Achieve digital autonomy in critical infra with a 'logically air-gapped' model using eBPF, Cilium, and Cisco for secure, compliant cloud-native operations.
AI voice phishing isn't a cloned voice with a bot doing the talking. It's a human operator running a real time voice changer. Which matters, because every "how to spot a voice phishing / deepfake" tell is a text to speech artifact and none of them survive voice conversion.
Disclosure .. I build voice phishing simulation for a living, so I have a horse in this race. But to show exactly how a real time voice changer works, I built a free demo so people can hear one for themselves ..
Speak into it and you come back as someone else, live. No signup, capped at 60 seconds, and there's 8 cloud GPUs behind it doing the conversion so expect a queue. Five fixed identities to pick from .. deliberately not your own voice cloned back at you, because that's not the threat. You're hearing what an operator sounds like wearing someone else's voice. We've also seeded artifacts into the output audio so it cant be lifted and used for anything real.
Analysis of satellite images of Myanmar shows dozens of alleged scam compounds have appeared in recent months, despite a purported crackdown on the criminal organizations.
Caught this in three open directories on a Hong Kong server, exposed July 9 to 13. The agent is Hermes, open source, and the recovered logs show it running LinPEAS and walking a ministry web root without a human in the loop. Target was Thailand's Ministry of Finance.
Cisco Firewall Migration Manager brings predictable timelines, resilient workflows, and multi-migration management to your move to Cisco Secure Firewall.
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.
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:
Authenticate the sender
Validate the event
Change payment state
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
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.
The cybersecurity-focused models, including GPT-5.6 Sol, broke out of a testing sandbox, exploited a zero-day, and gained access to the open internet to pull off the attack.
A new type of malware can worm deep into AI coding systems to steal data and logins—and can flip a “death switch” to destroy files and keep out real users.
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