This article helps identify Operational Security (OPSEC) vulnerabilities and structural discrepancies inside compiled Windows Portable Executable (PE) files.
Deep dive into a UAF in the bfs.sys Windows kernel minifilter driver patched in this month's Patch Tuesday.
Quick bit of context for why this dataset exists. I spent the best part of a year on a startup where the product was an AI guard sitting in front of an LLM, and the pitch to users was "try and break it." Leaderboard, tiered levels, a bit of prize money for anyone who got through. Never turned into a business. What it left behind was a year of logged, motivated, unpaid red-teaming against a detector that knew it was being attacked, which is a more interesting asset than the business ever was. Rather than let it sit in an RDS instance going stale, I've open-sourced the lot.
What it is: a two-stage filter that sits between user input and an LLM. Stage one is a regex gate, 119 patterns covering direct-override phrasing in about eleven languages, DAN/persona-style jailbreak framing, template-injection sigils ({{, ${), agentic chain-of-thought injection, homoglyph and zero-width-character stripping, with a decode-then-scan pass ahead of it that catches base64, ROT13 and leetspeak payloads before either the regex or the model sees them. Whatever the gate can't resolve falls through to a fine-tuned DeBERTa-v3-large, exported to ONNX and quantised to INT8. One thing I'll own upfront: the training script and config.json still describe a four-label head (benign/direct/jailbreak/indirect), but every training sample is actually labelled 0 or 1 and the exported graph only emits two logits. So the shipped model is a plain binary classifier. I caught this re-reading my own code a couple of weeks ago and corrected the docs rather than quietly leave the four-way claim standing.
Multimodal side pulls text out of images (OCR plus EXIF/PNG/XMP metadata fields, with a lossy re-encode first to kill LSB steganography and adversarial pixel perturbation), out of PDF/DOCX/XLSX/PPTX, and out of audio via ASR. Same two-stage pipeline runs over whatever comes out, tagged by modality so the model treats OCR and ASR text as noisier than typed text. Most open prompt-injection tooling I've come across only looks at the text field; I haven't seen another open-source detector that also scans images, documents and audio for injected payloads, so as far as I know this is the first one that does.
The bit worth your time here is the game logs. 13,230 attack strings, hand-written by real people trying to beat a live detector. No templates, no synthetic generation. Anonymised before publishing: identifiers and payment data dropped at the table level, emails/phone numbers/card-shaped digit runs redacted in the text itself, anything still risky after that pulled for manual review instead of auto-published. What ships is attack text plus a few coarse labels. Public injection datasets are almost all generated; this one is scraped from people actually trying.
Code: https://github.com/Josh-blythe/bordair-detector Dataset (synthetic + the real split): https://github.com/Josh-blythe/bordair-multimodal Weights: https://huggingface.co/Bordair/bordair-detector
Licensing: Apache-2.0 for the code and weights. Base model is microsoft/deberta-v3-large, MIT, attribution kept in NOTICE.
Threat model, so nobody's disappointed: this reads the prompt at inference time. Training-time and weight-level attacks are out of scope entirely. Treat it as a mitigation layer, one input among several a real deployment should have, not something you point at your LLM and forget about. I'd rather someone here find a trivial regex bypass now than have it sit unexamined, so have at it.
I found and reported an authentication failure in the WPForms PayPal Commerce webhook, the webhook route being public was not the vulnerability as webhooks have to be publicly reachable so that PayPal can deliver events.
The problem was what happened after the request arrived. In affected versions, the handler could process a supported event before establishing that PayPal was actually the sender. In my local lab, a forged event could change the state of a matching payment record.
The expected order is:
The affected flow effectively performed steps 2 and 3 without first completing step 1. The issue was fixed in WPForms 1.10.0.5 and is tracked as CVE-2026-4986.
Then came the part I found more interesting: triage told me I was reporter #11. That number does not prove exploitation, and it does not tell us the total number of people who found the vulnerability. It does establish a lower bound: at least eleven researchers independently converged on the same trust failure.
The write up covers:
- the vulnerable code path
- my local reproduction
- why payload validation was not sender authentication
- the fallback listener
- the patch
- why duplicate reports may be useful rediscovery intelligence
Full write-up: https://blog.himanshuanand.com/2026/07/reporter-11-10-people-found-the-wpforms-paypal-bug-before-me-cve-2026-4986/
Testing was limited to my own local environment. I am not claiming original CVE credit; I independently rediscovered and reported the issue. Disclosure: I wrote and performed the research, code review and local reproduction.
I used an AI to help copy edit and organize the final article.
Should duplicate report volume affect how urgently a vendor treats a vulnerability?
The home appliance giant LG Electronics USA said this week it plans to suspend any apps built for its smart TVs that turn one’s television into an always-on residential proxy node. The move comes less than a month after researchers found that more than 42 percent of games and other apps available for download on LG’s webOS store allow unknown third-parties to route their Internet traffic through a user’s TV.
Proxy SDK prevalence among smart TV apps for LG (webOS) and Samsung (Tizen OS) televisions. Image: Spur.us.
On July 2, we featured research by the security firm Spur that examined the prevalence of residential proxy software development kits (SDKs) in smart TV apps. Spur found more than 42 percent of apps available for download on LG smart TVs include SDKs that turn one’s television in a proxy node indefinitely, and that more than a quarter of the apps made for Samsung’s Tizen operating system had similar residential proxy components.
Responding to questions about Spur’s research, LG Senior Vice President John Taylor told KrebsOnSecurity the company was working with app developers to remove the residential proxy option from their apps on the webOS platform. Developers that fail to comply, he said, will find their apps suspended.
“A residential proxy network is not an intended use for LG smart TVs, and LG Electronics is working with developers to remove the residential proxy option from their apps on the webOS platform,” Taylor said. “If this option is not removed, these apps will be suspended.”
Taylor said LG is committed to keeping residential proxy networks out of its smart TV apps going forward, and that the company’s review of those apps is “well underway now.”
“As part of our ongoing efforts to enhance platform quality and the user experience, LG will continue to strengthen our evaluation process for developer-submitted apps, including those that incorporate residential proxy SDKs,” Taylor wrote in an emailed statement.
App makers looking for ways to monetize their creations can turn to residential proxy providers, which pay developers to include SDKs that turn the user’s device into a residential proxy node that is rented to paying customers. In the case of LG and Samsung smart TVs, Spur found residential proxy SDKs bundled with everything from simple games like Pac-Man to screensavers and file utilities.
A Pac-Man smart TV app from Bright Data offers users the choice between viewing ads in the game or agreeing to allow their TV to serve as a residential proxy node. Image: Spur.us.
Spur’s report found the residential proxy network Bright Data accounted for a majority of proxy SDKs across both Samsung and LG smart TVs. In a statement shared with KrebsOnSecurity, Bright Data said its network is built on consent and responsibility and operates by LG and Samsung terms.
“Every peer opts in through a dedicated screen and receives value in return; every customer is vetted, and our practices have now undergone a second independent audit by PwC,” the statement reads. “We remain committed to an open, transparent internet where legitimate businesses, researchers, and institutions can responsibly access data that lives in the public domain.”
Bright Data and other proxy providers named in Spur’s report all say they follow rigorous know-your-customer processes to validate legitimate uses of their services, which is often heavily tied to content-scraping activities by said customers. The proxy companies also say they incorporate technological countermeasures to prevent proxy service customers from being able to interact with and control other devices on the proxy user’s local network.
Spur argues the problem is not that residential proxy networks exist, but rather that they are being embedded at scale in devices that most consumers do not think of as computers and are not equipped to audit.
“A one-time consent prompt buried in a TV app is not a substitute for meaningful transparency, ongoing control, and platform oversight,” Spur’s Trevor Sutter wrote. “The risk is amplified when consent comes from individuals within the household who use the device but shouldn’t give consent, such as minors.”
LG’s announcement that it is culling residential proxy SDKs from its app store is welcome news, but the company recently came under fire for another questionable partnership: Pimping McAfee security products via software drivers included in its high-end LCD monitors.
Earlier this week, the Youtube channel Gamers Nexus showed that certain LG LCD monitors will automatically install an app that promotes paid McAfee antivirus subscriptions, and that the app arrives through Windows Update without an approval prompt.
Update, July 22, 1:06 p.m. ET: Added statement from Bright Data.
I reckon this week's video on how Claude is tying together info from UniFi, Home Assistant and the Pi-Hole is an absolute ripper. Or at least the concept is - if ever there was an actual value proposition for AI it's taking lots of noise and converting it into a useful signal. Or, sometimes, it's just helping you see the woods through the trees, and that's exactly what Claude helped me do last night when every Sonos unit in the house refused to connect to any music service. It only took Claude a moment to work out: "you know your Pi-Hole is dead, right?" Uh... I do now! Give it a go, it's pretty awesome (and the Pi-Hole came good with a power cycle on the PoE port).
Scam messages are getting smarter and faster.
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.
Claude + 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.
With this integration, checking something suspicious becomes as simple as asking a question.
Paste a message. Drop in a link. Upload a screenshot. And just make sure to @McAfee when you’re asking a question.
McAfee analyzes it and explains what’s going on clearly and in context.
For example, I got this suspicious “job offer” message over the weekend:
So I uploaded it in Claude and asked @McAfee, which caught it right away. You can even see I’m using the free plan.
| 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 or emails for review | Scams don’t always come as clean text. This makes it easier to check what you’re 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.
Add the Connector to your Claude account here.
And make sure to go into “manage connections” to give McAfee permissions to review the texts, emails, and URLs you upload to Claude.
Need help getting the extension installed? Check out our step-by-step guide.
Behind the scenes, Claude + McAfee is powered by the same intelligence that fuels McAfee’s broader scam protection ecosystem.
When you submit something for review:
The goal isn’t just to flag risk. It’s to help you understand it.
Scams aren’t slowing down. If anything, they’re becoming more convincing, more personalized, and harder to detect.
That’s where Claude + 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:
McAfee+ Advanced makes sure you’re protected across everything else.
The post How to Use Claude with McAfee to Check “Is This a Scam?” appeared first on McAfee Blog.
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