标签：#SEC

The breach at wearable ring maker Ultrahuman stemmed from credentials stolen from a malware-infected employee laptop.

Hackers appeared to take over victims’ accounts even after Meta said it fixed its AI-powered support chatbot, which granted hackers access to victims’ accounts.

Syndicated from the FavCRM blog . The number that predicts whether an agent is safe to let loose isn't the tool count. When people compare agentic CRMs, they count tools. The number that actually predicts whether an agent is safe to let loose is a different one: annotation coverage . An MCP tool annotation tells the agent what a tool does to the world — whether it reads or mutates, whether it's safe to retry, whether it reaches an external service. Without annotations, the agent is guessing. This is what they are, and why a catalog's annotation coverage matters more than its tool count. What an MCP annotation is Every MCP tool can carry hints alongside its input and output schemas: readOnlyHint — the tool only reads; it changes nothing. Safe to call freely. destructiveHint — the tool mutates or deletes. The agent should confirm before calling. idempotentHint — calling it twice with the same input has the same effect as once. Safe to retry on a timeout. openWorldHint — the tool reaches an external service (sends an email, charges a card), so its effects leave the system. These are not documentation for humans. They are machine-readable signals the agent reasons over before it acts. Why they prevent the worst failures The dangerous class of agent failure is not "the agent couldn't do something." It's "the agent did the wrong destructive thing because it misread an ambiguous instruction." Delete the customer instead of the tag. Refund the wrong invoice. Cancel every booking instead of one. Annotations let the agent self-gate. A well-annotated catalog means the agent calls list_members without ceremony but pauses to confirm before cancel_booking , because one is marked read-only and the other destructive. Pre-MCP function-calling had no equivalent — every tool looked the same to the model, so safety lived entirely in the prompt. Why coverage matters more than count A 190+ tool catalog with 100% annotation coverage is safer than a 30-tool catalog with none. A tool that l

Two rules that can't both be true Event sourcing has one rule: you never delete. You append. The log is the source of truth, and rewriting history is the cardinal sin. GDPR Article 17 has one rule too: when a user asks, you erase their personal data. Not "hide it," not "flag it deleted" — erase it, everywhere, including backups. Put an event-sourced system in front of a privacy regulator and those two rules collide head-on. The user's name, email, and address are baked into CustomerRegistered , AddressChanged , OrderPlaced — dozens of immutable events, replicated to read models, snapshotted, and sitting in every nightly backup you've ever taken. "Just delete the events" breaks event sourcing. "Never delete" breaks the law. Most teams discover this tension after they've committed to append-only. A word on why this isn't academic for me. I build from Germany. Article 17 is EU law — the GDPR, or DSGVO as we call it here — not a German invention, but Germany enforces it about as hard as anywhere in Europe: regional data-protection authorities that issue real fines, and "we were careful" has never been a defense that held up. That pressure is exactly why I wanted erasure to fall out of the architecture instead of being a promise I make to an auditor and then pray I can keep. Why "delete the row" doesn't actually erase anything Say you give in and hard-delete the events for one user. You've still got their data in: every read-model projection rebuilt from those events, every snapshot that rolled them up, every backup taken before the deletion, every replica and every export that already left the building. Chasing personal data across all of those, provably, on a 30-day regulatory clock, is a nightmare — and a single missed backup tape means you didn't comply. Physical deletion doesn't scale to a system designed to keep everything forever. Crypto-shredding: delete the key, not the data The trick is to stop trying to delete the data and instead delete the ability to read it

The Hook: The 48-Hour Re-Infection Nightmare It’s a scenario that keeps e-commerce founders and agency directors awake at night: You wake up to a critical alert that your flagship WordPress site is redirecting users to a spam domain. You immediately deploy a premium security plugin, run a deep scan, quarantine three suspicious files, and breathe a sigh of relief. The scanner gives you a green checkmark. You're safe. Then, exactly 48 hours later, the redirects return. What went wrong? The automated scanner checked the surface, but the attacker had already established a foothold deeper in the architecture. They didn't rely on a loose PHP file in your uploads directory; instead, they weaponized an overlooked, unauthenticated WordPress REST API endpoint to re-inject the payload the moment your scanner turned its back. When high-value enterprise sites are compromised, treating the symptoms with standard security plugins is like putting a band-aid on a structural fracture. To truly remediate a persistent infection, you must think like a forensic analyst, hunt down hidden persistence mechanisms, and harden the application perimeter. The Anatomy of Persistence: Where Malware Hides Modern WordPress malware is sophisticated. Attackers know that standard security tools look for modified core files or rogue scripts in the /wp-content/plugins/ directory. To survive cleanups, they embed themselves into the core infrastructure of your site using three primary vectors: 1. wp-config.php Pre-Loading Attackers frequently inject obfuscated code directly into the top of wp-config.php . Because this file executes before the rest of the WordPress core loads, malware can hook into the initialization process, silently recreating deleted malicious files every time a page is requested. 2. Malicious Must-Use (MU) Plugins Files placed in /wp-content/mu-plugins/ are executed automatically by WordPress and cannot be disabled from the admin dashboard . Attackers love this directory. They will ofte

From a massive DOGE data breach and the hacking of critical energy and water systems to the hack of an FBI surveillance system, here are the most damaging security incidents and data breaches of 2026.

Every detection vendor ships a MITRE ATT&CK heatmap, and every one of them is mostly green. Broad coverage, techniques lit up across the board, a reassuring wall of color in the sales deck and the board slide. It's the universal flex. We cover the matrix. Then you parse the actual rules – the real YAML in the public repo, not the marketing layer on top of it – and the green collapses into three tactics. Everyone covers execution and persistence. Almost nobody covers discovery, lateral movement, or collection. The heatmap wasn't measuring coverage. It was counting rules, and counting them in a way designed to look complete. What a green cell actually means A green cell in a Navigator layer means one thing: at least one rule somewhere references that technique tag. That's it. Not "we detect this reliably." Not "this fires on real attacks and stays quiet otherwise." Not "this survives an attacker who knows the rule exists." One rule that names the technique in its metadata turns the cell green, and forty rules turn it the same shade. Unless the layer is scored by rule count – and most published heatmaps aren't – one and forty are indistinguishable. I've written before that an untested detection isn't a detection, it's a query that runs on a schedule. The heatmap is the same lie one level up. A green matrix isn't coverage. It's a wall of queries that run, rendered in a color that means "present," dressed up as a color that means "protected." The vendor knows the difference. The buyer staring at the green doesn't. You can measure the real shape yourself Here's the part the heatmap marketing depends on you not doing: the rules are public, and you can count them. SigmaHQ, Elastic's detection-rules, Splunk ESCU, Panther, Sublime – all on GitHub, all tagged with attack.TXXXX technique IDs and tactic tags in the rule metadata. The method is boring on purpose. Walk the rule directories. Pull the ATT&CK tags out of each rule. Aggregate by technique, roll the technique counts up

🚀 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.