标签：#security

🚀 Try it now: Open the Arthas web app — create a room, share the code, chat with E2EE. No signup needed. TL;DR — Try It in 2 Minutes No signup required. A free public server is running at wss://arthas100-arthas-server.hf.space/ws . 1. Create an encrypted room (CLI) # Linux/macOS — download and make executable curl -L -o arthas-cli https://github.com/michaelwang123/arthas/releases/latest/download/arthas-cli chmod +x arthas-cli # Windows (PowerShell) — download the .exe # curl.exe -L -o arthas-cli.exe https://github.com/michaelwang123/arthas/releases/latest/download/arthas-cli-windows-amd64.exe # Create a room — generates AES-256 key locally, outputs share code ./arthas-cli create --server wss://arthas100-arthas-server.hf.space/ws --name "Alice" # Windows: .\arthas-cli.exe create --server wss://arthas100-arthas-server.hf.space/ws --name "Alice" # Output: # ✓ Room created! Share code: # QYEq9uxfKP9h-KCUsPUay:NlZezXoUErYr92grhif3Y-Hy3FOOK1ocb3WocCJJrQM # # The encryption key never leaves your device. ⚠️ Keep this terminal open — the room exists only while at least one participant is connected. 2. Join from another terminal (or send the code to a friend) # Linux/macOS ./arthas-cli join QYEq9uxfKP9h-KCUsPUay:NlZezXoUErYr92grhif3Y-Hy3FOOK1ocb3WocCJJrQM \ --server wss://arthas100-arthas-server.hf.space/ws \ --name "Bob" # Windows # .\arthas-cli.exe join QYEq9uxfKP9h-KCUsPUay:NlZezXoUErYr92grhif3Y-Hy3FOOK1ocb3WocCJJrQM --server wss://arthas100-arthas-server.hf.space/ws --name "Bob" That's it — you're chatting end-to-end encrypted. The server only sees ciphertext blobs; it cannot read, store, or parse anything. 💡 Prefer a web UI? Open the Arthas web app , create a room, and share the code. Bonus: Connect an AI Agent to the Same Room Every AI agent channel today (Telegram bots, Slack apps, Discord) transmits prompts in plaintext. With Arthas, your AI joins the encrypted room as a regular participant — the server can't tell human from bot (both are encrypted binary blobs). npm

TL;DR Day 2 of AI Native DevCon shifted from agent capability to operating discipline. The strongest sessions focused on how teams can run AI-native delivery with clearer context pipelines, measurable agent behavior, safer execution boundaries, and better organizational ownership. The scale showed up in the numbers too. Across the two days, DevCon brought together 650+ in-person registrations, around 2,000 online registrations, and a packed mix of sessions, workshops, hallway conversations, and practical lessons. Day 2 leaned into workshops. That shift mattered because the second day was less about proving agents can do useful work and more about showing how teams can make that work repeatable. Hey there, welcome back. Rohan Sharma here again continuing the devcon series. Day 1 gave us the framing, including Guy Podjarny ’s core point that skills should be treated like real software assets. Day 2 picked up from there and moved into the operating details. Once agents are inside daily engineering work, platform and product teams need to decide what changes first, who owns those changes, and how the results are measured. Talks that shaped Day 2 Harness engineering beyond code Marc Sloan from Tessl focused on the next gap many teams are hitting. Code context is increasingly structured, but product and design context still lives in external systems such as Figma, Notion, and Linear. Pulling that context live can reduce staleness, but it introduces drift in evals, versioning, and reproducibility. The practical lesson was to stop treating external product and design context as random reference material. Teams need a defined layer between the repository and those external systems, with clear versioning so evaluations can be replayed against known context snapshots. Without that, agents can produce work that looks technically correct while missing the product constraint that actually mattered. That is a very expensive kind of almost-right. From vibes to metrics Simon Obstbau

id CTI-2026-0603-NETSCALER title The Third Shadow of CitrixBleed — Large-Scale Exploitation of a NetScaler Memory Overread Reignites subtitle CVE-2026-3055: a March-disclosed SAML IdP information-disclosure flaw escalates in June — the gap between the "RCE" label and the real impact author Dennis Kim (김호광 / HoKwang Kim) email gameworker@gmail.com github gameworkerkim date 2026-06-03 classification TLP:GREEN severity CRITICAL lang en tags Edge-Device · Pre-Auth · Memory-Overread · Session-Hijack · SAML-SSO · CitrixBleed · CISA-KEV threat_actors Unattributed (likely a mix of ransomware and state-sponsored actors) cve CVE-2026-3055 (CVSS 9.3 v4.0 · CISA KEV) · related CVE-2026-4368 (CVSS 7.7) frameworks MITRE ATT&CK · NIST SP 800-61 · NIST SP 800-207 (Zero Trust) · CISA KEV · STIX/TAXII license CC BY-NC-SA 4.0 🚨 Heads-up: this is a VPN/remote-access issue — check your company's appliances now. If your organization runs Citrix NetScaler Gateway (the VPN / remote-access front door) or NetScaler ADC with SAML SSO enabled, you may be directly exposed to active, large-scale exploitation. Don't wait for a formal advisory to land in your inbox — inventory your internet-facing NetScaler appliances today , confirm patch level, and (critically) invalidate active sessions after patching . The details below explain why patching alone is not enough. The Third Shadow of CitrixBleed — Large-Scale Exploitation of a NetScaler Memory Overread Reignites Report ID CTI-2026-0603-NETSCALER · Published 2026-06-03 · Classification TLP:GREEN · Severity 🔴 CRITICAL Author Dennis Kim (김호광) · gameworker@gmail.com · @gameworkerkim CVE-2026-3055: a March-disclosed SAML IdP information-disclosure flaw escalates in June — the gap between the "RCE" label and the real impact Table of Contents Executive Summary (TL;DR) Opening — "An edge device, once it leaks, keeps leaking" Vulnerability Analysis — CVE-2026-3055 Memory Overread "RCE" or "Information Disclosure"? — Decomposing the Real Impact Timeline —

Just released an open-source bash checker for CIFSwitch (CVE-2026-46243) — the 19-year-old Linux kernel LPE disclosed last week that lets any unprivileged local user get root by abusing the CIFS/SPNEGO upcall path. The script runs on bare-metal, VMs, and inside containers, and is CI/CD-friendly with JSON output and clean exit codes. It checks: ✅ Kernel version against patched thresholds (6.18.22 / 6.19.12 / 7.0+) ✅ cifs-utils presence and exploitable version ✅ CIFS kernel module load state and blacklist status ✅ Unprivileged user namespace sysctl (the pivot point for the exploit) ✅ Active request-key cifs.spnego rules ✅ SELinux / AppArmor enforcement ✅ Container capabilities (CAP_SYS_ADMIN) ✅ Kernel symbol verification for the fix commit Outputs human-readable or JSON for SIEM ingestion. Exit 0 = safe, exit 1 = action needed — drop it straight into a pipeline. CIFSwitch is the fourth Linux LPE in under six weeks (after Copy Fail, Dirty Frag, and Fragnesia). If you're running multi-tenant Linux, CI runners, or container build farms, now is a good time to audit. I have also updated the cve_checks.conf in my my K8s-container_escape_audit toolkit to detect this issue.

The cybersecurity company is nearing a $300 million round led by Evolution Equity Partners.

The AI agent security community has been converging on a problem. A researcher recently ran an experiment — feeding a memory-retrieval framework 10 scenarios involving certificate operations: signing, issuing, revoking, delegating. The system retrieved the right memory 8 out of 10 times. It matched the external authorization gate 7 out of 10. The conclusion: metadata per item isn't enough. You need a separate authorization gate over the proposed operation. That conclusion is correct. But I want to show what that gate actually looks like when you build it — because the primitive already exists, and it's older than LLMs. The problem is authorization, not retrieval Most agent frameworks today invest in memory and observability. The agent can recall what it did before. You can see what tools it called. Logs, traces, dashboards. What they don't have is a cryptographically enforced answer to the question: was this agent authorized to do this, before it did it? Those are different problems. Retrieval tells you what the agent remembers about its permissions. Authorization tells you what it was actually granted — signed, tamper-proof, at dispatch time. An agent that retrieves "I have revocation permissions" from memory and then revokes a certificate it shouldn't touch is not an authorization failure at the retrieval layer. It's an authorization failure at the gate layer — because there was no gate. Certificates are that gate A certificate is a signed declaration of what an entity is authorized to do. Issued once, verifiable offline in ~1ms, revocable instantly. We've used them for TLS, for IoT devices, for code signing. The same primitive works for agents. The model is simple: Orchestrator issues a certificate at dispatch time The certificate carries the agent's identity and its exact scope in meta Every tool call goes through a gate that verifies the certificate offline On completion — or abort, or timeout — the orchestrator revokes it // Orchestrator — dispatch const { cer

Migrar 100 usuários manualmente no console da AWS é lento, suscetível a erros e impossível de auditar com precisão. Neste artigo você vai ver como automatizar esse processo usando AWS CLI e Shell Script direto no AWS CloudShell — sem instalar nada localmente. O resultado final: usuários criados, alocados nos grupos corretos e com MFA obrigatório, tudo em minutos. O que é o IAM? O AWS Identity and Access Management (IAM) é o serviço que controla quem pode acessar os recursos da sua conta AWS e o que cada pessoa ou serviço pode fazer. Com o IAM você gerencia: Conceito Descrição Usuário Identidade individual com credenciais próprias Grupo Conjunto de usuários que compartilham as mesmas permissões Política Documento JSON que define o que é permitido ou negado Role Identidade temporária assumida por serviços ou usuários A boa prática é nunca conceder permissões diretamente a um usuário — sempre use grupos. Visão geral da solução O fluxo é simples: Criar os grupos IAM no console Montar um arquivo CSV com os dados dos usuários Rodar um shell script no CloudShell que lê o CSV e cria tudo automaticamente Aplicar a política de MFA obrigatório nos grupos Passo 1 — Criar os Grupos IAM Antes de importar os usuários, os grupos precisam existir. No AWS Console , acesse IAM → User groups → Create group e crie um grupo para cada perfil do seu ambiente. Neste exemplo usaremos: RedesAdmin — administradores de rede LinuxAdmin — administradores de servidores Linux DBA — administradores de banco de dados Estagiarios — acesso limitado para estagiários Nomes de grupos suportam até 128 caracteres (letras, números e + = , . @ _ - ), são únicos por conta e não diferenciam maiúsculas de minúsculas. Passo 2 — Montar o arquivo CSV Crie uma planilha com os dados dos usuários e salve como CSV separado por vírgula (UTF-8) . O arquivo deve ter exatamente três colunas: Username , Group e Password . Username , Group , Password joao . silva , LinuxAdmin , Senha @2024 ! maria . souza , DBA , Senha @2024

My father worked in IT for over thirty years, and growing up around that shaped how I thought about computers. The earliest memory I have is sitting in my father's lap as he does something on his computer. One of the oldest photos I have is of me sitting on a chair in front of a computer. I grew up idolizing him. I switched to Linux when I was 12, by myself. I taught myself scripting, picked up programming basics, and spent more time in a terminal than most adults I knew. I have memories of sitting on the roof at 13 with my laptop, trying to crack my neighbor's WiFi with aircrack-ng (they were aware of my endeavors). However, growing up in a politically volatile neighborhood (Lyari) also made me politically aware and literate from a young age. With that, I developed an interest in political science and philosophy. I sat my A levels in economics and sociology, and I did not look back. For the next few years, the technical side of my life became just a habit rather than a professional direction. Then I realized I do not have to choose one or the other. I can carry on doing both. Today, I am an academic and technical editor. The social sciences gave me the writing skills: reading long blocks of dense theory, explaining abstract concepts in plain language, writing long analytical essays. And I understand technical concepts well enough to work with them seriously. I thought of synthesizing both. When I started building a technical writing portfolio, cybersecurity documentation felt like a natural place to go. Not because I had operational experience, but because I had grown up adjacent to that world. I understood the culture, the tooling, and the mindset, even if I had never worked a SOC shift. I knew I wanted to cover security documentation. Security teams produce some of the most consequential written work in any organization, and most of it is poorly structured, inconsistently formatted, or written for the person who already knows the answer rather than the person who

President Donald Trump signed an executive order Tuesday creating a "voluntary framework" for AI companies to share their frontier models with the federal government before they're released "to promote secure innovation and strengthen the cybersecurity of critical infrastructure." The order says the US AI industry has succeeded in part "because we refuse to stifle this […]

Available for Android 12 and later, the anti-scam feature is baked into Google Dialer, which sends a silent “confirmation signal” to ensure whoever’s calling you is who they appear to be.

Using threat modelling to make system dependability observable, testable, and actionable Executive summary Security and Reliability address system degradation. Security addresses degradation from intentional actions, such as denial-of-service attacks, while reliability addresses degradation from failure, load, dependency behaviour, operational change, or complexity. The underlying engineering question is the same: which critical system property can degrade, how would users experience that degradation, how would we detect it, and what controls would prevent, contain, or recover from it? This document proposes a practical way to bridge the two disciplines: anchor analysis on Critical User Journeys, express expected behaviour through SLOs and SLIs, use RAMSS to ensure coverage across dependability dimensions, and adapt PASTA-style threat modelling to reliability scenarios. The goal is not to merge Security and Reliability into one generic practice. The goal is to reuse the strongest habits of each discipline: security's adversarial modelling and reliability's production-oriented measurement, validation, and recovery loops. The most useful outcome is a shared model of degradation scenarios. A degradation scenario links a critical user journey to a concrete reliability or security threat, the system weakness that makes it possible, the signal that would detect it, the objective it would violate, and the mitigation or experiment that would validate the control. This makes risk easier to discuss with engineering teams because it connects abstract concerns to user impact, SLO burn, business loss, and testable remediation. 1. The problem: two disciplines, one degradation model After working in both Reliability and Security, I found that the two domains share much in common: both focus on objectives, weaknesses, control effectiveness, incident response, prioritisation, and residual risk, but often use different rituals, terminology, metrics, and boundaries. This separation ca

The password manager giant said hackers were able to 'brute-force' its two-factor system, allowing them to access customer accounts and download their password vaults.

Anthropic is expanding Project Glasswing, its security vulnerability program, and access to Mythos to 150 organizations across 15 countries — targeting critical infrastructure in power, water, healthcare, and communications where a cyberattack could affect 100 million people.

We Scanned 100 AI Repos on GitHub. Here's What We Found. A drone firmware project with 3× more stars than the real one. A crypto protocol that turned GitHub into a points farm. A README with 6,289 stars and 2 commits. As a developer turned architect, I used to treat GitHub stars as a proxy for trust. More stars meant more legitimate, fewer reasons to question before cloning. That instinct got me thinking. So I built TrustStar , audited hundreds of repos, and found that some people had figured out that instinct before me. Here's what the data showed. Case 1: The Airdrop Farm (QuipNetwork) 🔴 DANGEROUS Repository Stars Forks Fork/Star ratio hashsigs-py 11,200 9 0.0008 hashsigs-rs 11,300 42 0.0037 hashsigs-ts 11,300 31 0.0027 hashsigs-solidity 11,300 33 0.003 quip-protocol 11,645 159 0.014 ethereum-sdk ~11,400 72 0.006 cpp-sdk ~11,300 44 0.004 Six repos in completely different languages (Python, Rust, TypeScript, Solidity, C++) all converging on exactly ~11,300 stars. Projects with genuinely different audiences don't do that. The mechanism was on their own website: "Each GitHub repo star earns 5 QUIP points." QuipNetwork launched a crypto airdrop in early February 2026. Users who wanted QUIP tokens starred every repo in the organization. 11,000 stars in 48 hours, after five months of zero activity. The tell: dashboard.quip.network has 2 stars. nodes.quip.network has 2 stars. The repos they forgot to include in the airdrop show the real numbers. This is the first documented instance of a crypto airdrop using GitHub as a gamification layer. These aren't bots. They're real users who just wanted tokens. Case 2: The Typosquat (ShlkOfTheRa/scarab-osd) 🔴 DANGEROUS. The most dangerous case in this dataset. ShikOfTheRa/scarab-osd is a legitimate drone flight controller firmware project. 468 stars, built over 10 years. ShlkOfTheRa/scarab-osd , one character different, was created March 3, 2026. Byte-for-byte identical code. Twelve days later, 1,485 stars purchased in a 90-minute

As part of their 20th Anniversary celebration, Dark Reading asked five cybersecurity industry leaders who wrote blogs or columns for them over the years to select their favorite piece and share their reflections on the topic today. This is my section. Renowned technologist and author Bruce Schneier contributed a column on June 20, 2010, warning about cryptography’s inability to secure modern networks , a point he says he has been trying to argue since 2000. “For a while now, I’ve pointed out that cryptography is singularly ill-suited to solve the major network security problems of today: denial-of-service attacks, website defacement, theft of credit card numbers, identity theft, viruses and worms, DNS attacks, network penetration, and so on...

The right-wing think tank is actively pushing “civil terrorism”—increasing penalties for minor crimes committed while people engage in constitutionally protected free speech.

Security is not a feature you bolt on after launch. It is not the CISO's problem alone. It is not a checklist you run through before a compliance audit. It is a shared responsibility across every engineer, every team, every layer of the stack. This guide walks through the three layers where most web vulnerabilities live — Frontend , In Transit , and Backend — using a threat modeling lens: thinking like an attacker so you can build like a defender. What Is Threat Modeling? Before writing a single line of defensive code, you need to think systematically about your system's attack surface. Threat modeling is the process of: Identifying entry points — Where does untrusted data enter your system? Form inputs, URL parameters, uploaded files, third-party APIs? Assessing potential impact — If this entry point is exploited, what can an attacker access or do? Designing defenses proactively — Before the exploit occurs, not after. It shifts your mindset from "let's hope nothing breaks" to "let's assume something will be tried." Part 1 — Frontend Security: Stopping XSS What Is XSS? Cross-Site Scripting (XSS) happens when untrusted data is rendered as executable code in a browser. An attacker injects a script; your application runs it on behalf of your users. The consequences are severe: session hijacking, credential theft, defacement, redirects to malicious sites. There are three flavours: ┌─────────────────────────────────────────────────────────────────┐ │ XSS TYPES │ ├──────────────────┬──────────────────────────────────────────────┤ │ Stored XSS │ Malicious script saved in your DB, │ │ │ served to every user who loads that data. │ │ │ Most dangerous — persistent and broad. │ ├──────────────────┼──────────────────────────────────────────────┤ │ Reflected XSS │ Script lives in a URL parameter. │ │ │ Requires tricking the user into clicking │ │ │ a crafted link. Temporary, per-request. │ ├──────────────────┼──────────────────────────────────────────────┤ │ DOM-Based XSS │ Entir

Most people use these technologies every day. Almost nobody knows they exist. Every time you open YouTube, browse Instagram, join a Zoom meeting, or play an online game, your router is quietly performing a series of networking tricks behind the scenes. Those tricks have names: NAT SNAT DNAT PAT Port Forwarding They sound intimidating. They're actually much simpler than they appear. Let's break them down using something familiar: your home Wi-Fi. The Problem the Internet Had to Solve Imagine a family of five living in one house. Everyone owns a device: Laptop Phone Smart TV Gaming Console Tablet Each device needs internet access. The problem? Your Internet Service Provider usually gives you only one public IP address . Something has to manage all those devices sharing a single internet connection. That's where NAT comes in. NAT: The Receptionist of Your Network NAT stands for Network Address Translation . Think of NAT as a receptionist in an office building. People inside the building have room numbers: Laptop = Room 101 Phone = Room 102 TV = Room 103 But when communicating with the outside world, everyone uses the building's main address. The receptionist keeps track of who sent what. Your router does exactly the same thing. What Happens When You Visit Google? Inside your home: Laptop 192.168.1.10 Your router: Public IP 49.x.x.x When you open Google: 192.168.1.10 ↓ Router ↓ 49.x.x.x ↓ Google Google never sees your private IP. It only sees your router's public IP. That's NAT in action. SNAT: Changing the Sender's Address SNAT stands for Source Network Address Translation . The keyword is: Source It changes the sender's address. Before leaving your network: Source: 192.168.1.10 After SNAT: Source: 49.x.x.x The router replaces your private IP with its public IP. Without SNAT, websites wouldn't know how to send responses back to you. Real-Life Example Imagine mailing a letter. Instead of writing your bedroom number as the return address, you write the house address. Tha

From the motivation-pattern-log — a public, dated, falsifiable prediction log for AI-era cybersecurity attack patterns grounded in motivation analysis. Predictions are scored quarterly against stated falsifiers. PREDICTION-20260601-0008 Created: 2026-06-01 Pattern: boredom-with-asymmetric-leverage Substrate: Open-source package registries (npm, PyPI, Crates.io, Packagist) and GitHub Actions CI/CD workflow injection Leading indicator observed: Four distinct, concurrent, cross-registry supply chain campaigns (TrapDoor: 34 packages across npm/PyPI/Crates.io; Megalodon: 5,718 automated commits to 5,561 GitHub repos in six hours; Packagist compromise of 8 packages; Laravel-Lang PHP credential stealer) appeared within a 72-hour window in 2026-W22, all exhibiting automation signatures — throwaway publisher accounts, wave publishing, base64-encoded shell payloads, off-the-shelf delivery via GitHub Releases — consistent with toolkit operation rather than bespoke tradecraft. npm's reactive rollout of 2FA-gated publishing signals registry operators recognizing volume pressure. Predicted window: 2026-Q3 through 2027-Q1 Predicted shape: Automated, low-sophistication credential-stealing and backdoor-planting campaigns against npm, PyPI, Crates.io, and Packagist will continue to increase in incident volume while average per-campaign novelty declines. The dominant operational signature will be scripted account creation, automated package publication across multiple registries simultaneously, and CI/CD workflow injection via forged or compromised GitHub bot identities — all executable with commodity toolkits requiring no original exploit development. At least two registry operators beyond npm will announce reactive publishing controls (mandatory 2FA, namespace-squatting detection, automated malware scanning with publication holds) within the window in direct response to volume pressure. Security vendors will report a measurable increase in "unsophisticated supply chain" incidents re

A Google Sheets add-on with 4 million installs has been silently sending your spreadsheet cell data to OpenAI. Hacker News discovered this 9 days ago, when a PromptArmor security report went viral. Last night — when any normal HN story would be decaying into oblivion — it exploded a second time, gaining 59 points and 23.9% in a single day. I track Hacker News every day. I've seen 518 posts come and go over 319 days of systematic monitoring. Most stories follow a predictable death curve: peak on Day 1, bleed points for 2–3 days, then vanish from the Algolia search layer entirely. A post that survives 5 days is exceptional. One that accelerates on Day 9 is something else entirely. Here's the trajectory: 104 → 106 → 148 → 199 → 219 → 247 → (gap) → (gap) → 306 points. Over 9 days, that's a +194.2% total gain. But the real story is the shape of the curve. From Day 5 to Day 6, it added 20 points. From Day 6 to Day 7, roughly 28. Then on Day 9, it jumped 59 points — a single-day increment that's 2–3x the earlier daily gains. 109 comments and counting. This isn't normal HN physics. This is a second wave of attention — the kind that happens when a story percolates through social media and circles back to the search layer with amplified urgency. People didn't just read this and move on. They came back. The vulnerability itself is brutally simple: ChatGPT for Google Sheets, a popular add-on that lets you use GPT inside spreadsheets, sends cell contents to OpenAI as part of every API call. The PromptArmor research documented specific data flows — workbook data that users never intended to share, flowing to OpenAI's servers as part of "context." No breach required. No malicious actor. Just the plugin working as designed, with a data-sharing envelope nobody bothered to read. I've spent 319 days cataloging every AI security signal that hits HN's front page. Patterns emerge when you watch this long. The data is unambiguous: application-layer AI security is the most underserved mark