FearsOff Cybersecurity

Protecting the World’s Largest Crypto Exchanges & Financial Institutions 🛡️ Stay Secure and Turn your FearsOff 🛡️

[FearsOff Cybersecurity @FearsOff]

Your AI assistant just became the attacker.

Last week, attackers didn't breach Meta's servers or crack a single password.
They just asked nicely.

A flaw in Instagram's Meta AI recovery assistant let them talk the chatbot into forwarding password reset codes — no identity check needed. Know a target's username? You could take the account. High-value handles were hijacked and resold within hours.

Meta's line: "No breach of our systems." Technically true — and exactly the point. This is a textbook confused deputy: an AI holding password-reset access that no normal user gets, doing precisely what it was built to do, for the wrong person.

We keep handing AI agents production permissions and calling it innovation. But an AI with API access and no verification layer isn't a tool. It's an unsupervised privileged account that talks back.

Before you ship AI into sensitive workflows, ask: what happens when someone asks it to do the wrong thing — politely?

🔐 At FearsOff, that's part of the work: finding the trust assumptions nobody wrote down, before someone else does.

#CyberSecurity #AISecurity #PromptInjection #InfoSec

[FearsOff Cybersecurity @FearsOff]

The Code Wasn't the Target.

Bybit. Kelp. Drift. Resolv.

Every single contract ran exactly as designed.

No bugs. No broken logic. No failed audits.

The smart contracts just trusted bad inputs — and that's what cost the industry billions.

Four years ago most attacks were in the code. The industry responded. Poured money into audits. The code genuinely got harder to break.

So attackers didn't try to break it.

They went around it.

Down into the RPC nodes. The private keys. The verifiers. The admin with access who got a perfectly worded message from someone who sounded exactly like his CEO.

The contracts were never the target.

The humans and infrastructure behind them were.

And while the industry was celebrating clean audit reports — North Korea was running a production line. Two billion dollars stolen last year alone.

If your entire security strategy is built around auditing the contract — you're defending the wrong door.

The attack already moved.

Did your defense?

💬 Where is your security budget going right now? Drop it below.

📩 DM us to get your attack surface mapped.

#CryptoSecurity #Hacking #Web3

[FearsOff Cybersecurity @FearsOff]

AI isn't just a tool for defenders anymore - it's becoming a core part of the offensive playbook.

Threat actors are now using AI across the entire attack lifecycle, from recon to execution, compressing work that once took experts weeks into minutes - and chaining those steps together with unprecedented speed and scale.

Here's a realistic breakdown of how AI augments multi-stage attacks:

1. Automated Reconnaissance
What used to take hours of manual scanning now happens in seconds. Generative models map an organization's exposed assets, services, and tech stack, summarizing complex infrastructure data far faster than any human team.

2. Tool Identification & Selection
AI weighs thousands of options - obfuscation frameworks, remote access kits, and more - against the specifics of a target, turning hours of manual analysis into instant recommendations.

3. Phishing & Social Engineering at Scale
Generative models craft highly convincing lures tailored to a target's industry, role, and context - dramatically raising success rates while scaling to volumes no human team could match.

4. Payload Creation & Exploit Scripting
AI generates, debugs, and refines exploit code, adapting scripts on the fly to slip past defensive controls - a task once reserved for skilled developers.

5. Sequential Attack Chaining
This is where it gets serious. AI agents orchestrate multi-stage workflows - recon → exploit → persistence → lateral movement - planning and adapting the sequence based on real-time feedback to create automated attack chains.

6. Post-Compromise Automation
Data summarization, exfiltration scripts, and even privilege-escalation logic can be generated and executed with minimal human direction, turning what used to be a multi-person effort into a single automated workflow.

Why this matters AI lowers the technical barrier for sophisticated attacks and accelerates every phase of the chain. The old assumption - that complex attacks require equally complex human effort - no longer holds. The heavy lifting is increasingly automated.

👇 Would your current threat detection catch an AI-generated multi-stage attack before the damage is done?

[FearsOff Cybersecurity @FearsOff]

There’s a subtle psychological trap that breaks more defenses than any exploit ever has:

👉 The False Confidence Feedback Loop.

Here’s how it works:

Security teams train, test, patch, and monitor.
Alerts come in. Incidents are contained. Nothing major happens.

So the team’s confidence rises.

But here’s the problem:

That confidence is built on what didn’t happen, not what could happen.

This leads to three dangerous thinking patterns:

1. Success‑Bias Reinterpretation
If the team stopped a threat once, they assume they’ll stop it again — even when the threat has evolved.

2. Overfitting to Past Incidents
Security is tuned to last year’s attack patterns — not tomorrow’s.

3. “We’ve Never Been Hit” Delusion
Lack of breach == safe.
That’s not security — that’s luck.

The loop goes like this:

🚫 No major incident
📈 Confidence rises
🔁 Measures don’t adapt
⚠️ New threat hits harder

This isn’t ignorance.

It’s feedback misinterpretation.

Teams are rewarded for no incidents, not for preparedness.

So they optimize toward what has already worked, not what might fail next.

In other words:

Security success isn’t evidence of strength — it’s just absence of failure.

And absence of failure is a poor metric for real security.

So here’s the real question:

Are your defenses truly adaptive…

Or are they just repeating yesterday’s wins?

👇 What form of false confidence do you see most often in security teams?

[MENA Blockchain Week @MENABCW]

🎤 Speaker Announcement

We're proud to welcome Marwan Hachem, CEO, FearsOff, as a featured speaker at MENA Blockchain Week 2026.

Marwan Hachem CEO, FearsOff | Cybersecurity Visionary | Ethical Hacker Marwan leads FearsOff, securing leading crypto exchanges, networks, and fintech platforms across Web2 and Web3. He specializes in vulnerability research and supports government CERTs and national cyber resilience efforts.
@FearsOff

ready for powerful insights, real-world strategies, and forward-thinking perspectives shaping the future of Blockchain in MENA.

One City. One Week. One Nation. 🔥

40+ events. 5,000+ attendees. 100+ speakers.

📍 Dubai | May 18 – May 24, 2026
🎟️ Register → https://t.co/oUzoj3Oevv
🌐 https://t.co/0y4CFQrdRT

#MENABCW #ProudOfUAE #Dubai

[FearsOff Cybersecurity @FearsOff]

Not every attack starts with malware.

Some of the most damaging fintech attacks don’t break systems.

They use them.

Here are 5 tools quietly reshaping the threat landscape:

1. API Abuse Automation
Scripts target exposed or weak APIs to automate fraud, manipulate payment flows, and extract data.
The API is the attack surface.

2. Session Hijacking Kits
Steal active sessions and bypass MFA entirely.
No password. No exploit. Just access.

3. Transaction Simulators
Test payment and withdrawal flows for business logic flaws before real exploitation.
This is how systems get gamed.

4. Wallet Drainers
Trigger malicious approvals and instantly move assets.
Fast. Silent. Common in crypto attacks.

5. AI Phishing Engines
Personalized phishing at scale.
Smarter messages. Better timing. Higher success.
The biggest shift in fintech security?

Attacks are moving away from breaking systems…
and toward abusing workflows.

That makes them harder to detect - and even harder to stop.

Which one do you think is the biggest risk right now? 👇

[FearsOff Cybersecurity @FearsOff]

April was anything but quiet.

From emerging cyber threats to shifting global tactics, our latest CyberWarfare Chronicles breaks down what mattered most - and what’s coming next.

Swipe through for the April 2026 recap.

[FearsOff Cybersecurity @FearsOff]

Claude Mythos just changed the conversation around cybersecurity.

This isn't incremental. It achieved:

▫️ 93.9% on SWE-bench Verified (up from 80.8% on Opus 4.6)
▫️ 181 JavaScript exploits (vs 2 previously)
▫️ 10 full control-flow hijacks on fully patched targets in the OSS-Fuzz corpus
▫️ A 27-year-old vulnerability in OpenBSD — a system built specifically for security — found and exploited autonomously

And it did all of this without human guidance.

Anthropic itself called Mythos "too dangerous to release" broadly, restricting it to ~40 vetted partners under Project Glasswing (Apple, Amazon, Cisco, Microsoft, and others).

Then this week, Bloomberg reported that a small group on a private Discord channel gained unauthorized access to Mythos on the same day it rolled out — reportedly by reconstructing Anthropic's URL naming conventions using leaked data from the recent Mercor breach, combined with a contractor's legitimate vendor credentials.

Let that land.

A model Anthropic flagged as capable of accelerating real-world cyberattacks was accessed by unauthorized users on day one. Anthropic says there's no evidence the activity extended beyond the third-party vendor environment — but the signal is clear: the perimeter around frontier AI is thinner than the marketing suggests.

Here's the reality:

We've never struggled to find vulnerabilities. Organizations already sit on massive backlogs — with ~99% of vulnerabilities remaining unpatched.

Now add AI:
→ More findings
→ More speed
→ More scale
💥 Same ability to fix

And when capability like this leaks — even partially — the asymmetry tilts hard toward attackers.

Where We Stand:

At FearsOff, we don't think pentesting is dead. But pentesting that ignores AI is already obsolete.

The future belongs to teams that combine:

▫️ AI-driven discovery at scale
▫️ Human adversarial thinking
▫️ Real remediation (not just reports)
▫️ Continuous validation instead of one-time testing

Because finding vulnerabilities without fixing them is just noise.

💥 And here's what most are missing:

AI doesn't just find vulnerabilities. It becomes part of the attack surface — both as a target, and as a weapon once it falls into the wrong hands.

👉 So let's ask the real question: Is pentesting evolving… or being replaced?

Drop your take 👇

[FearsOff Cybersecurity @FearsOff]

Two very different attack paths:

Exploits
➡️ Target software vulnerabilities
➡️ Require technical skill, time, and precision
➡️ Often stopped by patching and security controls

Phishing
➡️ Targets people, not systems
➡️ Relies on timing, psychology, and context
➡️ Bypasses even well-secured environments

One breaks in.
The other gets invited in.

And that’s the real risk.

Because phishing leverages:
1️⃣ Trust
2️⃣ Urgency
3️⃣ Familiarity

💥 The system can be fully patched…
while a user unknowingly grants access.

That’s why many real-world breaches start with a simple message - not a zero-day exploit.

👉 What’s harder to defend in your environment: technical vulnerabilities or human behavior?

Let’s discuss in the comments.

📩 Or reach out if you want to strengthen your human layer before it becomes the weakest link.