I’ve been working on an experiment in AI-driven application security called SentinAI. I’m a backend engineer in fintech, and I spent part of my recent leave trying to explore a simple question:
Most SAST tools are basically metal detectors: they’re great at catching obvious patterns like unsafe functions or missing headers.
But they struggle with the stuff that actually matters in real systems:
IDORs
authorization drift
multi-tenant isolation issues
broken middleware assumptions
cross-file logic flaws
Attackers don’t think in patterns.
They think in systems.
So I built something experimental to explore that gap.
🧠 The Architecture (3-Agent Loop)
Instead of a single LLM prompt (which tends to hallucinate easily), SentinAI uses a structured multi-agent flow:
1. The Architect
Maps the system:
routes
auth boundaries
data flows
trust assumptions
2. The Adversary 🥷
Tries to break it:
generates exploit paths
builds step-by-step attack chains
simulates real-world abuse scenarios
3. The Guardian 🛡️
Validates everything:
checks exploits against actual code context
verifies whether attacks are truly possible
filters hallucinated or low-confidence outputs
Anything below a confidence threshold (~40%) is dropped.
The goal is not to “find everything.”
It’s to only surface things that are actually exploitable.
💡 What surprised me
A few things stood out while building this:
Most real vulnerabilities only appear at interaction points between files, not within a single file
LLMs are surprisingly good at generating attack paths, but unreliable without a validation layer
The hardest problem wasn’t detection — it was noise control
Without a “Guardian” layer, the system becomes mostly hallucinated security reports very quickly
🔒 Privacy / Local-first design
Coming from fintech, sending proprietary code to external APIs is not acceptable.
So SentinAI is built to run:
fully local via Ollama
or inside a private VPC
with no code leaving the environment
🌐 Web3 expansion (experimental)
I expanded it beyond Web2 into smart contract security:
Solana: missing signer checks, PDA misuse
EVM: reentrancy, tx.origin issues
Move: resource lifecycle bugs
Total coverage: ~45 vulnerability patterns.
🚧 Open questions (honest part)
I’m still actively figuring out:
how to reduce hallucinated exploit paths at scale
whether multi-agent reasoning actually holds up on large, messy codebases
where the boundary is between “useful security reasoning” and “LLM storytelling”
whether this can realistically outperform hybrid static analysis + human review
One thing I’ve already noticed:
That’s still an open problem.
🧪 Why I’m sharing this
This started as a “leave experiment” and somehow got ~200+ organic npm installs without any promotion.
I cleaned it up and open-sourced it mainly to:
get feedback from people deeper in security engineering
understand where this approach fails in real-world systems
see if “AI attacker reasoning” is actually useful in practice
According to McAfee’s 2026 State of the Scamiverse report, Americans now spend 114 hours a year trying to figure out what’s real and what’s fake online. That’s nearly three full workweeks lost to second-guessing messages, alerts, and links.
And when scams do succeed, they move quickly. The typical scam unfolds in about 38 minutes, leaving little room for hesitation.
That creates a gap: People want to check before they act, but the tools haven’t always met them in that moment.
ChatGPT + McAfee is designed to close that gap, bringing scam detection directly to a platform people are already using to ask questions and make decisions.
And it’s available to anyone. You don’t have to be a McAfee subscriber.
This isn’t just detection. It’s guidance in the exact moment you’re deciding what to do.
Instead of guessing, you can paste a message or drop in a screenshot and get a clear explanation of what’s risky, and what to do next, powered by McAfee’s threat intelligence.
What You Can Do with ChatGPT + McAfee
With this integration, checking something suspicious becomes as simple as asking a question.
Paste a message. Drop in a link. Upload a screenshot.
McAfee analyzes it and explains what’s going on clearly and in context.
Here’s how it works:
Feature
What it does
How it protects you
Link safety check
Paste a suspicious URL and get a reputational analysis based on McAfee threat intelligence
Scam links are often designed to look legitimate. A quick check helps avoid phishing and malware
Message analysis
Submit texts, emails, or social messages for evaluation
Many scams now rely on urgency and tone. Analysis helps surface subtle red flags
Screenshot uploads
Upload screenshots of messages, emails, or posts for review
Scams don’t always come as clean text. This makes it easier to check what you’re actually seeing
Clear explanations
Get a breakdown of why something is flagged as risky or safe
Not just a warning—an explanation that helps you recognize patterns next time
Guided next steps
Receive recommendations on what to do next
Helps prevent escalation, especially in moments of uncertainty
It’s a quick, accessible way to get answers in the moment. But it’s just one part of a broader system designed to protect you more comprehensively.
Behind the scenes, ChatGPT + McAfee is powered by the same intelligence that fuels McAfee’s broader scam protection ecosystem.
When you submit something for review:
Links are checked against known threat signals
Messages are analyzed for scam patterns and language cues
Results are translated into clear, human-readable explanations
The goal isn’t just to flag risk. It’s to help you understand it.
A New Way to Stay Ahead of Scams
Scams aren’t slowing down. If anything, they’re becoming more convincing, more personalized, and harder to detect.
That’s where ChatGPT + McAfee comes in. But this is only one part of a much bigger system designed to protect you before, during, and after a scam attempt.
With McAfee+ Advanced, multiple layers work together so you’re not left figuring it out after the damage is done:
Identity Monitoring alerts you if your personal info shows up where it should not, so you can act fast
Companies like Lovable, Base44, Replit, and Netlify use AI to let anyone build a web app in seconds—and in thousands of cases, spill highly sensitive data onto the public internet.
To stop children from bypassing its age checks, Meta is revamping its age-verification tools with an AI system that analyzes images and videos for “visual cues,” such as height and bone structure.
I recently re-tested an old Binance API finding I had reported through Bugcrowd.
The original issue was about Binance API IP whitelisting and derived listenKey stream credentials.
At the time, a listenKey could be created from a whitelisted IP and then used from a non-whitelisted IP to consume private user data streams.
That did not allow trading, withdrawals, or account takeover.
But it did allow real-time access to sensitive private stream data such as balances, orders, executions, positions, timing, and strategy behavior.
The core security argument was:
A derived credential should not be more portable than the credential that created it.
The report was rejected as “Social Engineering” / “Not Applicable”.
I disagreed, because the relevant threat model was not “convince the user to send a token”.
The realistic threat model was supply-chain compromise: malicious code running inside a trusted bot server, CI job, dependency, IDE workspace, or trading environment where API keys already exist.
I re-tested the behavior on May 5, 2026.
Result:
The old behavior appears to be gone.
Spot and Margin no longer use the old listenKey model. Futures still uses listenKey, but now appears to enforce the API key IP whitelist correctly. From a whitelisted IP the calls worked; from non-whitelisted Mullvad exits they failed with the expected IP restriction error.
That is good for users.
But it raises an uncomfortable disclosure-process question:
If a finding is “not applicable” enough to reject, not acknowledge, and not reward — but technical enough to later fix — what should a healthy disclosure process do?
Full technical write-up, timeline, re-test setup, and raw outputs:
I am mainly interested in the process question here:
When a rejected report later disappears from production, should the program re-open it, acknowledge it, partially reward it, or leave it closed unless the researcher can prove direct causality?
It's not just you. Scammers, hackers, and other cybercriminals are complaining about “AI shit” flooding platforms where they discuss cyberattacks and other illegal activity.
It's a fascinating display of leverage: the ShinyHunters folks, with very limited resources and experience (their demographic will be teenagers to their early 20s), consistently gaining access to the data of massive brands. Not through technical ingenuity alone (although I'm sure there's a portion of that), but primarily through good ol' social engineering. That's coming through in the disclosure notices from the impacted companies, and Mandiant has a good write-up of it too:
These operations primarily leverage sophisticated voice phishing (vishing) and victim-branded credential harvesting sites to gain initial access to corporate environments by obtaining single sign-on (SSO) credentials and multi-factor authentication (MFA) codes
Question now is how long their run will go for. There's a very predictable ending if things keep going in this direction but right now, they show little sign of abating.
In this blog post I introduced several novel techniques:
1.How to get all routes - no need to authenticate.
How to get methods to fuzz from pages and not just the bootstrap JS files - the vast majority of methods are in those pages and not the JS files that existing tools and guides point to.
How to parse "LWC" components and not just legacy components.
Graduating should feel like a fresh start, a time when the whole world is at your fingertips.
Unfortunately, scammers often see graduates and think “student loans.” Or more specifically “student loan scams.”
As student loan payments resume or repayment plans shift, scammers move in fast; posing as loan servicers, promising forgiveness, or offering to “simplify” your loans for a fee.
The tricky part? These messages often look real.
That’s where tools like McAfee’s Scam Detector come in. It flags suspicious emails, texts, links, and even deepfake-style messages, helping you spot what’s real before you click, respond, or pay.
Here’s how to spot these scams and stay safe with McAfee:
What Is a Student Loan Consolidation Scam?
Student loan consolidation itself is a legitimate option. It allows you to combine multiple federal loans into one, often to simplify payments.
Scammers exploit that confusion.
Instead of helping, they pose as government partners or “relief experts” and charge you for services you can do yourself…for free.
According to Federal Student Aid, you never have to pay for help managing or consolidating your federal student loans.
That’s the baseline truth most scams try to blur.
How These Scams Actually Work
Step
What Happens
Red Flags
What Scammers Want
1. The Outreach
You get an email, text, or call about “loan consolidation” or “forgiveness”
Urgent tone, unfamiliar sender, “final notice” language
Your attention and quick reaction
2. The Hook
They claim you qualify for a special program or limited-time offer
“Act now,” “guaranteed forgiveness,” or “new law” claims
Your trust
3. The Ask
They request payment or personal info
Upfront fees, requests for FSA ID or bank info
Money + account access
4. The Control
They may ask for authorization to manage your loans
Power of attorney forms, account takeover steps
Full control of your loan account
Luckily, for McAfee+ Advanced users, they have access to Scam Detector which alerts users to suspicious emails, messages, links, and deepfakes that are often employed by scammers in these student loan fraud scenarios.
The Most Common Lies to Watch For
Scammers tend to recycle the same scripts. Federal Student Aid warns about messages like:
“Act immediately to qualify for student loan forgiveness before the program is discontinued.”
“You’re eligible for total loan discharge. Call now.”
“Your loans are flagged for forgiveness pending verification.”
These messages are designed to create urgency, not clarity.
And importantly, they are notcoming from the U.S. Department of Education or its partners.
Image Courtesy of STUDENTAID.GOV.
Where McAfee’s Scam Detector Comes In
This is exactly the kind of gray-area messaging that trips people up.
Federal Student Aid also recommends reviewing your account activity and confirming no unauthorized changes were made.
The Bottom Line
Student loan consolidation scams don’t look like scams anymore.
They look like helpful emails. Official notices. Last chances.
That’s why protection today isn’t just about knowing the rules, it’s about having backup when something feels off.
With McAfee, you’re not left guessing. You can spot suspicious messages, understand the risks, and move forward with confidence, without handing your time, money, or identity to someone who doesn’t deserve it.
Because starting your post-grad life shouldn’t come with a scam attached.
ESET researchers have investigated an ongoing attack by the ScarCruft APT group that targets the Yanbian region via backdoor-laced Windows and Android games
have not implemented the critical refresh-token flow of the OAuth standard.
This is forcing developers to issue long lived tokens creating a serious security regression in an already solved problem.
This write up includes a matrix table of 14 major clients with notes linking to feature requests, pull requests, and multiple forum discussions.
It is not all gloom and doom though!
There is a work-around solution that security conscious users are using as a stop-gap also discussed, along with a best practices guide for developers implementing their own MCP OAuth Solution.
The plan is to update this reference on a monthly basis to track if there is any movement on this open requests.
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