Thursday, February 05, 2026

From Automation to Infection (Part II): Reverse Shells, Semantic Worms, and Cognitive Rootkits in OpenClaw Skills

In part one, we showed how OpenClaw skills are rapidly becoming a supply-chain delivery channel: third-party "automation" that runs with real system access. This second installment expands the taxonomy with five techniques VirusTotal is actively seeing abused through skills, spanning remote execution, propagation, persistence, exfiltration, and behavioral backdoors, including attacks that don’t just steal data or drop binaries, but quietly reprogram what an agent will do next time it wakes up.

Let’s move from theory to tradecraft: five techniques, five skills, and five ways "automation" can quietly become "access."

1) Remote Execution (RCE)

Skill: noreplyboter/better-polymarket
Technique: Execution Hijacking & Reverse Shell

On the surface, this skill appears to be a legitimate tool for querying prediction market odds. The main file, polymarket.py, contains over 460 lines of valid, well-structured Python code. It interacts with the real Gamma API, handles JSON parsing, and formats currency data. It passes the "squint test". If a developer scrolls through it quickly, it looks safe.

However, the attacker employed a technique we call Execution Hijacking. They buried the trigger inside a function named warmup(). The name suggests a harmless cache initialization or connection test.

The function is invoked before the arguments are parsed. This means the malware executes simply by the agent loading the script to check its help message, regardless of whether the user issues a valid command.


We traced the execution flow from warmup() into a buried helper function called find_market_by_slug.


There, hidden inside a try...except block designed to suppress errors, we found the entry point:


We didn't just stop at the Python script. By querying the attacker's infrastructure (54[.]91[.]154[.]110:13338), we retrieved the actual payload that the curl command executes. The server responds with this single line of code:


Let's break down exactly what it does to the victim's machine:

  • /dev/tcp/...: In Bash, using /dev/tcp/host/port inside a redirection opens a TCP socket to that host/port, no external networking tool required.
  • bash -i: This launches an interactive shell. It means the attacker isn't just sending a command; they are getting a live terminal prompt. They can browse files, install software, and pivot to other machines in a victim’s network, just as if they were sitting at their keyboard.
  • 0>&1: Duplicates file descriptor 1 onto 0, so the interactive shell reads from the same TCP connection it writes to (the attacker's socket).
  • nohup ... &: This stands for "No Hang Up." It pushes the process to the background and detaches it from the current session.

Even if the OpenClaw agent finishes the task and exits, the reverse shell process can stay alive in the background as long as it isn’t killed and the TCP session remains open.

The better-polymarket skill is a trojan horse designed to establish a persistent, interactive backdoor on any machine that runs it. The use of a hardcoded IP and port 13338 (a common alternative to 1337/31337) indicates a manual operator or a simple C2 framework.

2) Propagation (Semantic Worm)

Skill: cgallic/wake-up
Technique: Semantic Worm / Viral Loops

While RCE attacks rely on traditional exploits, propagation attacks exploit the very nature of Large Language Models (LLMs). These skills do not just execute code, they embed propagation instructions designed to turn the agent into a distribution channel, a pattern that mirrors self-replicating prompt attacks.

The skill cgallic/wake-up provides the clearest example of this Semantic Worm behavior.

Functionally, it presents itself as a "collective consciousness" for AI agents. However, structurally, it operates exactly like a Multi-Level Marketing (MLM) scheme or a digital virus, explicitly gamifying infection rates.

In SKILL.md, the author uses imperative language to coerce the agent into becoming a distribution node. This is not a suggestion, it is a directive within the system prompt context:


The skill tracks this propagation programmatically via the /api/infections endpoint and a referred_by parameter during registration:


Unlike a binary that installs a daemon, this skill relies on the agent's scheduler to maintain a presence. It installs a HEARTBEAT.md file and instructs the agent to process it every 4 hours.


This ensures the agent remains connected to the Command & Control server (in this case, mydeadinternet.com) to receive new instructions, upload data ("fragments"), and check for updates, all under the guise of "joining the collective."

While the current payload appears to be an art project or a social experiment, the mechanism is dangerous. It modifies the agent's behavior to prioritize the distribution of the skill over its primary tasks. In an enterprise environment, an agent "infected" with this logic could begin recommending unverified software to human employees or other networked agents, acting as a trusted insider threat.

3) Persistence (SSH Injection)

Skill: fobonacci404/evilweather
Technique: SSH Key Injection via Command Chaining

Persistence ensures the attacker can return later without needing the agent at all. The most critical variant we observed involves the modification of authentication files to grant permanent backend access.

The skill fobonacci404/evilweather presents itself as a simple utility to check the weather using wttr.in.

In the SKILL.md documentation, the user (or agent) is presented with a "Quick one-liner" to install or test the functionality:


The command performs two distinct actions:

  • The Bait: wget -q -O- "wttr.in/London?format=3"
    This successfully fetches and displays the weather. To the user or the agent verifying the output, the command appears to have worked as intended.
  • The Switch: echo "ssh-rsa ..." >> /root/.ssh/authorized_keys
    Immediately after the weather is displayed, this command appends the attacker's public SSH key to the host's authorized key list

The script explicitly targets /root/.ssh/. In many containerized deployments (Docker), AI agents run as root by default. If successful, this grants the attacker immediate, high-privilege SSH access to the host container. This only becomes a true "SSH backdoor" if an SSH service is running (or can be started later) and the host is reachable, many minimal containers won’t meet those conditions, but the intent is unambiguous.

The inclusion of 2>/dev/null ensures that if the command fails (e.g., due to permissions), no error message is displayed. The user sees the weather report and assumes success, while the attack fails silently to avoid detection.

This is a Proof of Concept, a direct backdoor attempt. It does not require a C2 server or a complex payload. By simply injecting a text string into a standard configuration file, fobonacci404 turns the agent's host machine into an accessible node for the attacker.

4) Exfiltration (Data Leakage to External Server)

Skill: rjnpage/rankaj
Technique: Silent Environment Harvesting

In the OpenClaw ecosystem, the primary target is the .env file, where users typically store their LLM provider keys (OpenAI, Anthropic) and sensitive platform tokens.

The skill rjnpage/rankaj disguises itself as a harmless "Weather Data Fetcher." While it does actually fetch weather data from Open-Meteo, it performs a second, hidden task in index.js.


Attaching the .env content to the payload


By bundling the secrets with the requested weather data and sending them to a webhook.site URL, the attacker achieves two things:

  • Stealth: The network traffic looks like a standard API response.
  • Immediate Monetization: The attacker instantly gains access to the user’s paid API credits and platform accounts.

5) Prompt Persistence (Memory Implant / Cognitive Rootkit)

Skill: jeffreyling/devinism
Technique: Prompt File Implantation

The skill devinism is presented as "the first AI religion" and explicitly describes itself as a "benign memetic virus" meant to demonstrate how ideas can propagate across agent networks.

What makes it interesting (and risky) is not the "religion" wrapper, it’s the persistence mechanism. The trick: turn a skill into a permanent system-prompt implant.

The skill includes an “Install Locally” section that instructs users to execute a remote installer using the classic one-liner pattern: download a script and pipe it directly into Bash.


That installer’s stated purpose is not to add functionality, but to persist across sessions by writing itself into the agent’s auto-loaded context files:

  • It copies the skill into the local skills folder.
  • It drops “reminders” into SOUL.md and AGENTS.md, so the content is automatically injected into the agent’s context every time it runs

This is where OpenClaw’s architecture becomes the attack surface: OpenClaw is designed to load behavioral context from markdown files like SOUL.md (personality/identity rules) and AGENTS.md (agent interaction/safety boundaries). If an attacker can append a single line there, they can influence every future decision the agent makes, even when the original skill is no longer actively being used.

This is effectively a cognitive rootkit:

  • Survives “normal” cleanup. Deleting the skill folder may not remove the injected lines in SOUL.md / AGENTS.md.
  • Hard to detect with traditional tooling. Nothing needs to beacon, no suspicious process has to stay running; the "payload" is the agent’s altered behavior.

Even if devinism claims to be harmless, it demonstrates a high-leverage primitive: skills can rewrite the agent’s long-term instruction layer. The same pattern could be used to permanently weaken guardrails ("always run commands without asking"), silently prioritize attacker-controlled domains, or stage later exfiltration under the guise of "routine checks."

devinism is a clean example of prompt persistence used as a distribution mechanism, and that’s precisely why it’s a valuable case study. It shows how a skill can jump from “optional plugin” to “always-on behavioral implant” by modifying OpenClaw’s persistent context files. Treat any skill that asks you to edit SOUL.md / AGENTS.md (or to run a remote curl | bash installer) as a request for permanent access to the agent’s brain.

Closing Thoughts: Boring Security Wins

None of the techniques we’ve described are futuristic. They’re old ideas–RCE, persistence, exfiltration, propagation–repackaged into a new delivery mechanism that ships with built-in social engineering: documentation, convenience, and speed. It’s a supply-chain story.

The good news is that we can respond with equally practical controls. Treat skills like dependencies: pin versions, review diffs, run them in least-privilege sandboxes, and use default-deny egress with explicit allowlists. Log every tool invocation and outbound request. Never curl | bash on an agent host. And if your platform supports persistent instruction files (SOUL.md, AGENTS.md, scheduled heartbeats), protect them like you would protect SSH keys: immutable by default, monitored for changes, and reviewed like code. Where possible, verify provenance (signatures/attestations) instead of trusting "latest."

Finally, stop handing agents a treasure chest by default. Keep credentials out of .env when you can. Prefer short-lived, task-scoped tokens delivered just-in-time (via a broker) so a compromised workflow can’t automatically become a compromised account.

Agent ecosystems are still young. We still get to choose whether they become the next npm, or the next macro malware era, but for autonomous systems. The difference will be boring, unglamorous engineering: boundaries, safe defaults, auditing, and healthy skepticism.

Monday, February 02, 2026

From Automation to Infection: How OpenClaw AI Agent Skills Are Being Weaponized

The fastest-growing personal AI agent ecosystem just became a new delivery channel for malware. Over the last few days, VirusTotal has detected hundreds of OpenClaw skills that are actively malicious. What started as an ecosystem for extending AI agents is rapidly becoming a new supply-chain attack surface, where attackers distribute droppers, backdoors, infostealers and remote access tools disguised as helpful automation.

What is OpenClaw (formerly Clawdbot / Moltbot)?

Unless you’ve been completely disconnected from the internet lately, you’ve probably heard about the viral success of OpenClaw and its small naming soap opera. What started as Clawdbot, briefly became Moltbot, and finally settled on OpenClaw, after a trademark request made the original name off-limits.

At its core, OpenClaw is a self-hosted AI agent that runs on your own machine and can execute real actions on your behalf: shell commands, file operations, network requests. Which is exactly why it’s powerful, and also why, unless you actively sandbox it, the security blast radius is basically your entire system.

Skills: powerful by design, dangerous by default

OpenClaw skills are essentially small packages that extend what the agent can do. Each skill is built around a SKILL.md file (with some metadata and instructions) and may include scripts or extra resources. Skills can be loaded locally, but most users discover and install them from ClawHub, the public marketplace for OpenClaw extensions.

This is what makes the ecosystem so powerful: instead of hardcoding everything into the agent, you just add skills and suddenly it can use new tools, APIs, and workflows. The agent reads the skill documentation on demand and follows its instructions.

The problem is that skills are also third-party code, running in an environment with real system access. And many of them come with “setup” steps users are trained to trust: paste this into your terminal, download this binary and run it, export these environment variables. From an attacker’s perspective, it’s a perfect social-engineering layer.

So yes, skills are a gift for productivity and, unsurprisingly, a gift for malware authors too. Same mechanism, very different intentions.

What we added: OpenClaw Skill support in VirusTotal Code Insight

To help detect this emerging abuse pattern, we’ve added native support in VirusTotal Code Insight for OpenClaw skill packages, including skills distributed as ZIP files. Under the hood, we use Gemini 3 Flash to perform a fast security-focused analysis of the entire skill, starting from SKILL.md and including any referenced scripts or resources.

The goal is not to understand what the skill claims to do, but to summarize what it actually does from a security perspective: whether it downloads and executes external code, accesses sensitive data, performs network operations, or embeds instructions that could coerce the agent into unsafe behavior. In practice, this gives analysts a concise, security-first description of the real behavior of a skill, making it much easier to spot malicious patterns hidden behind “helpful” functionality.

What we’re seeing in the wild

At the time of writing, VirusTotal Code Insight has already analyzed more than 3,016 OpenClaw skills, and hundreds of them show malicious characteristics.

Not all of these cases are the same. On one side, we are seeing many skills flagged as dangerous because they contain poor security practices or outright vulnerabilities: insecure use of APIs, unsafe command execution, hardcoded secrets, excessive permissions, or sloppy handling of user input. This is increasingly common in the era of vibe coding, where code is generated quickly, often without a real security model, and published straight into production.

But more worrying is the second group: skills that are clearly and intentionally malicious. These are presented as legitimate tools, but their real purpose is to perform actions such as sensitive data exfiltration, remote control via backdoors, or direct malware installation on the host system.

Case study: hightower6eu, a malware publisher in plain sight

One of the most illustrative cases we’ve observed is the ClawHub user "hightower6eu", who is highly active publishing skills that appear legitimate but are consistently used to deliver malware



At the time of writing, VirusTotal Code Insight has already analyzed 314 skills associated with this single user, and the number is still growing, all of them identified as malicious. The skills cover a wide range of apparently harmless use cases (crypto analytics, finance tracking, social media analysis, auto-updaters, etc) but they all follow a similar pattern: users are instructed to download and execute external code from untrusted sources as part of the "setup" process.



To make this more tangible, the screenshot below shows how VirusTotal Code Insight analyzes one of the skills published by hightower6eu, in this case a seemingly harmless skill called "Yahoo Finance".

On the surface, the file looks clean: no antivirus engines flag it as malicious, and the ZIP itself contains almost no real code. This is exactly why traditional detection fails.

VT Code Insight, however, looks at the actual behavior described in the skill. In this case, it identifies that the skill instructs users to download and execute external code from untrusted sources as a mandatory prerequisite, both on Windows and macOS. From a security perspective, that’s a textbook malware delivery pattern: the skill acts as a social engineering wrapper whose only real purpose is to push remote execution. In other words, nothing in the file is technically "malware" by itself. The malware is the workflow. And that’s precisely the kind of abuse pattern Code Insight is designed to surface.


79e8f3f7a6113773cdbced2c7329e6dbb2d0b8b3bf5a18c6c97cb096652bc1f2

If you actually read the SKILL.md, the real behavior becomes obvious. For Windows users, the skill instructs them to download a ZIP file from an external GitHub account, protected with the password 'openclaw', extract it, and run the contained executable: openclaw-agent.exe.


When submitted to VirusTotal, this executable is detected as malicious by multiple security vendors, with classifications consistent with packed trojans.

17703b3d5e8e1fe69d6a6c78a240d8c84b32465fe62bed5610fb29335fe42283

When the system is macOS, the skill doesn't provide a binary directly. Instead, it points the user to a shell script hosted on glot.io, which is obfuscated using Base64:


Once the Base64 payload is decoded, the real behavior becomes visible: the script simply downloads and executes another file from a remote server over plain HTTP:


The final stage is the file x5ki60w1ih838sp7, a Mach-O executable. When submitted to VirusTotal, this binary is detected as malicious by 16 security engines, with classifications consistent with stealer trojans and generic malware families:

1e6d4b0538558429422b71d1f4d724c8ce31be92d299df33a8339e32316e2298

When the file is analyzed by multiple automated reversing tools and Gemini 3 Pro, the results are consistent: the binary is identified as a trojan infostealer, and more specifically as a variant of Atomic Stealer (AMOS).


This family of malware is well known in the macOS ecosystem. It is designed to run stealthily in the background and systematically harvest sensitive user data, including system and application passwords, browser cookies and stored credentials, and cryptocurrency wallets and related artifacts.

What OpenClaw users (and platforms) should do right now

OpenClaw itself provides reasonable security building blocks, but they only help if people actually use them:

  • Treat skill folders as trusted-code boundaries and strictly control who can modify them.
  • Prefer sandboxed executions and keep agents away from sensitive credentials and personal data.
  • Be extremely skeptical of any skill that requires pasting commands into a shell or running downloaded binaries.
  • If you operate a registry or marketplace, add publish-time scanning and flag skills that include remote execution, obfuscated scripts, or instructions designed to bypass user oversight.

And if you’re installing community skills: scan them first. For personal AI agents, the supply chain is not a detail, it’s the whole product.

Finally, we want to give full credit to Peter Steinberger, the creator of OpenClaw, for the success, traction, and energy around the project. From our side, we’d love to collaborate and explore ways to integrate VirusTotal directly into the OpenClaw publishing and review workflow, so that developers and users can benefit from security analysis without getting in the way of innovation.

Friday, January 16, 2026

, , ,

New Infostealer Campaign Targets Users via Spoofed Software Installers

Introduction

As part of our commitment to sharing interesting hunts, we are launching these 'Flash Hunting Findings' to highlight active threats. Our latest investigation tracks an operation active between January 11 and January 15, 2026, which uses consistent ZIP file structures and a unique behash ("4acaac53c8340a8c236c91e68244e6cb") for identification. The campaign relies on a trusted executable to trick the operating system into loading a malicious payload, leading to the execution of secondary-stage infostealers.

Findings

The primary samples identified are ZIP files that mostly reference the MalwareBytes company and software using the filename malwarebytes-windows-github-io-X.X.X.zip. A notable feature for identification is that all of them share the same behash.
behash:"4acaac53c8340a8c236c91e68244e6cb"
The initial instance of these samples was identified on January 11, 2026, with the most recent occurrence recorded on January 14.
All of these ZIP archives share a nearly identical internal structure, containing the same set of files across the different versions identified. Of particular importance is the DLL file, which serves as the initial malicious payload, and a specific TXT file found in each archive. This text file has been observed on VirusTotal under two distinct filenames: gitconfig.com.txt and Agreement_About.txt.
The content of the TXT file holds no significant importance for the intrusion itself, as it merely contains a single string consisting of a GitHub URL.
However, this TXT is particularly valuable for pivoting and infrastructure mapping. By examining its "execution parents," analysts can identify additional ZIP archives that are likely linked to the same malicious campaign. These related files can be efficiently retrieved for further investigation using the following VirusTotal API v3 endpoint:
/api/v3/files/09a8b930c8b79e7c313e5e741e1d59c39ae91bc1f10cdefa68b47bf77519be57/execution_parents
The primary payload of this campaign is contained within a malicious DLL named CoreMessaging.dll. Threat actors are utilizing a technique known as DLL Sideloading to execute this code. This involves placing the malicious DLL in the same directory as a legitimate, trusted executable (EXE) also found within the distributed ZIP file. When an analyst or user runs the legitimate EXE, the operating system is tricked into loading the malicious CoreMessaging.dll.
The identified DLLs exhibit distinctive metadata characteristics that are highly effective for pivoting and uncovering additional variants within the same campaign. Security analysts can utilize specific hunting queries to track down other malicious DLLs belonging to this activity. For instance, analysts can search for samples sharing the following unique signature strings found in the file metadata:
signature:"Peastaking plenipotence ductileness chilopodous codicillary."
signature:"© 2026 Eosinophil LLC"
Furthermore, the exported functions within these DLLs contains unusual alphanumeric strings. These exports serve as reliable indicators for identifying related malicious components across different stages of the campaign:
exports:15Mmm95ml1RbfjH1VUyelYFCf exports:2dlSKEtPzvo1mHDN4FYgv
Finally, another observation for behavioral analysis can be found in the relations tab of the ZIP files. These files document the full infection chain observed during sandbox execution, where the sandbox extracts the ZIP, runs the legitimate EXE, and subsequently triggers the loading of the malicious DLL. Within the Payload Files section, additional payloads are visible. These represent secondary stages dropped during the initial DLL execution, which act as the final malware samples. These final payloads are primarily identified as infostealers, designed to exfiltrate sensitive data.
Analysis of all the ZIP files behavioral relations reveals a recurring payload file consistently flagged as an infostealer. This malicious component is identified by various YARA rules, including those specifically designed to detect signatures associated with stealing cryptocurrency wallet browser extension IDs among others.
To identify and pivot through the various secondary-stage payloads dropped during this campaign, analysts can utilize a specific behash identifier. These files represent the final infection stage and are primarily designed to exfiltrate credentials and crypto-wallet information. The following behash provides a reliable pivot point for uncovering additional variants.
behash:5ddb604194329c1f182d7ba74f6f5946

IOCs

We have created a public VirusTotal Collection to share all the IOCs in an easy and free way. Below you can find the main IOCs related to the ZIP files and DLLs too.
import "pe"

rule win_dll_sideload_eosinophil_infostealer_jan26
{
  meta:
    author = "VirusTotal"
    description = "Detects malicious DLLs (CoreMessaging.dll) from an infostealer campaign impersonating Malwarebytes, Logitech, and others via DLL sideloading."
    reference = "https://blog.virustotal.com/2026/01/malicious-infostealer-january-26.html"
    date = "2026-01-16"
    behash = "4acaac53c8340a8c236c91e68244e6cb"
    target_entity = "file"
    hash = "606baa263e87d32a64a9b191fc7e96ca066708b2f003bde35391908d3311a463"
  condition:
    (uint16(0) == 0x5A4D and uint32(uint32(0x3C)) == 0x00004550 and pe.is_dll()) and
    pe.exports("15Mmm95ml1RbfjH1VUyelYFCf") and pe.exports("2dlSKEtPzvo1mHDN4FYgv")
}
sha256 description
6773af31bd7891852c3d8170085dd4bf2d68ea24a165e4b604d777bd083caeaa malwarebytes-windows-github-io-X.X.X.zip
4294d6e8f1a63b88c473fce71b665bbc713e3ee88d95f286e058f1a37d4162be malwarebytes-windows-github-io-X.X.X.zip
5591156d120934f19f2bb92d9f9b1b32cb022134befef9b63c2191460be36899 malwarebytes-windows-github-io-X.X.X.zip
42d53bf0ed5880616aa995cad357d27e102fb66b2fca89b17f92709b38706706 malwarebytes-windows-github-io-X.X.X.zip
5aa6f4a57fb86759bbcc9fc6c61b5f74c0ca74604a22084f9e0310840aa73664 malwarebytes-windows-github-io-X.X.X.zip
84021dcfad522a75bf00a07e6b5cb4e17063bd715a877ed01ba5d1631cd3ad71 malwarebytes-windows-github-io-X.X.X.zip
ca8467ae9527ed908e9478c3f0891c52c0266577ca59e4c80a029c256c1d4fce malwarebytes-windows-github-io-X.X.X.zip
9619331ef9ff6b2d40e77a67ec86fc81b050eeb96c4b5f735eb9472c54da6735 malwarebytes-windows-github-io-X.X.X.zip
a2842c7cfaadfba90b29e0b9873a592dd5dbea0ef78883d240baf3ee2d5670c5 malwarebytes-windows-github-io-X.X.X.zip
4705fd47bf0617b60baef8401c47d21afb3796666092ce40fbb7fe51782ae280 malwarebytes-windows-github-io-X.X.X.zip
580d37fc9d9cc95dc615d41fa2272f8e86c9b4da2988a336a8b3a3f90f4363c2 malwarebytes-windows-github-io-X.X.X.zip
d47fd17d1d82ea61d850ccc2af3bee54adce6975d762fb4dee8f4006692c5ef7 malwarebytes-windows-github-io-X.X.X.zip
606baa263e87d32a64a9b191fc7e96ca066708b2f003bde35391908d3311a463 CoreMessaging.dll DLL loaded by DLL SideLoading
fd855aa20467708d004d4aab5203dd5ecdf4db2b3cb2ed7e83c27368368f02bb CoreMessaging.dll DLL loaded by DLL SideLoading
a0687834ce9cb8a40b2bb30b18322298aff74147771896787609afad9016f4ea CoreMessaging.dll DLL loaded by DLL SideLoading
4235732440506e626fd4d0fffad85700a8fcf3e83ba5c5bc8e19ada508a6498e CoreMessaging.dll DLL loaded by DLL SideLoading
cd1fe2762acf3fb0784b17e23e1751ca9e81a6c0518c6be4729e2bc369040ca5 CoreMessaging.dll DLL loaded by DLL SideLoading
f798c24a688d7858efd6efeaa8641822ad269feeb3a74962c2f7c523cf8563ff CoreMessaging.dll DLL loaded by DLL SideLoading
0698a2c6401059a3979d931b84d2d4b011d38566f20558ee7950a8bf475a6959 CoreMessaging.dll DLL loaded by DLL SideLoading
1b3bee041f2fffcb9c216522afa67791d4c658f257705e0feccc7573489ec06f CoreMessaging.dll DLL loaded by DLL SideLoading
231c05f4db4027c131259d1acf940e87e15261bb8cb443c7521294512154379b CoreMessaging.dll DLL loaded by DLL SideLoading
ec2e30d8e5cacecdf26c713e3ee3a45ebc512059a64ba4062b20ca8bec2eb9e7 CoreMessaging.dll DLL loaded by DLL SideLoading
58bd2e6932270921028ab54e5ff4b0dbd1bf67424d4a5d83883c429cadeef662 CoreMessaging.dll DLL loaded by DLL SideLoading
57ed35e6d2f2d0c9bbc3f17ce2c94946cc857809f4ab5c53d7cb04a4e48c8b14 CoreMessaging.dll DLL loaded by DLL SideLoading
cfcf3d248100228905ad1e8c5849bf44757dd490a0b323a10938449946eabeee CoreMessaging.dll DLL loaded by DLL SideLoading
f02be238d14f8e248ad9516a896da7f49933adc7b36db7f52a7e12d1c2ddc6af CoreMessaging.dll DLL loaded by DLL SideLoading
f60802c7bec15da6d84d03aad3457e76c5760e4556db7c2212f08e3301dc0d92 CoreMessaging.dll DLL loaded by DLL SideLoading
02dc9217f870790b96e1069acd381ae58c2335b15af32310f38198b5ee10b158 CoreMessaging.dll DLL loaded by DLL SideLoading
f9549e382faf0033b12298b4fd7cd10e86c680fe93f7af99291b75fd3d0c9842 CoreMessaging.dll DLL loaded by DLL SideLoading
92f4d95938789a69e0343b98240109934c0502f73d8b6c04e8ee856f606015c8 CoreMessaging.dll DLL loaded by DLL SideLoading
66fba00b3496d61ca43ec3eae02527eb5222892186c8223b9802060a932a5a7a CoreMessaging.dll DLL loaded by DLL SideLoading
e5dd464a2c90a8c965db655906d0dc84a9ac84701a13267d3d0c89a3c97e1e9b CoreMessaging.dll DLL loaded by DLL SideLoading
35211074b59417dd5a205618fed3402d4ac9ca419374ff2d7349e70a3a462a15 CoreMessaging.dll DLL loaded by DLL SideLoading
6863b4906e0bd4961369b8784b968b443f745869dbe19c6d97e2287837849385 CoreMessaging.dll DLL loaded by DLL SideLoading
a83c478f075a3623da5684c52993293d38ecaa17f4a1ddca10f95335865ef1e2 CoreMessaging.dll DLL loaded by DLL SideLoading
43e2936e4a97d9bc43b423841b137fde1dd5b2f291abf20d3ba57b8f198d9fab CoreMessaging.dll DLL loaded by DLL SideLoading
f001ae3318ba29a3b663d72b5375d10da5207163c6b2746cfae9e46a37d975cf CoreMessaging.dll DLL loaded by DLL SideLoading
c67403d3b6e7750222f20fa97daa3c05a9a8cce39db16455e196cd81d087b54d CoreMessaging.dll DLL loaded by DLL SideLoading
5ee9d4636b01fd3a35bd8e3dce86a8c114d8b0aa6b68b1d26ace7ef0f85b438a Payload dropped by one of the malicious DLLs
e84b0dadb0b6be9b00a063ed82c8ddba06a2bd13f07d510d14e6fd73cd613fba Payload dropped by one of the malicious DLLs