Exposed Server Reveals AI-Assisted Phishing Toolkit Behind WebDAV Malware Campaign 31%

By Swati Khandelwal12%

7/20/2026, 5:29:00 PM

BS Summary: This article contains 26 faulty reasoning types, including Confirmation Bias, Appeal to Authority, and Overconfidence Bias, with Ambiguity (Equivocation) as the most egregious example at 16.6% saturation with 163 hits. Analysis detected 1,331 faulty-reasoning hits from 984 analyzed words, generating a BS Score of 40.6% and a BS Rank of 31% (14,664 of 21,167 articles). This article is better (less manipulative) than 69.30% of the article peer group.

A malware operator left its delivery server wide open, and Rapid7 pulled down the whole toolkit: 1,048 files spanning lure templates, filename-spoofing tests, execution experiments, droppers, builder notes, and two campaign chains. 
One was already live against Windows users in Mexico, delivering an infostealer through a fake government ID-lookup site over WebDAV. 
What makes it more than a payload dump: it caught the operation mid-build. 
Testing notes, failed experiments, documentation, and live delivery logs sat in one place, the kind of complete development trail defenders rarely see. 
Rapid7 reads the artifacts, down to a hardcoded path pointing at an open-source AI coding tool, as an operator using generative AI to produce, test, and document phishing delivery at speed. 
The most developed test set focused on CVE-2025-33053 (CVSS 8.8, now in CISA's KEV catalog), the WebDAV working-directory hijack Check Point documented last year in its Stealth Falcon reporting. 
The operator appeared to be reproducing it. 
The technique abuses a .url shortcut to launch a legitimate signed Windows binary while pointing its working directory at an attacker-controlled WebDAV share. 
In the original attack, the shortcut launched iediagcmd.exe, an Internet Explorer diagnostics tool that starts helpers like route.exe by bare filename; with the working directory pointed at the remote share, Windows loads the attacker's route.exe from WebDAV instead of the real one in System32. 
The operator's own README claims this runs with no SmartScreen or Mark-of-the-Web warning, "WITHOUT any security warnings. 
Zero alerts!" 
Microsoft patched the flaw in June 2025. 
The notes mirror Check Point's writeup closely enough that one recovered README preserved the exact summerartcamp[.]net@ssl@443\DavWWWRoot\OSYxaOjr example path from the original report. 
Then the operator scaled the testing. 
One "comprehensive test kit" expanded the single technique into 59 .url files aimed at other signed binaries: . 
NET tools like InstallUtil and RegAsm, LOLBAS entries, even UAC-bypass candidates, each with a written theory of why the hijack should work and a tiered testing order. 
The notes treat these as candidates to probe one by one, not confirmed hijacks, and the operator built the set for a concrete reason: the original trick breaks on Windows 11 24H2, where Internet Explorer, and so iediagcmd.exe, is gone. 
The directory also held smaller test sets for two other file-handling flaws, the MSHTML bypass CVE-2026-21513 and the NTLM-leak CVE-2025-24054, but the WebDAV hijack was the main event. 
The tell is in the paperwork. 
Rapid7 says the READMEs, lure-generation guides, matrix-style test write-ups, and a _MAPPING.csv tying each test file to its target binary carry the templated formatting, verbosity, and emoji-heavy structure it associates with LLM output. 
It reads the phishing site's emoji-laden JavaScript the same way. 
The Russian comments and folder names, one called testik (a diminutive of "test"), place the operator in a Russian-speaking context but don't identify them. 
Rapid7 attributes the operation to an LLM-assisted workflow, likely built with help from Coderrr, which it renders "CodeRRR." 
The Hacker News confirmed the repository is public as of July 20, 2026: a general-purpose, open-source AI coding agent inspired by Claude Code, GitHub Copilot CLI, and Cursor, not attacker-specific tooling. 
Rapid7's summary is blunt: "the attacker used LLMs to operate more like a modern software product team." 
The operator even left the delivery panel, an admin tool called Simba Service, sitting on the same server with its default port and credentials unchanged. 
An active campaign targeting Mexican users 
The MDR alert traced back to gobf[.]mx, a typosquat of the government's CURP national-ID lookup, which served victims a fake record-retrieval page whose download button fired a search-ms: query. 
That opened the operator's WebDAV share as a Windows Explorer search filtered to .scr files. 
The most-delivered lure looked like a CURP PDF report but was a .scr executable, its filename flipped with a right-to-left override to read as a PDF. 
It was an Inno Setup installer that unpacked a loader and ran a . 
NET infostealer entirely in memory, hollowed into a signed Qihoo 360 process. 
The stealer grabbed cryptocurrency wallets, browser credentials, session cookies, and Telegram sessions. 
A second campaign directory, DlrtyGames, took a different route, sideloading a trojanized DLL through a signed Ubisoft binary to drop a modular . 
NET RAT. 
Over roughly 5.5 days (June 20 to 26, 2026 UTC), the delivery panel logged 77,098 requests from 3,892 unique IPs across 101 countries, with Mexico alone driving 82.5% of traffic and 96.9% of launch activity. 
A single CURP lure accounted for 2,384 of the 2,441 launch events, about 97.7%. 
That figure measures delivery reach, not infections: Rapid7 counts a "launch event" when the panel sees a client request or opens an executable from the share, not a confirmed run on an endpoint, and the traffic from the US and Germany looked more like scanning than victims. 
The activity also clustered in Mexican working hours, consistent with real users rather than automated scanners. 
For defenders, the June 2025 patch closed the original iediagcmd.exe path, but the 59-file kit shows the operator hunting other signed binaries that behave the same way. 
Rapid7 has published indicators for both campaigns, including C2 addresses and file hashes, on its GitHub; block those first. 
For what the IOCs miss, watch the behavior the alert first caught: the WebClient service starting and davclnt.dll reaching a remote host, a signed binary spawning a child whose image path sits on a WebDAV or UNC share, and filenames using RTLO (U+202E), double extensions, or padding before .exe or .scr. 
The Hacker News has reached out to Rapid7 for clarification on the final payload identification and the current status of the exposed infrastructure, and will update this story with any response. 
The delivery burst was short-lived, cooling after June 24. 
What lasts is the method: an operator wired commodity AI coding tools, never built for the job, into a repeatable pipeline for producing and testing phishing delivery, ready to point at the next target. 
Article reasoning-pattern comparisonThis article: 14.5%Swati Khandelwal: 2.5%The Hacker News: 2.0%Confirmation Bias14.5%This article: 0.0%Swati Khandelwal: 1.5%The Hacker News: 1.2%Anchoring Bias0.0%This article: 2.2%Swati Khandelwal: 3.6%The Hacker News: 3.3%Availability Heuristic2.2%This article: 7.7%Swati Khandelwal: 1.4%The Hacker News: 1.4%Representativeness Heuristic7.7%This article: 4.3%Swati Khandelwal: 0.6%The Hacker News: 0.6%Hindsight Bias4.3%This article: 9.0%Swati Khandelwal: 2.5%The Hacker News: 2.5%Overconfidence Bias9.0%This article: 7.9%Swati Khandelwal: 2.9%The Hacker News: 2.9%Framing Effect7.9%This article: 1.9%Swati Khandelwal: 0.9%The Hacker News: 1.1%Loss Aversion1.9%This article: 0.0%Swati Khandelwal: 0.6%The Hacker News: 0.6%Status Quo Bias0.0%This article: 0.0%Swati Khandelwal: 0.1%The Hacker News: 0.1%Sunk Cost Effect0.0%This article: 0.0%Swati Khandelwal: 1.2%The Hacker News: 1.3%Optimism Bias0.0%This article: 3.5%Swati Khandelwal: 1.9%The Hacker News: 1.6%Pessimism Bias3.5%This article: 5.9%Swati Khandelwal: 6.5%The Hacker News: 6.8%Negativity Bias5.9%This article: 0.0%Swati Khandelwal: 0.4%The Hacker News: 0.8%Self-Serving Bias0.0%This article: 2.5%Swati Khandelwal: 0.4%The Hacker News: 0.4%Fundamental Attribution Error2.5%This article: 0.0%Swati Khandelwal: 0.1%The Hacker News: 0.1%Actor-Observer Bias0.0%This article: 0.0%Swati Khandelwal: 0.1%The Hacker News: 0.1%In-Group Bias0.0%This article: 2.4%Swati Khandelwal: 0.1%The Hacker News: 0.4%Out-Group Homogeneity Bias2.4%This article: 2.4%Swati Khandelwal: 0.4%The Hacker News: 0.6%Halo Effect2.4%This article: 0.0%Swati Khandelwal: 0.0%The Hacker News: 0.0%Horn Effect0.0%This article: 0.0%Swati Khandelwal: 0.0%The Hacker News: 0.0%Dunning-Kruger Effect0.0%This article: 6.7%Swati Khandelwal: 1.4%The Hacker News: 1.4%Recency Bias6.7%This article: 0.0%Swati Khandelwal: 0.2%The Hacker News: 0.2%Primacy Effect0.0%This article: 0.0%Swati Khandelwal: 0.1%The Hacker News: 0.1%Blind-Spot Bias0.0%This article: 0.0%Swati Khandelwal: 0.1%The Hacker News: 0.1%Ad Hominem0.0%This article: 0.0%Swati Khandelwal: 0.0%The Hacker News: 0.1%Straw Man0.0%This article: 10.2%Swati Khandelwal: 3.9%The Hacker News: 4.0%Appeal to Authority10.2%This article: 3.2%Swati Khandelwal: 1.4%The Hacker News: 1.6%False Dilemma3.2%This article: 3.5%Swati Khandelwal: 0.7%The Hacker News: 0.5%Slippery Slope3.5%This article: 0.0%Swati Khandelwal: 0.1%The Hacker News: 0.1%Circular Reasoning0.0%This article: 3.7%Swati Khandelwal: 3.8%The Hacker News: 4.3%Hasty Generalization3.7%This article: 0.0%Swati Khandelwal: 0.2%The Hacker News: 0.1%Red Herring0.0%This article: 0.0%Swati Khandelwal: 0.1%The Hacker News: 0.2%Bandwagon0.0%This article: 1.9%Swati Khandelwal: 0.9%The Hacker News: 1.1%Appeal to Emotion1.9%This article: 0.6%Swati Khandelwal: 0.3%The Hacker News: 0.5%Begging the Question0.6%This article: 6.4%Swati Khandelwal: 2.0%The Hacker News: 1.9%Post Hoc (False Cause)6.4%This article: 0.0%Swati Khandelwal: 0.0%The Hacker News: 0.0%Tu Quoque0.0%This article: 1.9%Swati Khandelwal: 0.7%The Hacker News: 0.6%Burden of Proof1.9%This article: 0.0%Swati Khandelwal: 0.1%The Hacker News: 0.1%Appeal to Nature0.0%This article: 0.0%Swati Khandelwal: 0.3%The Hacker News: 0.3%Composition/Division0.0%This article: 0.0%Swati Khandelwal: 1.1%The Hacker News: 1.0%Anecdotal0.0%This article: 0.0%Swati Khandelwal: 0.0%The Hacker News: 0.1%No True Scotsman0.0%This article: 16.6%Swati Khandelwal: 2.6%The Hacker News: 2.3%Ambiguity (Equivocation)16.6%This article: 0.0%Swati Khandelwal: 0.0%The Hacker News: 0.0%Gambler’s Fallacy0.0%This article: 0.0%Swati Khandelwal: 0.0%The Hacker News: 0.0%Middle Ground0.0%This article: 0.0%Swati Khandelwal: 0.0%The Hacker News: 0.0%Personal Incredulity0.0%This article: 0.0%Swati Khandelwal: 0.1%The Hacker News: 0.1%Special Pleading0.0%This article: 0.0%Swati Khandelwal: 0.1%The Hacker News: 0.1%Genetic Fallacy0.0%This article: 1.7%Swati Khandelwal: 1.1%The Hacker News: 1.4%Unattributed Quote1.7%This article: 3.7%Swati Khandelwal: 0.8%The Hacker News: 1.0%Quote-first Misdirection3.7%This article: 3.8%Swati Khandelwal: 2.8%The Hacker News: 2.4%Biased Writer Voice3.8%This article: 7.1%Swati Khandelwal: 4.8%The Hacker News: 4.4%Indoctrination7.1%This article: 0.0%Swati Khandelwal: 0.0%The Hacker News: 0.0%Politically Left Leaning Bias0.0%This article: 0.0%Swati Khandelwal: 0.0%The Hacker News: 0.0%Politically Right Leaning Bias0.0%This article: 0.0%Swati Khandelwal: 0.4%The Hacker News: 3.0%Attempt to Sell a Product or S…0.0%

984 words analyzed.

Speakers

2speakers19%attributed speech794writer words
Voice mapSelect a segment to jump to its words
Writer's voice • 10 words • 0.0% coverageWriter's voice • 32 words • 0.0% coverageWriter's voice • 20 words • 0.0% coverageWriter's voice • 13 words • 0.0% coverageWriter's voice • 22 words • 0.0% coverageRapid7 • 31 words • 100.0% coverageWriter's voice • 29 words • 0.0% coverageWriter's voice • 7 words • 0.0% coverageWriter's voice • 23 words • 0.0% coverageWriter's voice • 44 words • 0.0% coverageWriter's voice • 17 words • 100.0% coverageWriter's voice • 2 words • 100.0% coverageWriter's voice • 7 words • 0.0% coverageWriter's voice • 22 words • 0.0% coverageWriter's voice • 6 words • 0.0% coverageWriter's voice • 18 words • 0.0% coverageWriter's voice • 27 words • 0.0% coverageWriter's voice • 40 words • 0.0% coverageWriter's voice • 28 words • 0.0% coverageWriter's voice • 6 words • 0.0% coverageRapid7 • 33 words • 0.0% coverageRapid7 • 10 words • 0.0% coverageWriter's voice • 24 words • 0.0% coverageRapid7 • 18 words • 0.0% coverageThe Hacker News • 31 words • 0.0% coverageRapid7 • 17 words • 100.0% coverageWriter's voice • 25 words • 0.0% coverageWriter's voice • 6 words • 100.0% coverageWriter's voice • 29 words • 0.0% coverageWriter's voice • 15 words • 0.0% coverageWriter's voice • 26 words • 0.0% coverageWriter's voice • 14 words • 0.0% coverageWriter's voice • 12 words • 0.0% coverageWriter's voice • 12 words • 0.0% coverageWriter's voice • 23 words • 0.0% coverageWriter's voice • 2 words • 0.0% coverageWriter's voice • 35 words • 0.0% coverageWriter's voice • 14 words • 0.0% coverageWriter's voice • 47 words • 0.0% coverageWriter's voice • 16 words • 0.0% coverageWriter's voice • 27 words • 0.0% coverageRapid7 • 19 words • 100.0% coverageWriter's voice • 51 words • 100.0% coverageThe Hacker News • 31 words • 0.0% coverageWriter's voice • 9 words • 0.0% coverageWriter's voice • 34 words • 0.0% coverage
Selected voice

Rapid7

100%flagged-word coverage
128 attributed words67% of attributed speech69% writer coverage
0%12.5%25.0%Biased Writer Voice+23.5 ptsWriter: 0.8%Rapid7: 24.2%24.2%Indoctrination+8.4 ptsWriter: 6.4%Rapid7: 14.8%14.8%Quote-first Misdirection+10.9 ptsWriter: 2.4%Rapid7: 13.3%13.3%Unattributed Quote-2.1 ptsWriter: 2.1%Rapid7: 0.0%0.0%

Attribution is sentence-level. Pattern percentages are calculated only from words assigned to that voice.

Loading…
Loading…
Loading…

Analysis

Hover over highlighted words in the article to view the associated bias or fallacy analysis.