Generate Malicious Commands
Description
Adversaries may use large language models (LLMs) to dynamically generate malicious commands from natural language. Dynamically generated commands may be harder to detect as the attack signature is constantly changing. AI-generated commands may also allow adversaries to more rapidly adapt to different environments and adjust their tactics.
Adversaries may utilize LLMs present in the victim's environment or call out to externally hosted services. APT28 utilized a model hosted on HuggingFace in a campaign with their LAMEHUG malware [[logpoint]]. In either case prompts to generate malicious code can blend in with normal traffic.
- ATLAS ID
- AML.T0102
- Maturity
- RealizedUsed by real adversaries against AI systems in the wild.
- Platforms
- Enterprise
Mitigations
- AML.M0020Generative AI Guardrails
Block prompts and outputs that request or contain malicious commands.
- AML.M0022Generative AI Model Alignment
Align generative AI models with safety objectives to reduce the likelihood that they will generate malicious commands or harmful instructions.