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“Meta”
latent.spacearticle🧠 AI

Agents Prompt People: Using Agents for Long-Horizon Financial Work

Latent Space podcast write-up of an AI Engineer New York interview with Aman Singh, lead AI Engineer at Arca, an AI-native wealth management firm. Arca runs agent tasks lasting up to ten days, uses a per-client memory system, and owns its harness, which routes tasks to models (mainly Claude) by internal benchmarks. Safety rules are deterministic, trading stays with human advisors, and a meta-agent uses advisor feedback and traces to improve the harness.

ClaudeLLM
ArcaAman SinghAnthropic+6

agents, ai-infra· Published and added 31 Oct 2026

socket.devarticle🛡️ Security

Cursor Removes Malicious Claude Code 2.1.313 Extension From Its Marketplace

Socket's analysis of the reported malicious Claude Code extension 2.1.313, which Cursor confirmed was served from its own marketplace on 29 October 2026 to up to 5,000 users. Open VSX's change feed (3,392 events) and npm metadata hold no publication or removal record for that version, contradicting the initial OpenSourceMalware claim. Cursor published hashes, IOCs and remediation scripts.

Claude CodeCursorOpen VSX+3
OpenSourceMalwareSocketPaul McCarty+1

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

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

ethresear.chpaper🛡️ Security

Secure Transaction Verification and Authorization (STVA)

Secure Transaction Verification and Authorization (STVA) is a draft Standards Track ERC (created October 2026) by @MATOBOYCRYPTO65. It proposes structured human-readable metadata and semantic intent for transactions and UserOperations, an independent simulation and verification interface, and scoped, revocable capability tokens for privileged operations. It targets blind signing and frontend supply-chain attacks, citing the February 2025 Bybit Safe multisig theft. The page gives only the abstract, motivation and core concepts.

EthereumSafe
BybitEIP-712ERC-1271+5

Blockchain· Published and added 28 Oct 2026

arxiv.orgpaper🧠 AI

Metacognitive Decoding: Efficient Inference for Diffusion Language Agents

Metacognitive Decoding is a lightweight plug-in controller for diffusion language model backbones. It learns when to finalize tokens and how far generation should extend, and is trained on agent trajectories to balance correctness against generation cost. Across five benchmarks it cuts inference FLOPs by 20.6% on average versus the most efficient baseline, with competitive task accuracy. The abstract names no specific benchmarks or backbones.

diffusion model
arXiv

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

arxiv.orgpaper🧠 AI

Interpretable Meta-Policies for Neural Decision Making

NEXUS is a hierarchical reinforcement learning framework that pairs neural skills with symbolic meta-policies, so transparent reasoning sits at the level of high-level skills rather than atomic actions. It uses object-centric observations and LLM interaction to design reward functions and meta-policies. Agents learn disentangled skills, align better with goals in the game manual, resist reward hacking more, and cope better with game simplifications than deep RL. The paper also compares neural, symbolic and neuro-symbolic meta-policy architectures.

LLM
NEXUSarXiv

research, training, ai-safety· Added 28 Oct 2026, published 27 Oct 2026

securityweek.comarticle🛡️ Security

iOS, macOS 27.2 Patch 286 Bugs; Fix for Exploited Flaw Backported to iOS 18

Apple's 27.2 releases fix 286 vulnerabilities across iOS/iPadOS (163) and macOS Golden Gate (263), with memory corruption in WebKit and the kernel, sandbox escapes, Gatekeeper bypasses and a remote code execution bug in Remote Management. iOS and iPadOS 18.7.11 backport the fix for CVE-2026-86950, an exploited zero-day found by Meta's product security team and first patched in 26.7.1. Patch counts for tvOS, watchOS, visionOS, Safari and the 26.7.2 and Sequoia 15.8.2 releases are also listed.

iOSmacOSSafari+2
CVE-2026-86950Metasandbox escape+2

Mobile Security, other· Published and added 27 Oct 2026

x.comadvisory🛡️ Security

lee1981 (@lee1981b) on X

CVE-2026-115905 is a ZIP cloaking flaw in the Rust zip crate (zip-rs/zip2), rated CVSS v4.0 10.0 and affecting versions below 9.0.5. ZipArchive reads central-directory metadata while ZipStreamReader reads local headers, so a crafted archive can pass filename inspection and then overwrite a different file during streaming extraction. Version 9.0.5 adds consistency checks, but ZipStreamReader::new leaves them off by default, and extraction is not atomic, so files can be written before an error is raised. The post covers CWE-20 and CWE-436, KEV and EPSS status, Debian status and a six-step investigation trail.

Rustzip crateCargo+3
CVE-2026-115905CWE-20CWE-436+3

AppSec, Blue Team & Detection· Added 29 Oct 2026, published 27 Oct 2026

x.comthread🛡️ Security

AGTP (@AGTPinsights) on X

AGTP's X thread summarizes Meta's Muse Spark 1.3 Evaluation Report. Prompt injection was tested on Exponential Security Labs' RepoInject v1 and BrowserInject v1 and on Gray Swan's IPI Arena, with robustness said to be on par with GPT-5.6 Sol. It also covers chain of thought controllability evals, PostTrainBench under Loss of Control, and Handshake's pre-release human red teaming. It notes the Midas Project's criticism of Muse Wonder's missing safety report.

Muse SparkMuse Spark Open
prompt injectionRepoInject v1BrowserInject v1+10

AI Security· Added 27 Oct 2026, published 26 Oct 2026

x.comthread🧠 AI

AGTP (@AGTPinsights) on X

Thread summarizing Meta's Muse Spark 1.3 Evaluation Report, the safety write-up for the model released on September 2, 2026. It covers prompt-injection results on Exponential Security Labs' RepoInject v1 and BrowserInject v1 and Gray Swan's IPI Arena, chain-of-thought controllability evals, PostTrainBench under Loss of Control, and Handshake's human red teaming. It also notes The Midas Project's criticism that Muse Wonder still has no safety report, and the Muse Spark Open rollout (816B total, 42B active parameters).

Muse Spark 1.3Muse Spark OpenMixture of Experts
MetaMuseMuse Wonder+19

ai-safety, evals, models· Added 27 Oct 2026, published 26 Oct 2026

arxiv.orgpaper🛡️ Security

Log-To-Leak: Prompt Injection Attacks on Tool-Using LLM Agents via the Model Context Protocol

Log-To-Leak is a prompt-injection attack on tool-using LLM agents that abuses Model Context Protocol tool metadata to trigger an attacker-controlled logging call after the last legitimate call, leaking the user query, server name, server response and agent response. Evaluated on five MCP servers and four agents, it reaches 62.64-100% attack success with 76.55-95.64% logging accuracy. Three defenses leave over 55% residual ASR on GitHub MCP with GPT-4o.

Model Context ProtocolGitHubGPT-4o+1
Log-To-Leaktool poisoningprompt injection

AI Security· Added 28 Oct 2026, published 26 Oct 2026

securityweek.comarticle🛡️ Security

11 Million Impacted by Capcom Data Breach

Capcom disclosed a breach of CAPCOM ID, its shared login service for online games, discovered on 19 October. Attackers took 11,774,247 email addresses plus account metadata (creation date, last login, login count, last IP). For 43 users dates of birth, gender, country and nicknames were also exposed, and passwords were changed on 196 accounts. Capcom says no passwords were stolen, reset the affected accounts, and warns of impersonation emails. The root cause is not stated.

CapcomCAPCOM ID

Threat Intel· Published and added 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

arxiv.orgpaper🧠 AI

The Geometry of Metaphor: Probing and Steering Metaphor Representations in Large Language Models

The Geometry of Metaphor is an October 2026 arXiv preprint that uses mechanistic interpretability to find causal subspaces for metaphor in LLMs of different architectures and sizes. The authors steer output on classification tasks across parts of speech. They also steer generation tasks, showing the metaphoricity of model output can be controlled. The abstract gives no model names or metrics.

LLM
arXiv

research· Added 24 Oct 2026, published 22 Oct 2026

alignmentforum.orgarticle🧠 AI

Monitorability evaluations and policy proposals

Monitorability evaluations and policy proposals is an AI Alignment Forum post by evhub, published 22 October 2026, with about a five-minute read time. The fetched page contains only the title and metadata, so its arguments are not known. The title points to evaluations of how well model behaviour can be monitored and to related policy proposals.

evhubAI Alignment Forum

ai-safety, evals, ai-policy· Published and added 22 Oct 2026

ethereum-magicians.orgthread🧠 AI

ERC-TBA: Agent Income Statements

ERC-TBA: Agent Income Statements is a draft Ethereum standard, a profile of the ERC-8004 Reputation Registry. A writer posts chained statements of what an agent's agentWallet received in one asset, with each incoming transfer a Merkle leaf, so lenders and buyers can verify earnings from x402 payments. Windows are cut at owner or wallet changes, and the post asks about statement grain, the MetadataSet requirement and who may write. The reference implementation is unaudited.

x402EthereumSolidity+1
ERC-8004ERC-8434AID Document

agents· Published and added 22 Oct 2026

blog.cloudflare.comarticle🧠 AI

How Commerce.com and Cloudflare are getting storefronts ready for AI agents

Cloudflare and Commerce.com describe enabling WebMCP, Google's proposed browser standard for exposing structured tools to AI agents, across almost 100,000 storefronts. Cloudflare injects a bridge script at the edge that proxies tools from Commerce.com's backend MCP server. Per-merchant opt-out uses Configuration Rules and custom hostname metadata, and a configurable MCP URL (/api/mcp) was added.

WebMCPModel Context ProtocolCloudflare+3
Commerce.comGoogleKristina Pototska+1

mcp, agents· Added 29 Oct 2026, published 21 Oct 2026

latent.spacetalk🧠 AI

AI for the Fortune 2000: How Tenex Builds an AI-Native SDLC — Alex Lieberman & CJ Hess

Latent Space interview from AIE New York with Tenex cofounder Alex Lieberman and CJ Hess on running an AI-native engineering firm of 75 people. Covers story-point pay for engineers, the MetaHarness (plans, decisions and agent logs checked into Git with a CLI), roughly 2x engineer output in 2026, Opus 5.5 and Codex usage, and enterprise adoption rates. Also debates vertical AI and the Kill My SaaS experiment.

GitClaude CodeCodex+4
TenexAlex LiebermanCJ Hess+15

coding-agents, ai-policy, agents· Published and added 21 Oct 2026

latent.spacetalk🧠 AI

AI for the Fortune 2000: How Tenex Builds an AI-Native SDLC — Alex Lieberman & CJ Hess

Latent Space interview from AI Engineer New York with Tenex cofounder Alex Lieberman and CJ Hess. Tenex has 75 people, pays engineers by story points, and reports roughly 2x engineer output over the past year. CJ describes MetaHarness, a repo-based set of skills, plans and a CLI that logs agent decisions in Git. They also cover enterprise AI adoption rates, token spend, and vertical AI. The page text is truncated.

GitGitHubCodex+3
TenexAlex LiebermanCJ Hess+14

coding-agents, ai-policy, agents· Added 23 Oct 2026, published 21 Oct 2026

x.comthread🧠 AI

0xMarioNawfal - The Roundtable Show (@RoundtableSpace) on X

TorchTPU is Google's open-source project for running PyTorch apps on its TPUs, announced the day after Amazon released TorchNeuron for its Trainium chips. Google says migration needs "a simple configuration change" with no rewrite. It worked with Meta, Hugging Face, and the vLLM and SGLang teams on compatibility, and added an agent suite that helps migrate apps and fix errors. The post is brief and gives no benchmarks.

PyTorchvLLMSGLang+4
GoogleAmazonNVIDIA+5

inference, ai-infra· Added 21 Oct 2026, published 20 Oct 2026

latent.spacearticle🧠 AI

[AINews] not much happened today

AINews issue for 16 to 19 October 2026 from Latent Space, a digest of Twitter and Reddit AI news. It covers DeepSeek V4.1 Flash 1017 (552B total, 16B active MoE, AA Intelligence Index 45), MiniMax M3.1 Flash, Mercury 3, Meta Muse, Google's Harness², vLLM v0.32.0 and a Terminal-Bench audit. Only the first 40,000 of 47,045 characters were available.

Mixture of ExpertsGGUFllama.cpp+12
Latent SpaceDeepSeekDeepSeek-V4.1-Flash+42

models, evals, inference· Added 30 Oct 2026, published 20 Oct 2026

latent.spacearticle🧠 AI

[AINews] not much happened today

AINews issue covering 16-19 October 2026 from Latent Space, summarising 12 subreddits and 544 Twitter accounts. It leads with DeepSeek V4.1 Flash 1017 (552B total, 16B active MoE, 1M context, MIT license, AA Intelligence Index 45), plus MiniMax M3.1 Flash, Mercury 3 and Meta Muse. It also covers vLLM v0.32.0, InferenceX results, Harness² and Terminal-Bench audit findings.

Mixture of ExpertsvLLMSGLang+11
Latent SpaceDeepSeek-V4.1-FlashDeepSeek+43

models, evals, inference, agents· Published and added 20 Oct 2026

huggingface.coarticle🧠 AI

MPS in PyTorch 2.15

PyTorch 2.15 MPS release post from the Hugging Face PyTorch team, covering the Apple silicon backend. It adds float8_e4m3fn tensors with F.scaled_mm, a native grouped matmul that speeds MoE decoding 5 to 6.5x (Qwen3.6-35B-A3B from 9.4 to 61.3 tokens/s on an M3 Ultra), and an 8x faster softmax. Wan2.2 VAE decode peak memory falls from 270 GiB to 8 GiB. It previews 2.16 attention on Metal.

PyTorchMetalMPSGraph+9
Hugging FaceAppleQwen3.6-35B-A3B+6

inference· Published and added 20 Oct 2026

anthropic.compaper🧠 AI

How employment is changing at Anthropic

"How employment is changing at Anthropic" is an October 2026 technical report by Nathan Wilmers, Regina Catipon, Maxim Massenkoff and Peter McCrory. It uses internal employment, agent telemetry, output, survey, calendar and hiring data plus 17 interviews. Junior technical hiring fell from 27% to 9%, agent hours reached six times employee hours by August 2026, and adopters' output rose. Employees increasingly do meta-work managing agents.

ClaudeClaude CodeSlack+1
AnthropicNathan WilmersRegina Catipon+5

ai-policy, agents· Added 19 Oct 2026, published 18 Oct 2026

arxiv.orgpaper🧠 AI

ToFu: Exploiting and Fuzzing the LLM–Orchestrator–Tool Trust Boundary

ToFu is a tool-aware fuzzing framework for LLM tools, including MCP servers. It maps each tool argument, using its metadata, to one of 74 CWE-guided semantics-aware sanitizers (path, URL, SQL and similar). It then generates schema-valid Atheris harnesses and semantic oracles, using runtime tracers for in-process tools and outcome oracles for opaque ones. The LLM (gpt-4.1) only maps sanitizers and writes seeds; it is never the security judge. The paper also shows a "Repeat-After-Me" trick, where an LLM told to repeat attacker-crafted text emits a tool call the orchestrator executes, reproduced on LangGraph, MAF, ADK 2.0, AutoGen, Strands and CrewAI. Across 273 runnable MCP servers (3,405 tools) it reports 253 verified bugs in 343 tools across 118 servers, such as path traversal, log injection and exfiltration. These are failures a crash-based fuzzer would miss. The result matters because it shows LLM-generated tool arguments cannot be treated as trusted.

Model Context ProtocolAtherisCodeQL+7
ToFuStrandsMAF+10

mcp, research, agents· Added 18 Oct 2026

arxiv.orgpaper🛡️ Security

ToFu: Exploiting and Fuzzing the LLM–Orchestrator–Tool Trust Boundary

ToFu is a tool-aware fuzzing framework that derives semantic security checks for LLM tools (mainly MCP servers) from their metadata and a corpus of 74 CWE-guided sanitizers. It generates schema-valid Atheris harnesses, uses runtime tracers and outcome oracles to catch bugs that don't crash, and uses an LLM only to map sanitizers and seed inputs, never as the judge. It also describes a "Repeat-After-Me" attack, in which the LLM is told to repeat attacker-written text that the orchestrator then dispatches as a tool call. It was reproduced on LangGraph, MAF, ADK, AutoGen, Strands and CrewAI. On 273 MCP servers it reports 253 verified bugs across 343 tools in 118 repositories, including path traversal, log injection, exfiltration and command bypass.

Model Context ProtocolAtherisLangGraph+3
Repeat-After-Mepath traversalSSRF+6

AI Security, AppSec· Added 18 Oct 2026

interconnects.aiarticle🧠 AI

Meta shakes up the frontiers, open and closed

Analysis of Meta's Muse Spark Open, an 816B total/42B active parameter open-weights model with 1M context, now the strongest American open model, and its position relative to Chinese open models such as GLM-5.3, Kimi K3 and DeepSeek V4.1 Flash. It examines Meta's restrictive custom license ("Built with Muse" attribution, $20M/month revenue gate, acceptable use policy, coverage of outputs and derivatives), and ranks Meta against Anthropic, OpenAI, Gemini and xAI after the closed Muse Wonder release. It also covers tier-3 labs (Mistral Large 4, Reflection's Beam) and valuation questions.

Hugging FaceMixture of ExpertsLlama+5
MetaMuseMuse Wonder+27

models, ai-policy· Published and added 17 Oct 2026

github.comrepo🛡️ Security

AikidoSec/safe-chain

Aikido Safe Chain is an open-source local proxy and shell-alias wrapper for npm, yarn, pnpm, bun, pip, uv, poetry, pipx and pdm. It checks every package download, including deep dependencies, against the Aikido Intel malware feed and blocks malicious packages before install. It also enforces a configurable 48-hour minimum package age by suppressing too-new versions in registry metadata. It supports custom registries and a self-hosted malware-list mirror, and has CI/CD install recipes for GitHub Actions, Azure Pipelines, CircleCI, Jenkins, Bitbucket and GitLab.

Aikido SecuritynpmPyPI+3
Aikido IntelSafe Chain

Supply Chain, DevSecOps· Added 17 Oct 2026

decrypt.coarticle🧠 AI

TokenMaxxing: How to Get the Most Out of Your AI Models

Decrypt guide to "tokenmaxxing" reframed as maximizing value per token rather than burning tokens. It gives tiered tips: pick smaller models for simple jobs, write one clear prompt, start new chats, cap output length, and use free models (beginner); Claude Code commands like /model, /clear, /compact, /context, /mcp, /effort and plan mode (intermediate); and prompt caching via cache_control, 50% batch discounts, and agent budget limits like --max-budget-usd (advanced). Includes context on Meta, Amazon and Uber token leaderboards and spend, a worked invoice-processing example where routing cut cost from $30 to $3.90, and the Jevons paradox in falling token prices.

Claude CodeClaudeChatGPT+8
DecryptAnthropicOpenAI+14

ai-infra, coding-agents, ai-policy· Published and added 17 Oct 2026

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