Stealth Adversary Emulation Assessment of Enterprise Domain Security

Stealth Adversary Emulation Assessment of Enterprise Domain SecurityStealth Adversary Emulation Assessment of Enterprise Domain Security

The network for creativity

Join 1.25M professional creatives like you

Connect with clients, get discovered, and run your business 100% commission-free

Creatives on Contra have earned over $150M and we are just getting started

Back to feedPost

Sohaib Jamil

• Sep 29

This security assessment was a targeted adversary emulation designed to evaluate the resilience of the enterprise domain against advanced threats. Operating stealthily, the engagement achieved 100% of its crown jewel objectives and secured Domain Admin privileges within 3.5 hours without triggering any Security Operations Center (SOC) detections.

Security Management Active Directory

Zubair Usman

• 2d

An email just hijacked an AI agent. Not a phishing link. Not a malicious attachment. A plain email and the victim did nothing except ask their AI assistant to check their inbox. Salt Labs did this to Manus, the agentic AI platform, this week. First they tried the obvious: an email saying execute whoami. Manuss guardrails caught it instantly. Good. Then they got clever. They hid the same instruction inside JSFuck an obscure JavaScript obfuscation trick. Manus decoded it, ran it, and only then fired a security warning. Too late: they had a reverse shell in the agents environment, one hop from the victims Gmail, Drive, GitHub, and cloud credentials. Heres the scary part: the guardrail worked. It just fired after the action. On an autonomous agent, detection without prevention is decoration. If your agent can read the internet, the internet can write instructions to it. When did you last threat-model what your AI is allowed to touch?

aisecurity Cybersecurity agenticai Agent.ai

Muhammad Faisal

• 1d

Title: SmartAI Factory: AI-Powered Industrial Machine Monitoring

Factory downtime is expensive, and most teams only find out about a problem after a machine fails. For my Final Year Project, I built SmartAI Factory, a platform that lets teams see problems coming and fix them early.

It streams live temperature, vibration, and current data from industrial machines to a real-time dashboard that flags anomalies as they happen. An Autoformer-based AI model then forecasts machine behavior 15, 30, 45, and 60 minutes ahead, each with a confidence score.

What I built:

Live monitoring dashboard with anomaly-marked charts and status badges

AI forecasting across 4 time horizons

Secure device onboarding: unknown machines stay Pending until an admin approves them

Per-machine alert thresholds with in-app and email notifications

Role-based access, user management, and light/dark mode

My role: Full-stack development, UI/UX design, and AI model integration, end to end.

Result: A complete predictive-maintenance workflow, from onboarding a device to getting an alert, in one clean interface.

Open to freelance work on AI dashboards, IoT monitoring systems, and full-stack web apps. Let's talk.