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

Introduction

On May 23, 2026, a threat actor successfully authenticated to my Cowrie SSH honeypot using compromised credentials and, within 22 seconds, injected a backdoor SSH key, changed the root password, attempted to clear host-based access restrictions, and performed automated system reconnaissance. The speed and consistency of the behavior left no room for doubt: this was not a human attacker manually working through a system. This was automated post-exploitation infrastructure executing a pre-scripted playbook the instant it found an open door.

This post documents that intrusion, the broader campaign it belongs to, and what defenders can do about it. The data comes from a self-managed Raspberry Pi 5 honeypot running Cowrie, operating continuously since April 2026 as part of my SANS Internet Storm Center internship. Over the 30-day monitoring period analyzed here, the sensor captured over 112,000 SSH sessions and 72,000+ authentication attempts from 175+ unique malicious source IPs.

The Sensor and Setup

The honeypot runs Cowrie v2.3.0 on a Raspberry Pi 5 with a residential internet connection. Cowrie simulates an SSH server that accepts connections on port 2222 (forwarded from external port 22), logs all attacker activity including commands, file transfers, and credentials, and submits data automatically to ISC DShield. The sensor's logs are archived daily and analyzed for attacker TTPs, campaign patterns, and threat intelligence value.

All data referenced in this post was extracted from raw JSON Cowrie logs using jq queries and cross-referenced against AbuseIPDB, VirusTotal, GreyNoise, ISC DShield, AlienVault OTX, Shodan, and Whois.

The Intrusion: 22 Seconds From Login to Persistence

At 01:06:43 UTC on May 23, 2026, source IP 163.7.8.79 initiated an SSH connection to the honeypot. One second later, the actor successfully authenticated using the credentials root / Aa123123123, a weak password consistent with credentials leaked in past data breaches and commonly cycled through automated attack tools.

What happened next is best understood through the session timeline:

Session Timeline — 163.7.8.79 — May 23, 2026

The SSH key injected into authorized_keys was captured by Cowrie with the following hash:
a8460f446be540410004b1a8db4083773fa46f7fe76fa84219c93daa1669f8f2
The actor also removed the existing .ssh directory and recreated it before injecting the key, a technique used to eliminate existing authorized keys and ensure exclusive backdoor access. Changing the root password immediately after key injection further locks out legitimate administrators. Clearing /etc/hosts.deny removes any host-based access restrictions that might block future connections from the actor's infrastructure.

The entire sequence executed in 22 seconds. There was no hesitation, no exploration, no human decision-making visible in the command pattern. This is automation: a pre-scripted playbook executing the moment authentication succeeded.

The Attacker Kept Coming Back

After reviewing the full May 23 logs, I found that 163.7.8.79 returned to the sensor multiple times throughout the day, reconnecting approximately every few minutes and executing the same automated command sequence on each successful session. The consistency across sessions, identical command order, identical timing patterns, identical SSH key material, confirms this is not a human operator adapting to findings but an automated tool running a fixed exploitation script.

When I queried the logs for all successful authentications on May 23, I found 21 successful logins from 21 different source IPs within a single 24-hour period. The logins were clustered heavily between 01:00 and 02:30 UTC, suggesting coordinated wave-based scanning rather than independent actors discovering the honeypot randomly. A sample of the credentials used shows the breadth of the wordlists being deployed:

The presence of 'minecraft / 12345' is particularly noteworthy. Someone compiled a wordlist that includes gaming server default credentials, indicating active scanning for Minecraft or similar game server installations, not just generic Linux systems.

The Campaign Is Not Isolated and Has Not Stopped

To understand whether this was a one-time event or part of a sustained campaign, I cross-referenced the full list of IPs my sensor had observed over 30+ days of operation against a compiled list of IPs associated with the mdrfckr SSH campaign, a persistent automated SSH scanning operation that has been documented across multiple honeypot operators worldwide.

The result: 93 IPs from the mdrfckr campaign list were still actively hitting my sensor weeks after first being documented. This is not a historical observation. These actors did not stop. The campaign has been running continuously throughout the monitoring period.

Additionally, analysis of the top connecting IPs by session volume revealed a coordinated subnet cluster:

80.94.92.184    — high volume connections
80.94.92.186    — high volume connections
80.94.92.171    — high volume connections

Three IPs from the same /24 subnet hitting the sensor simultaneously is not coincidence. This is coordinated scanning infrastructure, either a botnet or a distributed scanning platform, operating multiple nodes from the same network block to maximize coverage while distributing the load.

Threat Intelligence on 163.7.8.79

Cross-referencing the primary actor IP across multiple threat intelligence platforms confirmed its malicious reputation:
AbuseIPDB: 100% confidence of abuse, over 5,700 reported incidents primarily related to SSH brute-force attacks, with recent reports confirming continued active scanning activity.
VirusTotal: Multiple security vendors classify the IP as malicious or suspicious.
GreyNoise: Identified as part of internet-wide SSH brute-force and reconnaissance scanning activity, confirming this is not a targeted attack but systematic exploitation of any reachable vulnerable host.
Whois: The IP is associated with Byteplus infrastructure (AS150436), a cloud hosting provider, consistent with the pattern of actors using cloud resources to scale automated attack campaigns.

MITRE ATT&CK Mapping

T1078 — Valid Accounts: Actor authenticated using compromised credentials from a wordlist.
T1098 — Account Manipulation: Malicious SSH key injected into authorized_keys to establish persistent access.
T1059 — Command Execution: Multiple shell commands executed immediately following authentication.
T1562 — Impair Defenses: /etc/hosts.deny cleared and processes terminated to remove access restrictions.

Why This Matters

The 22-second compromise window is the most important takeaway from this observation. In the time it takes a human to notice an alert, review it, and begin investigation, a fully automated actor has already established a persistent backdoor, locked out legitimate administrators, and completed system reconnaissance. On a real system with no monitoring, the attack would be invisible until the damage was done.

The credential root / Aa123123123 is not sophisticated. It follows a simple pattern: a common word plus repeating numbers plus a capital letter. Millions of systems remain accessible with credentials exactly like this, whether because they were provisioned with weak defaults, never hardened, or left unchanged after initial setup. The actors hitting your honeypot are not targeting you specifically. They are sweeping the internet for anyone who left a door unlocked.

The sustained nature of this campaign, 93 returning IPs still active weeks after first documented observation, reinforces that these actors are not deterred by a single failed attempt. They keep scanning. They keep trying. The math works in their favor when millions of internet-connected systems are in scope.

Who Benefits From This Information

System administrators who are responsible for any internet-exposed Linux system. If your system is reachable on port 22 with password authentication enabled, you are in scope for this campaign right now.
Security operations teams monitoring SSH authentication events. The behavioral signatures documented here, automated command sequences executing within seconds of authentication, consistent credential patterns, recurring source IPs, are detectable with proper log monitoring and should be included in detection rule sets.

Threat intelligence analysts tracking automated SSH campaigns. The mdrfckr campaign correlation data and the coordinated subnet cluster observations contribute to the shared picture of this ongoing threat.

Recommendations (MITRE Mitigations)

M1027 — Password Policies: Enforce strong passwords across all accounts. The credentials used in this campaign, including Aa123123123, follow predictable patterns that password complexity requirements would eliminate. Eliminate default credentials entirely.
M1036 — Account Use Policies: Implement rate limiting and account lockout for SSH authentication failures. Tools like fail2ban can automatically block IPs after repeated failed attempts, dramatically reducing the attack surface for automated scanners.
M1042 — Disable or Remove Feature: Disable SSH password authentication entirely and require public key authentication only. This single configuration change renders the entire credential stuffing attack class ineffective regardless of wordlist quality or campaign scale.
M1030 — Network Segmentation: Restrict SSH access to trusted IP ranges or VPN connections only. Internet-exposed SSH on port 22 is an open invitation to this class of automated attack.
M1047 — Audit: Monitor authentication logs continuously. The behavioral pattern of automated post-exploitation, rapid command sequences executing within seconds of login, is highly detectable with proper alerting in place.

Indicators of Compromise

IP: 163.7.8.79 (Byteplus, AS150436) — primary actor
Credentials: root / Aa123123123
SSH Key Hash: a8460f446be540410004b1a8db4083773fa46f7fe76fa84219c93daa1669f8f2
Associated Campaign: mdrfckr SSH campaign (93 confirmed overlapping IPs)

Conclusion

Automated SSH credential stuffing is not a sophisticated attack. It requires no novel exploits, no zero-days, and no targeted intelligence. It requires only an internet-connected system with weak credentials and no rate limiting. The 22-second compromise timeline documented here shows that the window between successful authentication and full backdoor establishment is too short for human response alone. Detection and prevention must be configured before the attack arrives, not after.

The campaign documented here has not stopped. The same infrastructure continues to scan, the same credential lists continue to be deployed, and the same post-exploitation playbook continues to execute the instant a weak system is found. The defenders who have hardened their SSH configuration are invisible to this campaign. The ones who have not are being hit right now.

[1] ISC DShield: https://isc.sans.edu/ipinfo/163.7.8.79
[2] AbuseIPDB: https://www.abuseipdb.com/check/163.7.8.79
[3] VirusTotal: https://www.virustotal.com/gui/ip-address/163.7.8.79
[4] GreyNoise: https://viz.greynoise.io/ip/163.7.8.79
[5] AlienVault OTX: https://otx.alienvault.com/indicator/ip/163.7.8.79
[6] Whois: https://whois.domaintools.com/163.7.8.79
[7] MITRE ATT&CK T1078: https://attack.mitre.org/techniques/T1078/
[8] MITRE ATT&CK T1098: https://attack.mitre.org/techniques/T1098/
[9] MITRE ATT&CK T1059: https://attack.mitre.org/techniques/T1059/
[10] MITRE ATT&CK T1562: https://attack.mitre.org/techniques/T1562/
[11] fail2ban: https://en.wikipedia.org/wiki/Fail2ban
[12] https://www.sans.edu/cyber-security-programs/bachelors-degree/

Note: This blog post was produced with the assistance of Claude (Anthropic) as a writing and organizational tool. All analysis, log data, threat intelligence findings, and conclusions are my own.

-----------
Guy Bruneau IPSS Inc.
My GitHub Page
Twitter: GuyBruneau
gbruneau at isc dot sans dot edu

When you learn that a compromised package executed on one of your build hosts, muscle memory takes over: revoke the npm token, rotate the GitHub PAT, cycle the cloud keys. That reflex has been correct in almost every supply-chain incident I have worked. In the keyv/cacheable compromise that has been unfolding since yesterday, it is the one thing you should not do first — because revoking the stolen token is exactly what arms the payload.

Let me back up.

What happened

On August 4, 2026, an attacker took over the maintainer account behind the widely used keyv and cacheable npm namespaces — caching libraries that sit near the bottom of a very large number of dependency trees — and published trojanized releases. Socket's Threat Research team, which did the primary analysis, places the first malicious release, [email protected], at 09:35 UTC. The poisoned versions ship a preinstall hook:

"scripts": { "preinstall": "node setup.mjs" }

setup.mjs downloads a standalone Bun runtime, runs an obfuscated second stage (Math_Symbol.js, ~728 KB), and harvests whatever it can reach: AWS instance metadata, cloud keys, Vault tokens, Kubernetes service-account tokens, GitHub Actions secrets, npm tokens, plus a generic regex sweep for private keys and bearer tokens on disk. Then — and this is why the campaign grew from roughly ten packages to several hundred within hours — it uses the stolen npm token to inject the same hook into other packages the compromised identity can publish, recomputes the integrity hashes, and republishes. It is a worm. The public IOC lists now cover more than 440 packages across two thousand-plus versions, and they are still moving.

Two properties make this one worth a closer look than the average typosquat.

It does not need npm install

Most teams scope this kind of incident to "who ran npm install in the exposure window." That misses half the population. The source repository also received IDE and agent autostart hooks — a SessionStart entry in .claude/settings.json and a folderOpen task in .vscode/tasks.json — that run the loader when the cloned folder is simply opened. No install, nothing built.

Sit with who that includes. It includes the security engineer who cloned the repository to investigate the incident after reading about it. It includes the AI coding agent that opened the directory to "take a look." I do not think we have seen AI-agent configuration files used as a first-class supply-chain execution vector at this scale before, and it is worth internalizing: a checked-out repository is now an execution surface, and .claude/, .cursor/, and .vscode/ are part of it.

It punishes remediation

Here is the part that should change how you respond. Alongside the credential theft, the payload installs a host-level dead-man's switch. It writes the stolen GitHub token and an attacker-supplied handler command to ~/.config/gh-token-monitor/, then persists itself as a macOS LaunchAgent or a Linux systemd user service with loginctl enable-linger so it survives logout. The systemd unit describes itself, helpfully, as a "GitHub Token Validity Monitor," so at a glance it reads like a developer convenience.

A watcher script polls the GitHub API with the stolen token every 60 seconds. While the token works, nothing happens. The moment the token stops working — an HTTP 4xx, which is precisely what your revocation produces — it evals the remote-supplied handler string, then deletes its own state and exits. It is single-shot and self-clearing, and it also self-destructs after a 24-hour TTL.

What is in the handler? Public analysis cannot say, because it is attacker-controlled text pulled at runtime and can be changed remotely. It could be data destruction, re-implant, or nothing at all. That is the whole problem: the risk is not that the trap does something specific and known — it is that you cannot assess it, and it fires at the exact moment your team believes it is containing the incident and starts to relax.

One consequence is counterintuitive but load-bearing: isolating the host from the network is safe. With no connectivity there is no HTTP response, so there is no 4xx, so the switch does not fire — and exfiltration stops at the same time. Isolate first. Do not power off; volatile memory is evidence.

Why the usual checks miss it

• "The signature was valid." [email protected] shipped with a passing SLSA attestation. Provenance attests to build integrity, not source integrity — the legitimate workflow faithfully built already-trojanized code.
• "The diff was clean." The library itself was not modified. The malice lives in package.json and two added files. A dist/ comparison shows nothing.
• "We don't use keyv." You almost certainly do, transitively. The common path is eslint → file-entry-cache → flat-cache → keyv. Very few victims installed any of these directly.
• "Nobody ran npm install." See the second section.

What to actually do

The order matters more than the individual steps:

1. Isolate the host from the network. Safe, for the reason above. Do not shut it down.
2. Preserve evidence before you delete anything — the watcher self-clears in ~24 hours. Copy ~/.config/gh-token-monitor/{handler,token,started_at}, the payloads, the plist/unit, and record hashes. Do not execute the handler; treat it as inert text. started_at bounds your exposure window.
3. Eradicate: kill the watcher, unload the LaunchAgent / disable the systemd unit, drop loginctl linger, remove the files and the .claude/.vscode hooks, and clear the package caches.
4. Rotate — now, and only now. npm token first, to stop propagation; then GitHub, cloud, Vault, Kubernetes, CI secrets, and anything that was sitting in a file, because there was a regex sweep. Revoke, do not merely rotate.
5. Audit what was done in your name: repositories freshly described "Shai-Hulud: Here We Go Again," unexpected npm publishes under your accounts, and credential use in your cloud logs during the started_at window.

CI runners and any host with confirmed execution should be rebuilt, not cleaned. Arbitrary code ran; the list of known artifacts is not a completeness guarantee.

A small tool to help with the triage

Enumerating this by hand across a fleet is tedious, and the moving IOC list makes a hardcoded grep obsolete within hours. I wrote a scanner to help with the triage: it checks lockfiles and node_modules for the compromised name/version set (with the transitive chain, so "we don't use keyv" gets answered on the spot), flags the host persistence and the dead-man's switch, and prints the response order above so nobody rotates before cleaning.

It is built to be easy to trust during exactly this kind of incident: one auditable file you can read in fifteen minutes, zero dependencies, zero egress (it never phones home; --update is the only network call and it is explicit), and read-only. It runs offline. It is MIT-licensed and open source, and — disclosure — it comes out of my work at Securest8; the IOC data is not mine but the public research of Socket, Wiz, and Kodem, credited in the repository.

• https://github.com/Securest8/npm-incident-response

If you only take the tool, take the response order with it. The scanner finds the problem; the order in which you touch credentials is what keeps a bad day from getting worse.

Bottom line

The novel part of this campaign is not the credential theft — it is the two design choices around it: an execution path that does not require installing anything, and a switch that turns your remediation reflex into the trigger. Scope the second vector, isolate before you revoke, and clean the host before you touch a single token.

References

- Socket, "Popular npm Packages in the keyv and Cacheable Namespaces Compromised in Active Supply Chain Attack," August 4, 2026. https://socket.dev/blog/popular-npm-packages-in-the-keyv-and-cacheable-namespaces-compromised-in-active-supply-chain
- Wiz Research, public IOC feed (keyv/cacheable). https://github.com/wiz-sec-public/wiz-research-iocs/blob/main/reports/keyv-packages.csv
- Wiz, "keyv and cacheable npm supply chain attack." https://www.wiz.io/blog/keyv-and-cacheable-npm-supply-chain-attack
- Kodem Security, keyv supply-chain attack IOCs and first-hour runbook. https://www.kodemsecurity.com/resources/keyv-supply-chain-attack-shai-hulud-npm-worm-affected-versions-iocs-and-first-hour-response-runbook

--

Renato Marinho
LinkedIn|Twitter

This morning, I noticed specific sources "hunting" for vulnerabilities in URLs that I haven't noticed before. All of these URLs appear to be associated with diagnostic tools:

URL	Count	Vulnerability
/	1	(simple recon for index page)
/apply.cgi	20	CVE-2024-12856 Four-Faith router command injection
/cgi-bin/adv_ping.cgi	20	?
/cgi-bin/diagnostic.cgi	20	CVE-2013-7179 Seowon Intech WiMAX SWU-9100 mobile route
/cgi-bin/DiagnosticsMsg.cgi	20	?
/cgi-bin/ping.cgi	20
/cgi-bin/system_mgr.cgi	20
/cgi-bin/traceroute.cgi	20
/diag_ping.cgi	20	CVE-2020-8949 (maybe.. slightly different URL) Gocloud devices
/goform/diagTool	20	CVE-2024-48419 (maybe..) Edimax Routers
/goform/ping	20
/ping_test.cgi	20
/sys_diag.html	20

The naming of these URLs points to diagnostic tools. I was unable to find any specific vulnerabilities associated with many of the URLs, but the table above reflects those I found. But diagnostic tools often suffer from file inclusion and code execution vulnerabilities.

These tools will often call operating system commands directly, without properly separating user-provided arguments. Here is a sample vulnerability in a ping utility:

response = os.system("ping -c 1 -w2 " + hostname )

The above example is in Python. But most (all?) languages have something equivalent to "os.system" (often called "exec", "shell_exec", "process" ...) Often, proper input validation and output encoding are used to prevent this vulnerability, but, in my opinion, there is a better approach that should always be used in addition to input validation, and I do not see it used much.

As with many other vulnerabilities, the root cause of command injection is the concatenation of user data and commands. Mixing control plane and data plane has been an issue since blue boxing and continues today with prompt injection. The real fix is to avoid this comingling of data and commands and instead properly separate them. Prepared statements in SQL are probably the best-known approach following this principle.

For OS command execution, we do have a very similar solution. The "system" command in your language will typically call the standard C function "exec" [1]. This family of function implements some meant to pass command line arguments: execv ("exec vector"). In addition to the command, it accepts an array of command-line arguments that are then passed to the command, properly separating the command from the arguments.

Python implements execv as part of the subprocess module:

response = subprocess.run("ping", "-c", 1, "-w", 2, hostname )

Using "subprocess.run" eliminates the possibility of command injection in this example.

For example, if you are using "google.com; ls" as a hostname, you get:

ping: cannot resolve google.com; ls: Unknown host

The entire string "google.com; ls" was used as a hostname, and the ";" no longer acted as a separator. Give it a try with other command injection strings, and you will see similar results.

There are a few cases where "execv" is not sufficient. Some operating system commands may execute additional commands passed on the command line. For example, tcpdump offers the "-z" option to execute a "postrotate command". But these cases are rare, and if you are running into them, you are back to proper input validation to use these specific command line options. In most cases, users cannot specify the command-line option itself but only the parameter; using the "execv" API will help.

A while ago, I also made a brief video with more details on preventing OS command injection: https://www.youtube.com/watch?v=7QDO3pZbum8. It also covers some of the issues around Windows, which implements different APIs.

 

[1] https://man7.org/linux/man-pages/man3/exec.3.html

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

Introduction

This diary provides indicators from an Atomic MacOS (AMOS) stealer infection that I generated in my lab on July 31st, 2026.  This was distributed through a web page from getmacouscloud[.]com with instructions to paste text into a macOS Terminal window, supposedly for "macOS toolkit," but instead the text is a command to retrieve and install AMOS stealer malware.

Of note, I ran the text in the Terminal window twice, because I wanted to make sure I retrieved copies of files in the host's /tmp directory before entering the user account password.  This is why the initial infection traffic is repeated, and also likely why there are two different directories with the AMOS stealer malware persistent on my infected lab host.

Images from the Infection

Shown above: Website with instructions to copy and paste text into a Terminal window, supposedly for a "macOS toolkit" but actually for malware.

Shown above: The malicious text pasted into a Terminal Window on a macOS host.

Shown above: Files from my infected host's /tmp directory, showing data stolen and other info for AMOS stealer.

Shown above: Examples of AMOS stealer persistent on my infected macOS host.

Shown above: Traffic from the AMOS stealer infection filtered in Wireshark.

Indicators of Compromise

Traffic leading to the getmacouscloud[.]com page on Friday 2026-07-31:

• hxxps[:]//macostruecloud[.]xyz/?h=2f9548d041648a8030c040ae0e1e530b&z=304
• macspheres[.]com - HTTPS traffic
• hxxps[:]//getmacouscloud[.]com/?FSSbmnNdviEDE5S?io=16vwsb0rgIiPNIgM

URL from the base64 text provided by getmacouscloud[.]com for the initial download:

• hxxps[:]//render65[.]com/curl/f5509695dd98a9732378e5256d6235415d64d92194459bb08525c7ce5991a0c9

URLs from extracted from the payload returned from the initial download:

• hxxps[:]//grove-89[.]com/api/metrics/run?event=pasted
• hxxps[:]//render65[.]com/2kqYRM0DCrnyJgoS4gVLl_FHJRRdTUhGCbjyuYwpZ6c/m1/update

AMOS stealer C2 traffic - HTTP POST requests over TCP port 80:

• hxxp[:]//188.166.78[.]138/api/metrics/run?event=started&stage=boot
• hxxp[:]//188.166.78[.]138/api/metrics/run?event=stage&stage=init_session
• hxxp[:]//188.166.78[.]138/api/metrics/run?event=stage&stage=messengers
• hxxp[:]//188.166.78[.]138/api/metrics/run?event=stage&stage=credentials
• hxxp[:]//188.166.78[.]138/api/metrics/run?event=stage&stage=browsers
• hxxp[:]//188.166.78[.]138/api/metrics/run?event=stage&stage=wallets
• hxxp[:]//188.166.78[.]138/contact
• hxxp[:]//188.166.78[.]138/api/metrics/run?event=stage&stage=resolve_auth
• hxxp[:]//188.166.78[.]138/api/metrics/run?event=stage&stage=local_data
• hxxp[:]//188.166.78[.]138/api/join/
• hxxp[:]//188.166.78[.]138/api/bots/device-info
• hxxp[:]//188.166.78[.]138/api/tasks/ack
• hxxp[:]//188.166.78[.]138/api/feed/register

AMOS stealer C2 traffic - examples of HTTP GET requests over TCP port 80:

• hxxp[:]//188.166.78[.]138/api/tasks/r3dqbX7fptIT-gXz--D_nw?v=2.1
• hxxp[:]//188.166.78[.]138/api/feed/items/49359f77ebb4ffd9a95568d27a8ff3e7

SHA-256 hash: b9ec3261d633c289e51c5fa8842af4350efe68446df39cb995de82e0941d0f3c

• File size: 1,973 bytes
• File type: Paul Falstad's zsh script text executable, ASCII text
• File description: Initial file retrieved by malicious text in Terminal window

SHA-256 hash: 13b868b3ea8b492e7fbab1ca04535c53d0930650185b5a082cd59c1974689cd5

• File size: 1,227 bytes
• File type: Paul Falstad's zsh script text executable, ASCII text, with very long lines (315)
• File description: Script extracted from a gzip-compressed file from base64 text in the above file

SHA-256 hash: 9f25ec533cb23d020e568fb771500d7776b1300f07119ad9d0876f4329ce22ab

• File size: 297,952 bytes
• File location: /tmp/helper
• File type: Mach-O universal binary with 2 architectures: x86_64 & arm64

SHA-256 hash: 0a03cf18de28017c0ea591dffc380a6b41fedd2acc3a39e901e58d9188c01836

• File size: 438,656 bytes
• File location: /Users/[username]/Library/Application Support/.com.apple.accountsd/AccountsHelper
• File type: Mach-O universal binary with 2 architectures: x86_64 & arm64

SHA-256 hash: 01a0d5332b09bb299f7784bf0d0c43c4199269ed6a0712377279eeb999847d20

• File size: 503,152 bytes
• File location: /Users/[username]/Library/Application Support/.com.apple.metadata.mds/mdworker_shared
• File type: Mach-O universal binary with 2 architectures: x86_64 & arm64

---
Bradley Duncan
brad [at] malware-traffic-analysis.net

Most phishing campaigns rely on the fact that the victim is afraid to loose "something": money, access to information, ... Many brands have been impersonated by campaigns but I spotted some phishing emails that focus on AI services like ChatGPT.

Yesterday, I found this email that was properly designed but also sent with a very good timing: the end of the month when your classic billing process is restarted!

The threat actor is just trying to grab your payment details:

Seeing the importance of AI used by most companies but also residential users, this is a clever move from threat actors! Many people will be afraid to loose their access to ChatGPT.

Xavier Mertens (@xme)
Senior ISC Handler | SANS Principal Instructor | Freelance Consultant
Xameco | PGP Key