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Charlie, one of our readers, has forwarded an interesting phishing email. The email was sent to users of the Vivladi Webmail service. While not overly convincing, the email is likely sufficient to trick a non-empty group of users:

The e-mail gets more interesting as the user clicks on the link. The link points to Google Documents and displays a slide show:

Usually, Google Docs displays a footer notice that warns viewers about phishing sites and offers a "reporting" link if a page is used for phishing. Bots are missing in this case. At first, I suspected some HTML/JavaScript/CSS tricks, but it turns out that this isn't a bug; it is a feature!

Usually, if a user shares slides, the document opens in an "edit" window. This can be avoided by replacing "edit" with "preview" in the URL, but the footer still makes it obvious that this is a set of slides. To remove the footer, the slides have to be "published," and the resulting link must be shared. When publishing, the slides will auto-advance. But for a one-slide slideshow, this isn't an issue. There is also a setting to delay the advance. Here are some sample links:

[These links point to a simple sample presentation I created, not the phishing version.]

Normal Share:

https://docs.google.com/presentation/d/1Quzd6bbuPlIcTOorlUDzSuJCXiOyqBTSHczo6hnXcac/edit?usp=sharing

Preview Share:

https://docs.google.com/presentation/d/1Quzd6bbuPlIcTOorlUDzSuJCXiOyqBTSHczo6hnXcac/preview?usp=sharing

Publish Share:

https://docs.google.com/presentation/d/e/2PACX-1vRaoBusJAaIoVcNbGsfVyE0OuTP1dS-2Po9lpAN9GGy2EkbZG_oR9maZDS7cq2xW_QeiF8he457hq3_/pub?start=false&loop=false&delayms=30000

The URL parameters in the last link do not start the presentations, nor loop them, and delay the next slide by 30 seconds.

The Vivaldi webmail phishing ended up on a "classic" phishing login form that was created using Square. So far, this form is still visible at

hxxps [:] //vivaldiwebmailaccountsservices[.]weeblysite[.]com

???????

 

 

—
Johannes B. Ullrich, Ph.D. , Dean of Research, SANS.edu
Twitter|

(c) SANS Internet Storm Center. https://isc.sans.edu Creative Commons Attribution-Noncommercial 3.0 United States License.

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I was looking for possible exploitation of CVE-2026-21962, a recently patched WebLogic vulnerability. While looking for related exploit attempts in our data, I came across the following request:

GET /weblogic//weblogic/..;/bea_wls_internal/ProxyServlet
host: 71.126.165.182
user-agent: Mozilla/5.0 (compatible; Exploit/1.0)
accept-encoding: gzip, deflate
accept: */*
connection: close
wl-proxy-client-ip: 127.0.0.1;Y21kOndob2FtaQ==
proxy-client-ip: 127.0.0.1;Y21kOndob2FtaQ==
x-forwarded-for: 127.0.0.1;Y21kOndob2FtaQ==

According to write-ups about CVE-2026-21962, this request is related [2]. However, the vulnerability also matched an earlier "AI Slop" PoC [3][4]. Another write-up, that also sounds very AI-influenced, suggests a very different exploit mechanism that does not match the request above [5].

The source IP is 193.24.123.42. Our data shows sporadic HTTP scans for this IP address, and it appears to be located in Russia. Not terribly remarkable at that. In the past, the IP has used the "Claudbot" user-agent. But it does not have any actual affiliation with Anthropic (not to be confused with the recent news about clawdbot). 

The exploit is a bit odd. First of all, it does use the loopback address as an "X-Forwarded-For" address. This is a common trick to bypass access restrictions (I would think that Oracle is a bit better than to fall for a simple issue like that). There is an option to list multiple IPs, but they should be delimited by a comma, not a semicolon. 

The base64 encoded string decodes to: "cmd:whoami". This suggests a simple command injection vulnerability. Possibly, the content of the header is base64 decoded and next, passed as a command line argument?? Certainly an odd mix of encodings in one header, and unlikely to work.

Let's hope this is AI slop and the exploit isn't that easy. We have seen a significant uptick in requests, including the wl-proxy-client-ip header, starting on January 21st, but the header has been used before. It is a typical exploit AI may come up with, seeing keywords like "Weblogic Server Proxy Plug-in".

I asked ChatGPT and Grok if this is an exploit or AI slop. The abbreviated answer:

ChatGPT: "This looks more like a “scanner/probe that’s trying to look like an exploit” than a complete, working exploit by itself — but it’s not random either. It’s borrowing real WebLogic attack ingredients."

Grok: "This is an actual exploit attempt — not just random "AI slop" or nonsense traffic."

​​​​​​​Google Gemini: "That is definitely an actual exploit attempt, not AI slop. Specifically, it is targeting a well-known vulnerability in Oracle WebLogic Server."

[1] https://nvd.nist.gov/vuln/detail/CVE-2026-21962
[2] https://dbugs.ptsecurity.com/vulnerability/PT-2026-3709
[3] https://x.com/0xacb/status/2015473216844620280
[4] https://github.com/Ashwesker/Ashwesker-CVE-2026-21962/blob/main/CVE-2026-21962.py
[5] https://www.penligent.ai/hackinglabs/the-ghost-in-the-middle-a-definitive-technical-analysis-of-cve-2026-21962-and-its-existential-threat-to-ai-pipelines/

—
Johannes B. Ullrich, Ph.D. , Dean of Research, SANS.edu
Twitter|

(c) SANS Internet Storm Center. https://isc.sans.edu Creative Commons Attribution-Noncommercial 3.0 United States License.

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[This is a Guest Diary by Fares Azhari, an ISC intern as part of the SANS.edu BACS program]

Romance scams are a form of social-engineering fraud that causes both financial and emotional harm. They vary in technique and platform, but most follow the same high-level roadmap: initial contact, relationship building, financial exploitation. In this blog post I focus on the initial stages of the romance scam ? how scammers make contact, build rapport, and prime victims for later financial requests.

I was contacted by two separate romance scammers on WhatsApp. I acted like a victim falling for their scam and spent around two weeks texting each one. This allowed me to observe the first few phases, which we discuss below. I was not able to reach the monetization phase, as that often takes months and I could not maintain the daily time investment needed to convince the scammers I was fully falling for it.

The scammers claimed to be called ?Chloe? and ?Verna?. We use these names throughout to differentiate their messages. Snippets from each are included to illustrate the phases, along with my precursor or response messages.

Phase 1: Initial contact

Both conversations began the same way ? the sender claimed they had messaged the wrong person.

Verna:

Chloe:

That ?wrong-number? ruse is low effort and high reward. It gives the out-of-the-blue message a plausible reason, invites a short helpful reply, and lowers suspicion. Two small but useful fingerprints appear immediately: random capitalization and awkward grammar. These recur later and help identify when different operators are involved.

Phase 2: The immediate hook

If you reply politely, the scammer usually responds with an over-the-top compliment:

Verna:

Chloe:

These short flattering lines serve as rapid rapport builders ? they feel personal and disarming.

Phase 3: Establishing identity and credibility

After a few messages, both claimed to be foreigners working in the UK:

Verna:

When asked what she does for a living:

When asked to explain her job:

Chloe:

When asked how COVID affected her life:

When asked about her job:

When asked what made her choose business:

Both claim the same job ? Business Analyst ? which later supports credibility when discussing investments. Claiming to be foreigners explains grammatical errors and factual mistakes about the UK. Notably, job descriptions are long and well-written, lacking earlier quirks ? suggesting prewritten, copy-pasted content. This points to a playbook: flatter the target, establish credibility with occupation and location cover, then use scripted replies where legitimacy matters.

Phase 4: The hand-off

After a few days of texting, both explained they were using a business number and asked to move to a ?personal? one:

Verna:

After I said it didn?t bother me to switch:

Chloe:

After the switch:

The excuse is plausible and low-friction. Once texting the new number, writing style often changes ? a strong sign of a hand-off to a different operator or team focused on long-term grooming.

Phase 5: The grooming phase (signs of a different operator)

The writing style shift is clear on the new numbers:

Verna:

When asked if she made friends at work:

When asked to share a steak recipe:

Chloe:

When asked what languages she speaks:

When asked about her studies:

When asked about work stress:

Responses show weaker English: more basic grammar errors, shorter sentences, quicker replies, daily ?Good morning? routines, and frequent (likely stolen or AI-generated) photos. These changes strongly indicate a hand-off.

Phase 6: Credibility building

By the second week both began describing financial success and sent images of cars, apartments, gym visits, and meals to build trust:

Verna:

Pictures sent when asked about her side hustle:

When asked if investments are high risk:

When asked how she chooses investments:

Photo sent saying she finished work (face covered):

Chloe:

When asked about plans for her 30s:

When asked about foundations/programs:

Property photo (Australia):

Both positioned themselves as successful investors with diversified portfolios ? building trust for future proposals. The wealth, charity, and expertise narratives emotionally prime the target. Direct money requests usually come much later, after deep emotional commitment.

Practical advice for readers

• If you receive a random ?wrong number? message, be cautious ? do not share personal information.
• Be suspicious if someone quickly asks to move off-platform or to a new number. Stay on the original platform until identity is verified.
• Ask for a live video call ? repeated refusal is a major red flag.
• Reverse-image search any profile photos or images received.
• Never send money, gift cards, or personal documents to someone you only know online.

(c) SANS Internet Storm Center. https://isc.sans.edu Creative Commons Attribution-Noncommercial 3.0 United States License.

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Visual Studio Code is a popular open-source code editor[1]. But it’s much more than a simple editor, it’s a complete development platform that supports many languages and it is available on multiple platforms. Used by developers worldwide, it’s a juicy target for threat actors because it can be extended with extensions.

Of course, it became a new playground for bad guys and malicious extensions were already discovered multiple times, like the 'Dracula Official' theme[2]. Their modus-operandi is always the same: they take the legitimate extension and include scripts that perform malicious actions.

VSCode has also many features that help developers in their day to day job. One of them is the execution of automatic tasks on specific events. Think about the automatic macro execution in Microsoft Office.

With VSCode, it’s easy to implement and it’s based on a simple JSON file. Create in your project directory a sub-directory ".vscode" and, inside this one, create a “tasks.json”. Here is an example:

PS C:tempMyProject> cat ..vscodetasks.json
{
  "version": "2.0.0",
  "tasks": [
    {
      "label": “ISC PoC,
      "type": "shell",
      "command": "powershell",
      "args": [
        "-NoProfile",
        "-ExecutionPolicy", "Bypass",
        "-EncodedCommand",
      "QQBkAGQALQBUAHkAcABlACAALQBBAHMAcwBlAG0AYgBsAHkATgBhAG0AZQAgAFAAcgBlAHMAZQBuAHQAYQB0AGkAbwBuAEYAcgBhAG0AZQB3AG8AcgBrADsAIABbAFMAeQBzAHQAZQBtAC4AVwBpAG4AZABvAHcAcwAuAE0AZQBzAHMAYQBnAGUAQgBvAHgAXQA6ADoAUwBoAG8AdwAoACcASQAgAGEAbQAgAG4AbwB0ACAAbQBhAGwAaQBjAGkAbwB1AHMAIQAgAH0AOgAtAD4AJwAsACAAJwBJAFMAQwAgAFAAbwBDACcAKQAgAHwAIABPAHUAdAAtAE4AdQBsAGwA"
      ],
      "problemMatcher": [],
      "runOptions": {
        "runOn": "folderOpen"
      },
    }
  ]
}

The key element in this JSON file is the "runOn" method: The script will be triggered when the folder will be opened by VSCode.

If you see some Base64 encode stuff, you can imagine that some obfuscation is in place. Now, launch VSCode from the project directory and you should see this:

The Base64 data is just this code:

Add-Type -AssemblyName PresentationFramework; [System.Windows.MessageBox]::Show('I am not malicious! }:->', 'ISC PoC') | Out-Null

This technique has already been implemented by some threat actors![3]!

Be careful if you see some unexpected ".vscode" directories!

[1] https://code.visualstudio.com
[2] https://www.bleepingcomputer.com/news/security/malicious-vscode-extensions-with-millions-of-installs-discovered/
[3] https://redasgard.com/blog/hunting-lazarus-contagious-interview-c2-infrastructure

Xavier Mertens (@xme)
Xameco
Senior ISC Handler – Freelance Cyber Security Consultant
PGP Key

(c) SANS Internet Storm Center. https://isc.sans.edu Creative Commons Attribution-Noncommercial 3.0 United States License.

This post was originally published on this site

IDNs or “International Domain Names” have been with us for a while now (see RFC3490[1]). They are (ab)used in many attack scenarios because.. it works! Who can immediately spot the difference between:

https://youtube.com/

And:

https://youtube.com/

The magic is to replace classic characters by others that look almost the same. In the example above, the letter “o” has been replaced by Greek character “o”.

If they are very efficient for attackers, they remain below the radar in many organizations. To avoid issues when printing unusual characters, Punycode[2] helps to encode them in plain characters. The example above will be encoded as:

xn--yutube-wqf.com

This format is based on:

• “xn--“ : the common prefix for all IDNs requests.
• “yutube.com”: The normal ASCII characters
• “wqf” : The Punycode encoded version of the Unicode character

Python can decode them easily:

$ python3
Python 3.12.3 (main, Jan  8 2026, 11:30:50) [GCC 13.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> domain = "xn--yutube-wqf.com"
>>> decoded = domain.encode("ascii").decode("idna")
>>> print(decoded)
y?utube.com
>>> for c in decoded:
...     print(f"{c} -> {ord(c)}")
...
y -> 121
? -> 1086
u -> 117
t -> 116
u -> 117
b -> 98
e -> 101
. -> 46
c -> 99
o -> 111
m -> 109
>>>

You can see the value of “o” is not “usual” (not in the ASCII range). They are plenty of online tools that can (de|en)code Punycode[3].

If not all IDNs are suspicious, they are not very common and deserve some searches in your logs. If you already collect your DNS resolver logs (I hope you do!), it’s easy to search for such domains:

$ grep "xn--" queries.log*
queries.log:19-Jan-2026 19:54:38.399 queries: info: client @0x999999999999 192.168.255.13#47099 (in.xn--b1akcbzf.xn--90amc.xn--p1acf): query: in.xn--b1akcbzf.xn--90amc.xn--p1acf IN A +E(0) (192.168.254.8)
queries.log:20-Jan-2026 04:38:25.877 queries: info: client @0x999999999999 192.168.255.13#49850 (in.xn--b1akcbzf.xn--90amc.xn--p1acf): query: in.xn--b1akcbzf.xn--90amc.xn--p1acf IN A +E(0) (192.168.254.8)
queries.log.0:18-Jan-2026 15:22:11.741 queries: info: client @0x9999999999 192.168.255.13#60763 (in.xn--b1akcbzf.xn--90amc.xn--p1acf): query: in.xn--b1akcbzf.xn--90amc.xn--p1acf IN A +E(0) (192.168.254.8)
queries.log.0:18-Jan-2026 17:27:23.127 queries: info: client @0x99999999999 192.168.255.13#44141 (in.xn--b1akcbzf.xn--90amc.xn--p1acf): query: in.xn--b1akcbzf.xn--90amc.xn--p1acf IN A +E(0) (192.168.254.8)
queries.log.0:18-Jan-2026 22:54:36.841 queries: info: client @0x99999999999 192.168.255.13#35963 (in.xn--b1akcbzf.xn--90amc.xn--p1acf): query: in.xn--b1akcbzf.xn--90amc.xn--p1acf IN A +E(0) (192.168.254.8)

The detected Punycode domain is decoded to: 

Another good proof that DNS is a goldmine for threat hunting!

[1] https://datatracker.ietf.org/doc/html/rfc3490
[2] https://en.wikipedia.org/wiki/Punycode
[3] https://regery.com/en/domains/tools/punycode-decoder

Xavier Mertens (@xme)
Xameco
Senior ISC Handler – Freelance Cyber Security Consultant
PGP Key

(c) SANS Internet Storm Center. https://isc.sans.edu Creative Commons Attribution-Noncommercial 3.0 United States License.