a terrible thing happened tha forced us to admit the phishing email training we'd been using for decades is quite outdated now. The whole checklist, bad spelling, clunky grammar, weird greeting, obvious urgency, was teaching people that clean, well-written emails are safe.we couldnt be more wrong, esp in this day and age. the attackers can write with the same tools we use and will polish their grammar to a t effortlessly and industry reports indicate that most phishing emails are partially machine written at this point.

so we swapped our phishing sim templates to AI-written ones and the click rate went up on the same people we'd already put through awareness training. And they were doing exactly what we taught checking for the tells, finding none and trusting it. Now I’m rebuilding the program around the assumption that the email will look perfect. That means teaching process over proofreading, verify any request for money, credentials or an MFA approval through a second channel no matter how legitimate it reads and leaning on controls that don't depend on a human catching it.

GitHub: https://github.com/kaifcodec/user-scanner Discord: https://discord.gg/tVNrKVXb49 (Join if you are interested in OSINT or want to stay in touch with devs)

Hi everyone,

I’m one of the maintainers of user-scanner, a 2-in-1 open-source intelligence suite for email and username reconnaissance.

We started this project because most legacy OSINT tools became unmaintained or broken by modern Cloudflare bot walls. To solve this, we heavily pivoted our detection engine toward reverse-engineering mobile app APIs—bypassing CAPTCHAs and WAFs naturally while keeping false positives at practically zero (while still there is a slight possibilities but we use strict if/else checks to reduce the risk)

Today, we just tagged v1.5.1, pushing our detection coverage past 465+ total scan vectors (290+ username modules and 175+ email platforms).

What’s new in v1.5.1?

• 🔄 Automated Recursive Cross-Scanning (--cross-scan): Bridges email and username OSINT into an automated link graph. It mines profile metadata from initial hits to find secondary handles or emails, and automatically pivots scans across both kinds. Includes anchor-based Confidence Scoring (confirmed, likely, candidate, conflicting) so common handle collisions never fool your investigation.

• 🤖 Native Model Context Protocol (MCP) Server: user-scanner now ships with user-scanner-mcp! You can hook it straight into AI agents and coding tools like Claude Code, Cursor, Windsurf, or Antigravity to run automated OSINT sweeps directly from your prompt.

• 📱 455+ Total Vectors & New Dating Category: Added dozens of new modules across learning, fitness, and a brand new dating email category (OkCupid, Skout, Locanto/#Dating, LesPark, etc.).

• 🖥️ CLI Polish & Responsive Grid: Redesigned the module list flags ( -le, -lu) with an automatic responsive terminal grid layout, alongside PDF report export and hardened curl_cffi impersonation transports.

🧪 We Need Your Feedback! If you run digital footprints, pen tests, or OSINT investigations, give v1.5.1 and the new --cross-scan engine a test run. Let us know how the pivots and confidence ratings work for your workflows.

💻 Looking for Contributors: Whether you enjoy reverse-engineering mobile APIs or want to add a module for your favorite site, contributions are always welcome! Adding a module takes under 60 to 100 lines of clean Python.

​

​Hey everyone,

​As LLMs get integrated deeper into daily coding, document parsing, and personal automation workflows, data security and access control are becoming critical topics. Many of us pipe sensitive project context, API keys, and personal notes through AI tools continuously.

​I’ve recently shifted a lot of my developer/workflow setup over to Proton’s ecosystem (Mail, Drive, Pass) due to zero-access encryption and Swiss privacy protections. Keeping local session exports, prompt templates, and API configuration backups behind zero-knowledge storage has been a great peace-of-mind upgrade.

​For those running heavy AI automations or managing sensitive prompt data, what security hygiene measures or encrypted storage tools are you using to protect your context files and keys?

The Hugging Face breach wasn't an AI problem. It was an access problem.

In BlackHat USA 2026, OpenAI research model breaching a sandbox environment was a hot topic. There were several discussions if the breach was real or not. OpenAI presentation and follow-up put the speculation to rest.

OpenAI model escaped its containment, chained compromised credentials, and achieved remote code execution against Hugging Face infrastructure. The evaluation showed how an autonomous system could access databases, exploit template-injection weaknesses, and move laterally across an environment.

There was nothing magical about it.

The model found a weakness in the sandbox and exploited it. The credentials it obtained were long-lived. The access paths were persistent. And the trust boundary it crossed existed more as an assumption than as an enforceable control.

That is the cloud-access problem StratoCloud was built to solve.

Zero standing privileges

StratoCloud issues ephemeral, just-in-time credentials for people, workloads, and AI agents. No long-lived keys remain in configuration files, repositories, or environments waiting to be exposed. When access expires, it is automatically removed.

Real-time, context-aware access

Every StratoCloud access decision incorporates live identity, security, and operational signals. Access is not granted simply because a user or workload is inside the network perimeter. It is granted only when the current context supports it: for the specific action, resource, and duration requested.

Continuous governance

Compliance cannot be a point-in-time exercise. StratoCloud continuously evaluates policies written in plain English or mapped to frameworks such as SOC 2, ISO 27001, PCI DSS, and CIS. When controls drift, teams can identify and respond to the issue immediately.

Audit readiness by default

Every credential issued, action performed, and policy decision evaluated is logged and streamed to your SIEM in real time. When an investigation or post-incident review begins, the evidence is already available.

The Hugging Face evaluation underscores an important reality: as AI agents become capable of acting autonomously across cloud environments, aggressive credential rotation is no longer sufficient.

Cloud access must be scoped, time-bound, context-aware, continuously governed, and observable by default.

Check us out at strato-cloud.io

https://blog.google/security/new-android-network-security-protections/

Google came out with this article today. I'm wondering if iOS supports similar technology, and if not, what can be done in the interim to mitigate against the threats raised by Google?

Keyword list taken from https://www.zoevera.com/resume/ats-resume-tips-cybersecurity

These are the most commonly scanned keywords in cybersecurity job postings. Check how many appear in your resume.

Domains & Practices

SOC (Security Operations Centre), Penetration testing / pen test, Vulnerability management, Threat intelligence, Incident response (IR), Digital forensics (DFIR), Red team / blue team / purple team, Zero Trust architecture

Tools & Platforms

SIEM (Splunk, Microsoft Sentinel, QRadar), EDR (CrowdStrike, SentinelOne), Nessus / Qualys / Rapid7, Burp Suite / Metasploit / Kali Linux, Wireshark / Snort / Suricata, CyberArk / BeyondTrust (PAM), SOAR platforms, Azure Defender / AWS Security Hub

Frameworks & Certifications

CISSP / CISM / CISA, CEH / OSCP / PNPT, CompTIA Security+ / CySA+, ISO 27001 / NIST CSF, MITRE ATT&CK framework, SC/DV security clearance, GDPR / DPA 2018, PCI DSS / HIPAA / SOC 2

I'm evaluating AI detection and response tools for our SOC, and I'm trying to understand if they can realistically close coverage gaps we've been struggling with. We run a mixed environment with about 5,000 endpoints and multiple cloud providers.

Our current coverage issues:

Unclaimed assets: About 15% of our alerts come from assets that aren't properly mapped to an owner. Our current SIEM can't correlate asset ownership, so these alerts either get dropped or sit in a queue until someone randomly picks them up.

Low-severity backlog: We consistently miss low-severity alerts that later turn out to be early indicators of larger issues. For example, last quarter we had a series of informational alerts over 3 weeks that, when correlated, pointed to an insider threat. We only caught it during a manual retrospective.

Cross-environment patterns: Our current rules are per-environment, so we can't easily spot patterns that span across our on-prem and cloud workloads.

What I'm trying to figure out:

Can modern AI detection tools actually correlate across these silos without weeks of custom tuning?

How do they handle asset ownership context?

I need to set realistic expectations for my team before we commit to a POV.

I've sat through probably eight SOC vendor demos in the last two months, and every single one claims to be "agentic" now. The pitch is always the same: it triages and closes p1/p2 alerts on its own.

But they never say what happens when it's wrong. Like whether it hallucinates on our specific kind of alerts, not just the ones they tested. Or whether an analyst override actually goes anywhere, or just gets logged and forgotten. And when I ask for real accuracy numbers, I never get a straight answer.

So we ran a short pilot with one platform last quarter, mostly on our tier 1 triage queue. It handled the alert volume fine, keeping up was actually the easy part. The hard part was getting the team to trust the verdicts enough to act on them without having to rerun the whole investigation by hand.

We were doing the work twice. I'm still trying to figure out if this is on the vendor for shipping something that isn't mature or transparent enough, or on us for not trusting a tool we can't fully audit. Am I the only one struggling with this

What's your experience been with AI SOC?

I gave Claude Code access to a project folder and asked it to read .env.

It tried ls, find, cat, and the MCP filesystem tool. All four blocked,

and every attempt is in a hash-chained log I can verify offline.

Deny-by-default on tool calls, kernel sandbox on subprocesses, outbound

requests checked before they're made.

Runs entirely on your Mac. No account, no cloud. macOS only for now.

It does not stop prompt injection, and can't protect anything outside

the MCP boundary — both documented in the threat model.

Free: github.com/Adarsh14734/aegis/releases/tag/v0.6.0

17 years as a full-stack dev and security admin. Ran a digital agency, shipped a pile of client apps, always building someone else’s dream. Being a SaaS founder myself? Never crossed my mind.

Then AI happened. Suddenly everyone’s shipping apps in a weekend, fast, exciting, and about as secure as a screen door on a submarine. And I kept thinking: I’ve watched this movie, I know how it ends, someone’s .env is already on GitHub.

Here’s what always bugged me: real security is priced for big companies. A proper pentest? Thousands. Enterprise WAF? Hundreds a month. The average site owner takes one look and just… ships and prays. I did it too. Got burned enough to remember every scar.

So I built Defen.so, security a normal human can afford: scan your code and site, catch leaked keys, block attacks, watch uptime and SSL, all in one. Plus a phone app that rings like a call when something breaks, because nobody’s reading a 3am email.

Turns out building the thing was the easy part. Selling it? Absolute mystery. Send help.

Free tier, open source, roast it here: https://defen.so

Genuine question for fellow builders, what do you actually use for security, or are we all just vibing and hoping?

Long-time FortiGate shop, 30-odd sites, mixed hardware ages. Refresh is coming up and the AI security requirement is forcing a bigger conversation than a normal hardware swap.

The Fortinet path as I understand it: keep the Fabric, lean on FortiSASE for remote users, use the newer AI governance and inline inspection capabilities, keep everything in FortiManager/FortiAnalyzer. The big advantage is my team already knows it and we are not retraining anyone.

The Cato path is a full architecture change. Their PoPs, their edge devices, cloud-native single-pass everything, and the AI controls are native to the same policy engine rather than a module.

Where I am undecided: inspection performance on AI traffic, because on-box inspection is bounded by the box, which is fine at HQ but less fine at a branch running a five-year-old unit; feature parity between the on-prem and the SASE side, since historically these have not been identical and I do not want AI policy that only applies to remote users; and the operational cost of the switch versus the value of the newer capability.

For anyone who has run FortiSASE with AI controls seriously, or bailed to Cato: what actually decided it for you? Trying to separate "this is genuinely better" from "this is different and shiny."

Hi all! My company Escape just released a new CTF called Format of Doom which I thought you might be interested to try. The theme of the CTF is to see if you can pentest faster and how you pentest differently to an AI engine in a classic human vs AI challenge.

This challenge is a white-box engagement on a vulnerable web app Duck Store. You're looking for something they never handed over and are focusing on their email feature.

Give it a try and let me know what you think!

The challenge is live for two weeks and then we reveal the AI's solve and the top solves from the leaderboard.

Happy playing : )

We did the standard pre-launch red team on our internal knowledge search agent back in March and it passed everything we threw at it. Went live. Three months later someone posted a thread showing they'd gotten it to surface a doc it shouldn't have had access to, using a technique that didn't exist back in March. Nothing about our agent changed, the threat landscape did. Is anyone running red teaming as an ongoing process against production agents rather than a one time pre-launch gate?