Weaponizing LLMs: Detecting Hive0163's AI-Generated "Slopoly"
The report on Hive0163 leveraging an AI-generated malware framework, dubbed "Slopoly," is concerning but not surprising. We've been discussing AI lowering the barrier to entry for a while, but seeing it actively used for establishing persistent access in ransomware operations changes the game significantly.
The researchers noted that while the malware itself is "unspectacular," the speed of iteration is the real threat vector. Traditional IOC lists are going to struggle here because the actors can simply re-prompt the model to generate slightly different binary structures or obfuscation layers, creating fresh hashes instantly.
I've been shifting focus to behavioral detection rather than signature-based hunting for this family. Based on the persistent access mechanisms described, here is a KQL query I’m testing to catch the scheduled task creation often associated with these variants:
ScheduledTasks
| where TaskContent contains "powershell" or TaskContent contains "cmd"
| where TaskName contains "-update" or TaskName contains "-service"
| where Action == "C:\\Windows\\System32\\WindowsPowerShell\\v1.0\\powershell.exe"
| project TimeGenerated, Computer, TaskName, Author, Action
| extend Score = iff(TaskContent matches regex @"-enc|EncodedCommand", 1, 0)
| where Score == 1
The TaskContent check for encoded commands is crucial, as AI-generated scripts often rely on standard encoding wrappers to bypass simple text-based detection.
Given that AI can produce "sloppy" code (hence the name), are you guys seeing an increase in script errors or AMSI failures in your logs that might help flag these low-effort, automated constructs?
We've actually noticed an uptick in AMSI alerts triggered by 'suspicious string concatenation' which aligns with this. LLMs often love using FormatString or complex char arrays to hide payloads, but they tend to reuse specific patterns that AMSI signatures pick up easily. I'd suggest adding a specific rule set for AMSI events with EventID 1111 and looking for those 'sloppy' obfuscation attempts.
The 'Slopoly' moniker is fitting. I've analyzed samples where the variable naming conventions were inconsistent—mixing camelCase and snake_case in the same script—which is a dead giveaway for AI generation. We've started implementing strict code style checks in our pre-commit hooks and using LLM detectors to flag incoming scripts in our email filters.
From a pentester's perspective, the speed is the scary part. I can generate a functional C# dropper in seconds that compiles without warnings. Defenders need to stop relying on hash reputations. Implementing application control (AppLocker/WDAC) to block unsigned binaries in user directories is about the only reliable defense left against this volume of variants.
Static signatures are losing this arms race. We’ve shifted focus to behavioral enforcement in our runtime environments since the source code changes too fast. A simple Falco rule detecting parent-child process anomalies often flags these droppers regardless of their variable naming conventions.
- rule: Suspicious Process Spawn
condition: >
proc.name in (python, node) and
spawned_process.name in (bash, sh)
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