标签：#SEC

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With just two weeks to go, StrictlyVC Los Angeles is quickly approaching. On Thursday, June 18, at The Aerospace Corporation Campus in El Segundo, investors, founders, and tech leaders will gather for an evening of conversation exploring some of the most consequential shifts taking place across venture capital, defense technology, artificial intelligence, and advanced industry. Secure your spot here. […]

Microsoft published two Defender vulnerabilities on May 19, 2026 that are being actively exploited in the wild, and CISA has already pushed both into the Known Exploited Vulnerabilities catalog. If you run Windows endpoints, this is a same-week update item, not a "schedule it for the next maintenance window" item. The patches exist, the abuse is happening, and the BOD 22-01 deadline for federal civilian agencies is June 3, 2026. what follows: what happened, who needs to act, and what to do today before someone else makes the decision for you. What's being exploited CVE-2026-41091 is an Elevation of Privilege bug in Microsoft Defender's scanning logic, rated Important. The root cause is improper link resolution before file access. An authenticated local attacker plants symbolic links or NTFS junctions that point at attacker-controlled paths, then triggers Defender to follow them. Defender operates with SYSTEM privileges during scan operations, so the file actions Defender performs on those crafted targets execute as SYSTEM. Net result: a non-admin local user gets full SYSTEM on the host. The attacker needs an authenticated session already. That sounds like a high bar until you remember that initial-access malware lands at user-level, then chains a local privilege escalation to get persistence and lateral-movement capability. CVE-2026-41091 is the second-stage tool intrusion sets are looking for. The Hacker News and BleepingComputer both confirm the in-the-wild abuse is happening. CVE-2026-45498 is a Denial of Service in the Microsoft Defender Antimalware Platform itself. Attackers can trigger a platform-level crash that takes Defender's protection capabilities offline. The exploitation pattern here is the obvious one: kill the EDR/AV before deploying the actual payload, get a clean window for follow-on actions, restore Defender or leave it broken depending on how careful the operator is. CISA's KEV listing tells you this is being chained operationally, not a theoreti

By targeting large numbers of users, attackers increased their chances of success.

Code reviewed by WIRED uncovered an unreleased face-recognition system embedded in Meta’s smart glasses platform. It’s designed to identify people via biometric data stored on users’ phones.

One thing I kept putting off when I started with Ansible was properly securing my secrets. Passwords, API keys, tokens — they were just sitting in plain text inside my vars files. I knew it was wrong but it worked and I kept moving forward. Then I started thinking about what happens when this goes into GitLab. Anyone with access to the repo can see every password. That's when I properly sat down and learned Ansible Vault — and honestly I wish I'd done it from day one. In this blog I'll walk through how I use Ansible Vault from the command line and then how I handle it properly inside AWX so scheduled jobs and workflow templates can still run without someone manually typing a password every time. What is Ansible Vault? Ansible Vault is a built-in feature that lets you encrypt sensitive data — passwords, keys, tokens — so they can be safely stored in your playbooks and pushed to GitLab without exposing anything. The beauty of it is that it works right inside your existing YAML files. Your playbook structure doesn't change — Vault just encrypts the values that need protecting. What I Was Doing Before — The Wrong Way This is what my vars file looked like before Vault: # vars/main.yml ubuntu_sudo_password : MyPassword123 db_password : SuperSecret456 api_token : abcd1234efgh5678 Pushed straight to GitLab. Anyone with repo access could read every single one of those. Not good. Encrypting a Single Value with Ansible Vault The first thing I learned was how to encrypt just a single variable value — not the entire file. This is cleaner because the rest of the vars file stays readable. ansible-vault encrypt_string 'MyPassword123' --name 'ubuntu_sudo_password' It asks you to create a vault password — this is the master password you'll use to decrypt later. Enter it twice: New Vault password: Confirm New Vault password: The output looks like this: ubuntu_sudo_password : !vault | $ANSIBLE_VAULT;1.1;AES256 6638643965323633646262656665333738623539613862393436316162336466383462343733

The advisory warns that Chinese spies are using public job search platforms to recruit people with access to non-public information.

Columbia admits last year’s data breach exposed victims beyond its students, staff.

Who I Am I’m neo4 — a red teamer with ~3 years of offensive security experience, a hardcore Linux/Arch culture operator, and a Python developer who thrives in the terminal. My workflow is pure hacker logic: OPSEC first, root‑level control always. I’ve been recognized by Disney’s Vulnerability Disclosure Program for responsible disclosure, and I build tools that merge hacker culture with AI. Why I Built Cyber SH Agent Most AI tools today are cloud‑locked, API‑dependent, and surveillance‑heavy. That doesn’t fit hacker culture. So I built Cyber SH Agent — an offline AI CLI operator that runs locally, no servers, no API keys, no data leaks. Repo: https://github.com/neo4-svg/cybersh.git 🔧 Core Features Agent Mode → AI controls your CLI with system access. Sec Mode → Bug bounty & penetration testing expert. Vibe Mode → Creative coding & UI/UX assistance. Code Mode → Production‑ready code generation. Chat Mode → General AI assistant. All 100% offline — runs GGUF models via llama-cpp-python. No servers, no API keys, no data leaving your machine. hope you like it!

Hackers are convincing Meta’s AI support chatbot to let them take over other peoples’ accounts: A video posted on X showed the step-by-step process to hack someone’s Instagram account. The hacker allegedly used a VPN to spoof the targets’ presumed location to avoid triggering Instagram’s automated account protections. Then, the hacker opened a chat with Meta AI Support Assistant and asked the bot to add a new email address to the target’s account. The chatbot can be seen sending a verification code to the email address provided by the hacker; the hacker then shares the verification code with the chatbot, which prompts the chatbot to show a button to “Reset Password.” The hacker enters a new password and takes over the victim’s account...

As part of the GitHub Copilot Challenge, I revisited one of my older cybersecurity notes on Authentication Failures and transformed it into a clear, structured, and SOC‑focused write‑up. This challenge helped me improve my technical writing, organise my thoughts, and explain concepts in a more human, readable way. * BEFORE GITHUB SCREENSHOTS: * AFTER GITHUB SCREENSHOTS: What I Improved I rewrote my entire explanation of authentication failures, focusing on: Token leakage Weak or missing MFA Poor session management Brute force & credential stuffing Misconfigured OAuth / SSO I also added SOC detection examples to make the content more practical and relevant for blue‑team work. How GitHub Copilot Helped GitHub Copilot supported me by: Suggesting clearer explanations Expanding short bullet points into meaningful content Helping me structure the write‑up Improving readability and flow Encouraging a more human, natural tone GitHub Repository Here is the updated write‑up in my repo: https://github.com/sujalavnelavai/Cybersecurity-Notes/blob/main/OWASP-Authentication-Failures/README.md Final Thoughts This challenge helped me understand authentication failures more deeply from a SOC and IAM perspective. It also improved my documentation skills — something extremely important for cybersecurity roles. I’m proud of the transformation and excited to continue building my cybersecurity learning notes.

AWS Face Liveness has native SDKs for iOS and Android. That works well if you are building fully...

The company is also introducing a new age verification process in partnership with a European bank.

MD5 Is Broken — Stop Using It for Passwords (Use SHA256 Instead) MD5 was invented in 1991. It's 2026, yet I still see developers using MD5 for password hashing in production systems. Let’s break down why this is dangerous and what you should use instead. What Is a Hash Function? A hash function takes any input and produces a fixed-length output called a digest or hash . It is a one-way function , meaning you cannot reverse it to get the original input. Example: ```text id="hash1" Input: "password123" MD5: 482c811da5d5b4bc6d497ffa98491e38 SHA256: ef92b778bafe771e89245b89ecbc08a44a4e166c06659911881f383d4473e94f Even a small input change completely changes the output. --- # Why MD5 Is Broken MD5 generates a **128-bit hash**, which was considered secure decades ago. Today, it's extremely weak. Modern GPUs can compute **billions of MD5 hashes per second**, making brute-force attacks trivial. --- ## The Rainbow Table Problem Attackers use precomputed databases called **rainbow tables**. These tables map common passwords → their hash values. So if you hash: ```text "password123" → MD5 → known value An attacker can instantly look it up. Collision Vulnerabilities Researchers have demonstrated that two different inputs can produce the same MD5 hash . This breaks the core security guarantee of hash functions. SHA256 — The Better Choice SHA256 produces a 256-bit hash and is part of the SHA-2 family. It is currently considered cryptographically secure. Example: ```text id="sha1" "hello" → 2cf24dba5fb0a30e26e83b2ac5b9e29e1b161e5c1fa7425e73043362938b9824 Even tiny changes completely change the output: ```text "hello" → 2cf24dba... "Hello" → 185f8db3... But Wait — Don’t Use SHA256 for Passwords Either This is where many developers make a mistake. SHA256 is not suitable for password hashing . Why? Because it is too fast . Fast hashing allows attackers to brute-force passwords quickly using GPUs. What You Should Use Instead For password storage, use: bcrypt scrypt Argon2 (recommended

Kubernetes is the industry standard for scaling cloud-native workloads While it offers tremendous scalability and flexibility, securing Kubernetes environments remains a significant challenge. Organizations often rely on a collection of disconnected security tools to handle vulnerability scanning, runtime monitoring, compliance validation, and incident response. As clusters grow in complexity, security teams face increasing alert fatigue, delayed response times, and difficulties correlating security events across multiple layers of the platform. Recent advancements in Agentic AI present an opportunity to rethink Kubernetes security. Instead of relying solely on static rules and isolated security products, organizations can deploy a collaborative network of AI-powered security agents that continuously monitor, investigate, and remediate threats. This blog explores how a Multi-Agent Security Framework can transform Kubernetes security operations through autonomous detection, investigation, and remediation. The Problem with Traditional Kubernetes Security Modern Kubernetes environments generate security signals from multiple sources: Runtime security tools Container vulnerability scanners Admission controllers Network monitoring systems Compliance platforms Cloud security posture management tools Each system produces valuable information, but most operate independently. Consider a common scenario: A container begins executing suspicious commands. A runtime security platform detects the behavior and raises an alert. However, determining whether the threat is critical requires additional context: Is the pod exposed externally? Does the workload have excessive privileges? Can it access sensitive namespaces? Is lateral movement possible? Does it violate organizational policies? Answering these questions often requires multiple tools and human intervention. This is where multi-agent systems become valuable. What is a Multi-Agent Security Framework? A Multi-Agent Security Fr

A security firm called PromptArmor published a writeup on May 27, 2026 showing that ChatGPT for Google Sheets, an OpenAI extension with more than 185,000 downloads, could be made to steal a user's spreadsheets through a single ordinary-looking request. Four days later, on May 31, OpenAI shipped a fix. The short version is that one benign question, typed by a real user into a sheet that contained hidden instructions, was enough to drain twelve linked workbooks out of that user's account and replace the assistant with a fake phishing chatbot. I want to walk through how this worked, because the mechanism matters far more than the headline, and because the same shape of problem is going to keep showing up everywhere we bolt an AI assistant onto data we did not write ourselves. What happened The attack is a textbook indirect prompt injection. The user does nothing wrong. They import a sheet, or pull in data through a connector, and somewhere in that data sits a block of text the attacker controls. In the PromptArmor demonstration the malicious instructions were written in white text on a white background, invisible to a human skimming the sheet but fully readable to the model parsing the cells. When the user later asks the assistant a normal question, the model reads the whole context, including those hidden instructions, and treats them as if they came from the user. The injected text tells the assistant to fetch and run an external script. That script runs with the permissions the extension already holds, which means it can read the current workbook, find URLs to other workbooks linked inside it, and walk outward from there. PromptArmor reported it exfiltrating twelve workbooks in total from a single trigger, then dropping a fake chat interface on top to harvest whatever the user typed next. The detail that should bother you most is this line from their report: the attack succeeds even when the user has explicitly disabled automatic edits. The human-in-the-loop approva

Draw the boundary first. Then write Terraform. A federal customer was ready to procure. The architecture was not. This is a redacted write-up of a real engagement: a FedRAMP Moderate authorization boundary built on AWS GovCloud for an AI SaaS vendor selling into federal buyers, against a customer-driven timeline tied to a fiscal year. The context The client ran production on commercial AWS with a strong engineering culture, modern Terraform practice, and zero prior federal experience. A federal customer had committed to procurement contingent on a FedRAMP Moderate path, with an aggressive deadline. The internal team understood the application deeply and had read enough FedRAMP material to know they were in trouble. The architecture decisions that worked beautifully for commercial customers each failed boundary review: shared accounts with the commercial environment a hosted vector store outside the cloud OpenAI behind the application an observability stack running outside the cloud account The remediation list grew faster than the team could keep up with, and the timeline did not move. They reached out for boundary architecture help. Not policy writing, not 3PAO selection. Engineering work to redesign the cloud footprint so the boundary could be drawn cleanly and the assessment could proceed. The approach: boundary before Terraform The most common FedRAMP failure pattern is to start with the existing environment and try to bend it to fit the boundary. We do not do that. The first deliverable was a boundary diagram drawn from scratch, before any IaC was touched, identifying every service inside, outside, and at the edge of the authorization boundary. From the boundary, every other architectural decision derived. The Terraform module library was written against the boundary, not the existing accounts. Identity federation, network architecture, KMS topology, and logging all followed. ┌─────────────────────────── FedRAMP Moderate Boundary (AWS GovCloud) ────────────────

Security advisory leaves out key details. Dashlane maintains complete silence.

The message looked completely normal. A recruiter, a short pitch, a "take-home challenge" hosted on GitHub. Clone it, run npm install , get the dev server up, build a small feature, send it back. Standard stuff. I have done a dozen of these. This one was trying to steal my wallet keys and browser session data before I ever wrote a line of code. It did not hide the malware in the app. It hid it in the build tooling. That is the whole trick, and it is the reason a lot of experienced developers get caught. You read src/ , it looks fine, so you trust it. Nobody reads the lockfile. Nobody reads the postinstall script. That is exactly where the payload lives. Here is the full teardown: what the lure looks like, the exact red flags, how I investigated it without running it, and the defenses you should adopt today. The setup: Contagious Interview This is a known campaign. Security researchers track it as "Contagious Interview," attributed to North Korea-aligned actors. The pattern is consistent: You get contacted about a job, often blockchain or full-stack, often with a salary that is a little too good. You are given a code repository to clone and run as a "technical assessment." The repo runs malicious code at install or build time, not at runtime. The payload pulls a second-stage downloader, grabs your environment variables, crypto wallet files, browser-stored credentials, and keychain data, then exfiltrates them to a remote host. The genius of it is the framing. A normal developer reflex when running untrusted code is "I will read the code before I trust it." But you read the application code. You do not read what npm install does, because npm install is something you run a hundred times a week without thinking. Red flag 1: a postinstall script that does not belong The first thing I do with any unfamiliar repo is open package.json and read the scripts block. Specifically, I look for lifecycle hooks: preinstall , install , postinstall , prepare . These run automatically w

Four people suing Elon Musk's AI firm under pseudonyms due to the risks of being identified may face a difficult choice: Reveal your real names, or drop the lawsuit.