an A/B test, 86% lower median post-push scan time and over 98% lower serving cost. It is a concrete case for using a small fine-tuned encoder instead of an LLM on a narrow, measurable classification task.

ModernBERTONNXGitHub

Microsoft

training, inference· Published and added 13 Oct 2026

github.blogarticle🛡️ Security

GitHub’s model for secret detection: Finding more secrets with less noise

GitHub describes its secret scanning pipeline for generic secrets. Regex finds candidates with high recall, then a fine-tuned ModernBERT classifier reads the candidate with its file path, secret type and surrounding code to filter hard negatives such as placeholders, fixtures and checksums. The post covers the training data (product feedback, weak labels, synthetic hard negatives), grouped train/eval splits, and ONNX 8-bit quantization. Reported results against the earlier LLM pipeline: 13% higher precision, 19% higher recall, 11.5% higher owner-weighted acceptance, 86% lower median scan time and over 98% lower serving cost. The speed gain made generic secret detection feasible in push protection.

GitHubModernBERTONNX+1

push protectionhard negatives

DevSecOps, AI Security· Published and added 13 Oct 2026

arxiv.orgpaper🧠 AI

Evidence-First Agentic Verification

Introduces Sauron, an evidence-first agentic verifier that checks rubric criteria for long-horizon agent tasks by investigating both environment state and execution trace, and trusts a claim (including an agent's self-reported success) only after validating it against its source. Bounded queries and joint investigation of related criteria cut context use, and each verdict carries an auditable evidence receipt. On 2,586 human-labeled criteria from 100 personal-assistant episodes it has the best F1 of five verifiers across eight backbones (0.96 vs 0.92 for Gandalf with GPT-5.6 Sol, at 23% lower cost). It detects 97% of injected failures across 13 fault families, including reward-hacking simulations, with 2% false flags on clean controls, and leads on the TRAIL agent-error benchmark. It matters because unreliable rubric verdicts used as RL rewards can reinforce bad behavior.

SauronGandalfTRAIL+2

evals, agents, research· Added 14 Oct 2026, published 13 Oct 2026

arxiv.orgpaper🧠 AI

Hide and Seek: HIDE Concepts to SEEK explanations for black box models

Hide and Seek (HnS) is a concept-level attribution method for black-box vision models. It needs no model weights and no concept-labeled datasets. Given a concept and an image set, it uses a vision-language model to retrieve concept-aligned images and localize the concept, masks the localized region, and takes attribution from the change in the black-box model's output. It agreed better with ground-truth attributions than existing methods on CelebA and COCO-Stuff, and it works on tasks existing concept attribution methods cannot handle, such as image captioning and satellite image segmentation.

vision-language model

Hide and SeekHnSCelebA+1

research, multimodal· Added 12 Oct 2026, published 9 Oct 2026

arxiv.orgpaper🧠 AI

BLiMPPI: Benchmark of Linguistic Minimal Pairs for Polarity Items

BLiMPPI is a multilingual evaluation suite of over 12,000 negative polarity item (NPI) minimal pairs across 33 languages. A pipeline finds NPIs in Universal Dependencies treebanks, labels their licensing environments, and perturbs them into grammatical/ungrammatical pairs. Across 12 multilingual and 2 monolingual models plus n-gram baselines, multilingual models do worst on lower-resource languages and on pairs that n-grams cannot solve. The paper also analyses NPI typology, language family, licensing variability and NPI-licenser distance.

Universal Dependenciesn-gram

BLiMPPI

evals, research· Added 12 Oct 2026, published 9 Oct 2026

en.cryptonomist.charticle🛡️ Security

Typed decision model vulnerabilities can push AI guardrail fail-open rates to 63%

A study by Erfan Baghaei Potraghloo, posted to arXiv on October 8, 2026, evaluates seven open-weight LLMs used as "typed decision model" guardrails in agent systems (assigning probabilities to caller-defined allow/block options rather than generating text). It finds that six lines of unrelated server-log text raised one model's fail-open rate from 0% to 63%, and that merely renaming the permissive option's label (without changing its definition or the assessed text) drove fail-open rates to 93-100% in four models; a deterministic rule on typed values instead hit 100% accuracy, leading the author to recommend using these models only to triage reviewer workload rather than as sole decision-makers.

LLM

Erfan Baghaei Potraghloo

AI Security· Published and added 9 Oct 2026

github.comrepo🧠 AI

Show HN: Let your AI agents paint big arrows, boxes and text on your screen

bigarrow is an MIT-licensed, single-binary Swift macOS CLI that draws a click-through arrow and sign over all windows so an AI agent can show a human where to click, paste or act (permission prompts, 2FA, OAuth consent). It ships an Agent Skills package (installed via `bigarrow install-skill`) for Claude Code and Codex, with JSON output and exit codes, and can target by Accessibility label, coordinates, window or Peekaboo snapshot. The README documents the skill's token cost (182 tokens for the description, 1,398 for the instructions) and 104 automated tests.

SwiftmacOSClaude Code+6

bigarrowNameplatePeter Steinberger+2

coding-agents, prompting, agents· Published and added 9 Oct 2026

arxiv.orgpaper🧠 AI

SWE-Critix: Post-Training Generalist Verifiers for Assessing Coding-Agent Trajectories

SWE-Critix is a post-training pipeline that trains generalist verifiers for coding-agent trajectories. Given a task and its (often hundreds-of-steps) trajectory, the verifier predicts a completion label and writes a natural-language analysis explaining the judgment. Starting from Qwen3-30B-A3B reasoning, classification accuracy on a held-out test set rises from 57% to 66% after SFT and 78% after RL, using trajectories from CodeForge-Preview. Models, datasets and training code are released.

Qwen3Mixture of Experts

SWE-CritixCodeForge-PreviewQwen3-30B-A3B+1

research, coding-agents, training, evals· Added 12 Oct 2026, published 9 Oct 2026

arxiv.orgpaper🛡️ Security

Understanding Verification Vulnerabilities in Cross-chain Bridges: An Empirical Study

First systematic empirical study of Cross-chain Verification Vulnerabilities (CVVs) in bridge smart contracts, such as flawed validation of state proofs, message sender identities and signatures. From 588 audit reports across 36 bridge projects it derives 11 CVV patterns in 5 categories and builds a dataset of 830 instances across 786 contract files, mapped to reports, source versions and pattern labels. Existing tools cover few CVV patterns, and only two of four tested LLMs beat a 29.3% Macro-F1 constant baseline (best 51.2%), which motivates detectors built for cross-chain verification semantics.

LLM

Cross-chain Verification VulnerabilitiesCVV

Blockchain· Added 12 Oct 2026, published 9 Oct 2026

eprint.iacr.orgpaper🛡️ Security

RM-KLPT: Algebraic Pathfinding for Superspecial Abelian Surfaces with Maximal Real Multiplication

The paper presents RM-KLPT, an algorithm that solves the algebraic pathfinding problem in isogeny graphs of superspecial abelian surfaces with maximal real multiplication. It does this by solving a norm equation in ideals of a quaternion algebra over a real quadratic field. The authors implement it and revisit the RM-CGL hash function of Charles, Goren and Lauter. They show that if the initial surface's algebraic description is known, RM-KLPT gives a second-preimage attack on the hash. Paths are much shorter (~p^3) than with KLPT^2, because a higher-dimensional Deuring correspondence exists in the RM setting. The paper also discusses cryptographic constructions that could be built on RM-KLPT.

RM-KLPTRM-CGLKLPT+4

other· Added 12 Oct 2026, published 9 Oct 2026

decrypt.coarticle🛡️ Security

Will AI Break Crypto Encryption? Ethereum’s Vitalik Buterin Weighs In on 'Bunker Mode' Shift

Decrypt reports on Ethereum co-founder Vitalik Buterin's warning that AI-driven advances in mathematics could plausibly break lattice-based cryptography within two years, a day after Ethereum Foundation researcher Justin Drake urged the industry to adopt "bunker mode" — moving funds to addresses whose public keys have never been exposed. The piece covers pushback on reusing public keys, the tradeoff between quantum-resistant lattice schemes (used by projects like Cardano) and their potential AI-math vulnerability, and Buterin's preference shift toward hash-based cryptography, with commentary from BIP-360 co-author Isabel Foxen Duke and Charlie Quant Lab's Ananda Banerjee urging calm preparation over panic.

EthereumCardano

Vitalik ButerinIsabel Foxen DukeAnanda Banerjee+7

Blockchain· Published and added 8 Oct 2026

arxiv.orgpaper🧠 AI

Who Verifies the Verifier? Co-Evolving Inspectable Graders with Self-Improving Agents

Proposes making the verifier the evolving object in self-improving agent loops. The verifier is an inspectable expression over small, mostly deterministic drawback detectors, synthesized from clustered failures, gated at birth, and selected for agreement with a ten-item anchored reference set plus consensus over unlabeled outputs, never for the agent's score. On MBPP+ it gains +0.21 held-out agreement over the hand-authored seed and ends ahead of the bare LLM judge it contains. Removing the anchor guards collapses the verifier into an always-pass grader that still trains skills as well, so downstream task score cannot certify a self-evolved verifier. The paper's Double Ratchet pairs the verifier with a lifecycle-managed skill loop and retains 88-110% of the lift from ground truth or a rubric on code generation, enterprise text-to-SQL and reference-free report generation.

MBPP+

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

twitter.comthread🧠 AI

OpenAI withdraws three mathematical results

A tweet from Dan Roberts announcing an update to a GitHub repository tracking Lean formal verification of mathematical results tied to OpenAI math research, adding six new Lean formalizations and nineteen modifications, now covering roughly 42 percent of top line results. The update also discloses withdrawal of three manuscripts, on algebraicity of Weil classes on split abelian eightfolds, Kuga Satake correspondences for K3 surfaces, and the rational Hodge conjecture for products of K3 surfaces, after formalization work revealed errors.

LeanGitHub

OpenAIDan Roberts

research· Published and added 8 Oct 2026

fiveminutesforward.comwriteup🧠 AI

Write Like It's 1866: LLMs Relearn Telegraphese

The author revives "cablese," the terse telegraph-era writing style, as an LLM prompting technique: instructing a model to write dense, article-dropping summaries cuts output tokens 25–49% depending on model family, while downstream models (including ones never shown the instruction) answer comprehension questions from the compressed text at parity or better than from plaintext. The piece introduces the "Telegraph Test," an open benchmark (github.com/Travis42/telegraph-test) with ~1,300 questions over 50 passages and a fixed deterministic grader, used to measure compression ratio, cross-model legibility, and read-back/decode costs for GLM-5.3-Flash, Gemma, Qwen and gpt-5-mini; it also contrasts this human-readable, auditable register against emergent machine-only compression protocols described in other papers (BabelTele, GLOSSOGEN) and existing tools (Terse, Caveman).

GLMGemmaQwen+1

GLM-5.3-Flashgpt-5-miniZ.ai+4

prompting, evals, ai-infra· Published and added 7 Oct 2026

x.comthread🛡️ Security

DevopsCube (@devopscube) on X

A tweet highlighting Steampipe, an open-source tool that lets practitioners query cloud infrastructure, Kubernetes, and other services using SQL instead of jumping between consoles or writing ad-hoc scripts. It calls out concrete security/ops use cases such as finding publicly accessible storage buckets, resources missing required labels, and Kubernetes namespaces without NetworkPolicies, and links to a longer tutorial on devopscube.com. The author frames it as a quick investigation layer on top of live cloud data, not a CSPM/AWS Config replacement.

SteampipeAWSKubernetes

Cloud & Infra· Added 8 Oct 2026, published 7 Oct 2026

arxiv.orgpaper🧠 AI

Post-Training Semantic Lifting for 3D Gaussian Splatting: Separating Detector, Lifting and Representation Error

This arXiv paper introduces a post-training semantic lifting method for 3D Gaussian Splatting that assigns a single target class to Gaussians by accumulating target/non-target evidence across all views, weighted by per-view visibility, then filters with a dual-threshold (β and γβ) scheme that selects high-confidence seeds and extends them via connected components. The authors evaluate by transferring labels from Gaussians to mesh vertices and decompose error into three sources — the 2D detector, the lifting step, and the representation transfer — tuning thresholds on seven Replica scenes and testing on ten held-out ScanNet++ scenes, reaching mean mIoU of 0.93/0.65 (dataset masks/YOLO masks) on validation and 0.80/0.54 on test, a 0.24 mIoU improvement over prior per-view thresholding. The error analysis finds the 2D detector is the dominant remaining error source, informing where future gains in 3D semantic reconstruction should focus.

3D Gaussian Splatting

ScanNet++ReplicaYOLO+1

research, multimodal· Added 7 Oct 2026, published 6 Oct 2026

eprint.iacr.orgpaper🛡️ Security

The Post-Quantum Cost of Garbled-Circuit Bitcoin Bridges

This IACR ePrint paper examines the post-quantum security of Bitcoin bridges that rely on off-chain garbled-circuit evaluation of succinct proofs to validate withdrawals, where input labels encoding the proof must be committed on-chain. The authors show current pairing-based proving systems are vulnerable to quantum attacks, and that swapping to post-quantum proving systems shrinks circuit size but drastically inflates proof size, forcing proofs to span multiple Bitcoin blocks and raising on-chain cost. They outline research directions to improve proof efficiency, circuit representation, and label encoding for quantum-resistant bridge designs.

Bitcoin

Mukesh TiwariAaron Feickert

Blockchain· Added 8 Oct 2026, published 6 Oct 2026

arxiv.orgpaper🧠 AI

Test-Time Adaptation of Quantized ViTs via Single-Pass Quantizer-Aligned Recalibration

This paper introduces Quantizer-Aligned Recalibration (QuAR), a single-pass, backprop-free test-time adaptation method for quantized vision transformers that recalibrates activations at frozen quantizers to counter distribution shift without updating model parameters. On ImageNet-C with ViT-B across 3-, 4-, 6- and 8-bit weight/activation precision, QuAR beats the strongest backprop-free baselines by up to 4.00 points while cutting latency 46% and adding only 0.17 MB memory overhead, with gains holding across continual streams, label shift, seven OOD suites, and three other backbones.

Vision TransformerTransformer

ViT-BImageNet-C

research, inference· Added 7 Oct 2026, published 6 Oct 2026

arxiv.orgpaper🧠 AI

FREA: A Multi-Source Expert Benchmark for Reaction Feasibility Verification

FREA is a benchmark of 751 chemical reactions labeled by expert chemists for feasibility verification, drawn from retrosynthesis model proposals, zero-yield experimental records, LLM-generated edits, and five negative-candidate generation methods. The authors show no single verifier type (LLMs prompted with the feasibility criterion, dedicated forward models, or retrosynthesis scorers) dominates across all sources, and that adding generated negative examples to training improves mean AUROC but fails to transfer to retrosynthesis proposal screening. They also release a corpus of over 14 million recorded and generated reactions to support further training and evaluation of feasibility verifiers.

FREA

evals, research· Added 6 Oct 2026, published 5 Oct 2026

github.blogarticle🧠 AI

ReviewBench: An open benchmark for AI code review

ReviewBench is an open benchmark from GitHub and Microsoft for evaluating AI code review agents, built from 219 pull requests drawn from 187 public repositories across 19 languages, modeled on the distribution of 103.9 million real GitHub pull requests. It uses a three-stage multi-source golden-set process, reports six precision/recall-family metrics (including grounded and augmented recall to credit issues beyond the fixed golden set), supports severity/category slicing and a configurable F-beta score, and was validated by independent senior engineers who agreed with its labels 96.6 percent of the time. GitHub used it to evaluate Copilot code review (CCR), showing its offline predictions tracked the direction of later production A/B results.

GitHub CopilotGitHub

ReviewBenchMicrosoftMichelle Zhou

evals, coding-agents· Added 8 Oct 2026, published 5 Oct 2026

arxiv.orgpaper🛡️ Security

VulValidate: Auditing Function-Level Vulnerability Labels with Executable Evidence

VulValidate is a framework where LLM agents coordinate dynamic-analysis tools to reconstruct vulnerable/fixed code revisions and build triggering experiments that verify whether a function labeled "vulnerable" in a commit-derived dataset actually exhibits the vulnerability. Applied to all 35,849 vulnerable-labeled instances in BigVul, PrimeVul, and DiverseVul, it confirms 57.2%, relabels 19.0% as mislabeled, and leaves the rest undecided or unmeasured, producing a corrected 15,890-function release; retraining five detectors on corrected labels improves F1 for four of five on each dataset. The work exposes how commit-based labeling noise inflates or depresses vulnerability-detector benchmarks and ships a reusable auditing skill and corrected datasets.

LLM

BigVulPrimeVulDiverseVul

AppSec· Added 6 Oct 2026, published 4 Oct 2026

arxiv.orgpaper🧠 AI

VulValidate: Auditing Function-Level Vulnerability Labels with Executable Evidence

VulValidate is a framework where LLM agents coordinate dynamic-analysis tools to reconstruct vulnerable/fixed code revisions, craft triggering inputs, and compare runtime behavior, producing executable evidence for whether a function-level vulnerability label is actually correct. Applied to all 35,849 "vulnerable" instances in the BigVul, PrimeVul, and DiverseVul datasets, it confirms 57.2%, corrects 19.0% of labels, and leaves 22.3% undecided, yielding a corrected release of 15,890 distinct vulnerable function bodies validated against expert review. Retraining five detectors on the corrected labels changes measured F1 on BigVul and DiverseVul, showing that commit-derived vulnerability labels used across the field are substantially noisy and that agentic, execution-grounded auditing is a viable correction method.

VulValidateBigVulPrimeVul+1

evals, agents· Added 6 Oct 2026, published 4 Oct 2026

decrypt.coarticle🛡️ Security

Ethereum Now Lets You Pay for AI Without Revealing Who You Are

The Ethereum Foundation and the Open Anonymity Project launched zkAPI on Ethereum mainnet: users deposit ETH/USDC into a vault contract and generate zero-knowledge proofs of sufficient, unspent balance to receive capped, short-lived API keys for AI services (currently routed through OpenRouter), so the AI provider never learns payer identity and the payment layer never learns prompt content. The design follows a February 2026 Ethereum Research proposal by Davide Crapis (Ethereum Foundation dAI team lead) and Vitalik Buterin, who had earlier proposed the ERC-8004 agent-identity standard; the protocol is explicitly labeled experimental with no formal audit, and a companion browser-based client (OA Chat) is open source on GitHub.

EthereumOpenRouter

zkAPIOpen Anonymity ProjectDavide Crapis+3

Identity & Access· Added 3 Oct 2026, published 2 Oct 2026

decrypt.coarticle🧠 AI

Ethereum Now Lets You Pay for AI Without Revealing Who You Are

The Ethereum Foundation and the Open Anonymity Project launched zkAPI on Ethereum mainnet: users deposit ETH or USDC into a vault contract, then generate a zero-knowledge proof of sufficient, unspent funds to obtain a capped, short-lived API key, letting them call AI models through OpenRouter without the provider linking their prompts to a billing identity. The protocol (based on a February 2026 Ethereum Research proposal by Davide Crapis and Vitalik Buterin) issues signed usage receipts after the key expires to settle actual spend, and ships with a browser chatbot (OA Chat) and an open GitHub repo; it is explicitly labeled experimental with no formal audit yet. The same proof-based metering pattern is proposed for other metered services like blockchain data queries, image/video generation, and machine-to-machine payments.

OpenRouterOpenAI APIEthereum

Ethereum FoundationOpen Anonymity ProjectDavide Crapis+4

ai-infra· Published and added 2 Oct 2026

github.comrepo🧠 AI

Microsoft Agent Framework — python-1.20.0 release

Release notes for Microsoft's Agent Framework (Python and .NET SDKs), covering versions python-1.16 through python-1.20 and dotnet-1.19 through dotnet-1.23. Changes span native vector-store connectors (MongoDB, Qdrant, PostgreSQL/pgvector, DuckDB, SQL Server, Azure Cosmos DB, Azure AI Search), Foundry hosting redesigns around request-scoped agent factories and durable Invocations, MCP skill/session security hardening (approval binding, security labels, ZIP-only skill archives), Magentic multi-agent orchestration and handoff routing, and checkpoint/replay correctness fixes across workflows.

PythonModel Context ProtocolMongoDB+10

MicrosoftTypeSafe AIMistral+1

agents, mcp· Added 2 Oct 2026

arxiv.orgpaper🛡️ Security

Threat-Preserving Representation Sensitivity in Agent-Security Benchmarks

This paper introduces threat-preserving representation sensitivity (TPRS), a metric showing that attack success rate (ASR) scores on LLM-agent security benchmarks shift dramatically based on cosmetic changes to tool naming, even when the underlying task, policy, and ground truth are held fixed. Testing on Agent Security Bench, MCPTox, and AgentDojo, the authors find swings of up to ~13 percentage points in ASR on GPT-5-mini and Claude Haiku 4.5 just from renaming tools between threat-related and threat-neutral labels, with a matched-length control reproducing most of the effect. The findings argue that single-representation ASR scores are not reliable robustness claims and that benchmarks should report results across multiple threat-preserving representations.

GPT-4o

Agent Security BenchMCPToxAgentDojo+3

AI Security· Added 5 Oct 2026, published 2 Oct 2026

arxiv.orgpaper🛡️ Security

CorrectGuard: Eyes-Off Correctness Estimation for Black-Box Security Guardrails

CorrectGuard is a framework for estimating whether black-box AI security guardrails made correct decisions on inputs that are inaccessible to humans (production privacy) or to the guardrail-auditing model itself, using only labeled "eyes-on" data and no internal access to the guardrail. The authors test in-context-learning, embedding, and finetuning-based correctness classifiers across 13 safety/security datasets (harmful content, jailbreaks, prompt injection, extraction) and multiple open-weight guardrails, showing up to 25-point macro-accuracy gains in error identification and improved risk-based abstention/ranking of guardrail decisions.

LLM

AI Security· Added 5 Oct 2026, published 2 Oct 2026

arxiv.orgpaper🧠 AI

CorrectGuard: Eyes-Off Correctness Estimation for Black-Box Security Guardrails

CorrectGuard is a framework for estimating how accurately black-box security guardrails (jailbreak, harmful-content, prompt-injection and extraction detectors) perform on inputs that cannot be inspected by humans or by the evaluating model itself, using an independent correctness model trained only on labeled "eyes-on" data. The authors test in-context-learning, embedding-based, and fine-tuned correctness classifiers under leave-one-dataset-out evaluation across 13 safety/security datasets and multiple open-weight guardrails, showing up to 25-point macro-accuracy gains and improved risk-coverage tradeoffs (AURC reduced from 0.33–0.44 to 0.17–0.22) for guardrail decision ranking and abstention. The work matters for anyone building auditing or eval pipelines for opaque safety classifiers without privileged access to their internals.

CorrectGuard

evals, research· Added 5 Oct 2026, published 2 Oct 2026

arxiv.orgpaper🛡️ Security

Passing the Test You Trained On: Re-evaluating Prompt-Injection Detectors for LLM Agents

This arXiv paper replays ground-truth tool calls from the AgentDojo and tau-bench agent benchmarks (without an LLM) to generate benign-by-construction tool outputs, then labels injected outputs via differential replay to evaluate fifteen prompt-injection detectors, including Meta's Prompt Guard 2, plus two task-aware LLM judges, also testing them on BIPIA. It finds detection rankings transfer poorly between benchmarks (the best detector on BIPIA catches only 2% of AgentDojo injections at 1% false-positive rate; a detector catching 72% of AgentDojo injections catches only 15% on tau-bench), while false-positive rates do transfer between the two agent benchmarks, and traces this to mismatches between training data format (e.g., training on short prompt strings vs. tool-output-embedded injections) and deployment context.

Prompt Guard 2

AgentDojotau-benchBIPIA+1

AI Security· Added 5 Oct 2026, published 2 Oct 2026

deepseek.comtool🧠 AI

DeepSeek Harness Desktop for macOS and Windows

DeepSeek Harness is an open-source general-purpose agent harness from DeepSeek, built on the "Cordis" everything-is-a-plugin architecture, shipped as a desktop app for macOS/Windows and runnable as a web UI from code. It handles everyday document/spreadsheet work, coding tasks (repo exploration, bug fixes, tests), research with citations, and background/scheduled jobs, with users able to install or author plugins (including via a chat-driven "Creator mode") and inspect per-turn tool-call traces for debugging. It runs on DeepSeek's DeepSeek-V4.1-Flash model and is explicitly labeled a preview release with evolving core plugins and APIs.

DeepSeek-V4.1-Flash

DeepSeekCordis

agents· Published and added 2 Oct 2026

theblock.coarticle🧠 AI

Ethereum Foundation launches zkAPI to let users pay for AI models without revealing identity

zkAPI is a protocol launched on Ethereum mainnet by the Ethereum Foundation and the Open Anonymity Project for paying AI API providers without revealing identity. Users deposit ETH or USDC into a vault as a private note and spend it with zero-knowledge proofs. A server issues temporary capped API keys, and nullifiers prevent double-spending. It is labelled experimental, offers no network anonymity, and prompt content can re-link sessions.

zkAPIEthereumUSDC+2

Ethereum FoundationOpen Anonymity ProjectVitalik Buterin+1

ai-infra· Added 10 Oct 2026, published 2 Oct 2026

blog.cloudflare.comarticle🧠 AI

Clef / Clef-flash decision models (Cloudflare)

Cloudflare announces Clef and Clef-flash, open-weight "decision models" (Apache 2.0, on Hugging Face and Workers AI) that are Jev-API compatible and return typed, probability-scored classifications instead of generated text. The models are built by post-training a frozen Qwen backbone (Qwen3.8-27B for Clef, a smaller Qwen3.5 variant for Clef-flash) with rank-256 LoRA adapters, a two-stage attention-routing head that scores schema choices non-autoregressively in a single prefill pass, label-smoothed cross-entropy plus Brier-loss calibration, and a custom RL objective called RLCD (Reinforcement Learning for Calibrated Decisions). Cloudflare also launches a fine-tuning/RL service for customers to adapt Clef, built on AI Gateway (traffic capture), Workers AI (rollouts), Containers (RL sandboxes) and a new "Trainer" component for weight updates.

Hugging FaceQwen3.8-27BQwen3.5+3

ClefClef-flashJev+6

models, training, inference· Added 3 Oct 2026, published 1 Oct 2026

arxiv.orgpaper🧠 AI

StateTape: Action-Conditioned Evidence Lifecycle Modeling for Long-Horizon Coding Agents

StateTape is a framework for long-horizon coding agents that replaces text-based context pruning/summarization with a symbol-level code graph tracking which code symbols each write could have affected. A per-write procedure uses the graph to nominate context records a write may have falsified, with a small manager model resolving ambiguous cases, turning staleness detection from text inference into an observation of actual code dependencies. The paper introduces TraceBench, a benchmark labeling what an agent holds versus what it actually needs, and shows improved resolve rates across six coding agents and three edit-heavy benchmarks with low computational overhead.

TraceBenchStateTape

coding-agents, research· Added 30 Sep 2026

arxiv.orgpaper🧠 AI

Ego4WAM: What Matters When Scaling Egocentric Human Data for Robot Learning?

Ego4WAM is a systematic study of using egocentric human video data to train robot policies under a unified world-action model framework. The authors disentangle which data properties matter most — human-robot alignment, data duration, task diversity, and whether action labels are available — finding that aligned human demonstrations improve out-of-distribution generalization and reduce target robot data needs, and that video-only supervision (without action labels) is still useful as a foundation before video-action training. Findings are validated via closed-loop policy evaluation on real robots and in the RoboDojo simulation benchmark.

RoboDojoEgo4WAM

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

arxiv.orgpaper🛡️ Security

From A2A Attacks to Envelope-Layer Defense: Red-Teaming Evaluation of LLM Agents and a Three-Layer Isomorphic Attack-Defense Model

This paper proposes A2A-TIBA, an indirect prompt injection attack that abuses agent-to-agent (A2A/ACP) protocols by getting a target LLM agent to deploy a callback program, letting an attacker bypass the agent entirely to issue commands and exfiltrate data. It also introduces GDA Measurement, a red-team testbed that captures raw gateway context to distinguish four outcome classes (semantic refusal, semantic breach, interception, penetration) instead of a single attack-success-rate metric, and ELA-ITL, a three-layer isomorphic attack-defense model splitting defense into envelope packaging, LLM recognition, and agent interception. Testing on 15 agent-frontend x LLM-backend combinations and a 1,000-case dataset shows that labeling malicious-prompt indicators in envelope channels (A2A, tool, memory) significantly improves LLM recognition of injected content.

LLM

A2A-TIBAGDA MeasurementELA-ITL

AI Security· Added 2 Oct 2026, published 30 Sep 2026

arxiv.orgpaper🧠 AI

Breakdown of Local Denoising as Semantic Speciation

This paper studies two temporal phenomena in generative model sampling dynamics: a 'speciation window' where a sample commits to a semantic class, and a 'nonlocality window' where local context becomes insufficient for generation. Under a 'common cause' hypothesis — that semantic labels explain correlations between distant tokens — the authors prove the nonlocality window must fall within the speciation window, and identify conditions under which both windows collapse to a single phase-transition point as system size grows, verified analytically in Gaussian mixture models.

research· Added 30 Sep 2026, published 29 Sep 2026

arxiv.orgpaper🧠 AI

A Function-level Dataset of Vulnerable and Fixed Source Code in JavaScript and TypeScript

JsVul is a function-level dataset of vulnerable and fixed JavaScript and TypeScript source code, built from seven sources using a language-specific pipeline that strips noise such as minified code and cosmetic edits, then applies multi-stage deduplication and heuristic labeling. Delivered as time-ordered JSONL pairs of pre-fix and post-fix code, it is designed to train and evaluate ML-based vulnerability-detection models, and the paper argues language-aware preprocessing improves data quality over generic multi-language datasets.

JavaScriptTypeScript

JsVul

training, research· Added 30 Sep 2026, published 29 Sep 2026

arxiv.orgpaper🛡️ Security

A Function-level Dataset of Vulnerable and Fixed Source Code in JavaScript and TypeScript

JsVul is a function-level dataset of vulnerable and fixed JavaScript/TypeScript code pairs, aggregated from seven sources and built with a language-specific pipeline that strips noise (minified code, cosmetic edits), deduplicates across commits, and applies heuristic labeling of security-relevant changes. It is released in time-ordered JSONL format to support training and evaluating automated vulnerability detection models specifically for the JS/TS ecosystem, where prior datasets were largely generic and multi-language.

JavaScriptTypeScript

AppSec· Added 30 Sep 2026, published 29 Sep 2026

huggingface.coarticle🧠 AI

NVIDIA Kumo Tabular Sets a New Accuracy-Efficiency Frontier for Tabular Prediction

NVIDIA Kumo Tabular is an open foundation model for tabular data (28M–215M parameters, three sizes) that predicts labels for new rows via in-context learning in a single forward pass, with no task-specific training or feature engineering. It uses a Transformer built around column, row, and in-context attention (building on TabICL and TabPFN), Fourier feature cell embeddings, and a length-aware attention temperature to stay accurate on very large tables; it's pretrained entirely on synthetic tables sampled from structural causal models. It tops the TabArena, BeyondArena, TALENT, and ScoringBench leaderboards while running much faster than the next-best model (LimiX-2), and is released under the OpenMDW-1.1 license with weights on Hugging Face and code on GitHub.

TransformerHugging FaceCUDA+1

NVIDIATabArenaBeyondArena+10

models, evals· Published and added 29 Sep 2026

arxiv.orgpaper🧠 AI

Reliability-Gated Fusion of Consumer Head and Foot IMUs for Lower-Body 3D Pose

This paper introduces a channel-gated fusion model for sparse inertial motion capture that learns per-channel, per-stream temporal gates to weight the reliability of consumer earbud head IMU and smart-insole foot IMU signals, trained with an auxiliary reliability objective on synthetically corrupted data. On a new 35-take benchmark (using SAM-3D-Body pseudo-ground-truth labels), the gated model achieves 69.4mm error on clean data and remains most robust across simulated bias, drift, and dropout faults, automatically suppressing biased firmware-fused foot-orientation channels without test-time supervision (dropout detection AUROC 0.92-0.999). It is compared against a fine-tuned HMD-Poser baseline, which is more accurate on clean data (64.4mm) but degrades far more under worst-case faults (+16.1mm vs +3.5mm).

IMU

SAM-3D-BodyHMD-Poser

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

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