Hive0163's Slopoly: AI-Assisted Malware Enters the Chat
We've been discussing the theoretical risks of AI-generated malware for a while, but it looks like Hive0163 has forced the issue with their new 'Slopoly' framework. According to the latest disclosure, this is suspected to be AI-generated code used to establish persistent access for ransomware operations.
While the researchers note that the functionality is currently "unspectacular," the implication is terrifying: the barrier to entry for creating functional, persistent malware just dropped significantly. Threat actors can now iterate on frameworks like Slopoly in a fraction of the time it used to take.
From a defensive perspective, this means we're going to see a massive spike in unique hash samples. Signature-based detection is going to struggle even more than it already does. We need to shift focus to behavioral heuristics.
For those hunting for this, Slopoly appears to use standard scheduled task mechanisms for persistence but generates random task names, likely to bypass static reputation checks. Here is a basic KQL query I'm drafting for our Sysmon environment to catch high-entropy task creations:
Sysmon
| where EventID == 1
| where ProcessCommandLine contains "schtasks" and ProcessCommandLine contains "/create"
| where ProcessCommandLine matches regex @"[A-Za-z0-9]{32,}"
| project Timestamp, Hostname, Account, ProcessCommandLine
Has anyone else started seeing indicators of AI-assisted development in their malware sandboxes? I'm curious if others are noticing specific patterns in the code structure or comments that feel 'machine-generated'.
We've actually seen a similar uptick in 'slop' code in our honeypots. The logic is often disjointed—functions that reinvent the wheel rather than using standard Windows API calls efficiently. It’s less about optimization and more about speed of generation.
We've had luck blocking these by flagging unsigned binaries that attempt to establish persistence via registry keys immediately after execution. The behavior is distinct even if the code is messy.
The 'unspectacular' comment is key here. Right now, it's just script-kiddies using LLMs to wrap standard DLL injection techniques. But the volume is the real problem. Our SOC is drowning in alerts because every slightly modified binary triggers a 'New File' event.
We moved to a strict application allowlisting policy for critical servers last month, and it's the only thing saving us from the noise. You can't rely on detection alone when the supply of malware variants is infinite.
Good point on the scheduled tasks. I'd add a filter to look for PowerShell spawning schtasks.exe without a console window, as that's a common combo in these automated scripts.
Get-WinEvent -FilterHashtable @{LogName='Microsoft-Windows-Sysmon/Operational'; ID=1} |
Where-Object {$_.Message -match 'powershell.exe' -and $_.Message -match 'schtasks.exe'}
The AI generation often misses proper OPSEC, leaving noisy command line arguments like this exposed.
To catch this specific brand of "slop," I've started flagging scripts with unusually high comment-to-code ratios. AI often generates verbose explanations for basic API calls that human authors skip. It's a noisy indicator, but it helps filter automated churn. I use this quick Python snippet to scan dropped files for verbosity anomalies:
def check_comment_density(code):
lines = code.splitlines()
comments = sum(1 for l in lines if l.strip().startswith('#'))
return (comments / len(lines)) > 0.3 if lines else False
Another tell I've noticed is the tendency for AI to generate Python scripts with 'docstrings' inside functions—something rarely seen in obfuscated human malware. You can hunt for this with YARA rules targeting verbose multi-line strings combined with standard persistence imports.
Here’s a basic snippet to flag these verbose docstrings:
yara rule AI_Verbose_Docstrings { strings: $doc = /"""[A-Z].{30,}"""/ nocase condition: $doc }
Pairing this with entropy analysis helps filter out the noise generated by these frameworks.
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