Machine Compromise - AI Security Technique
AI Security TechniqueAdversaries may compromise a machine by exploiting or manipulating AI-enabled components on the system. Compromising a victim system allows the adversary to execute arbitrary code, steal credentials, exfiltrate data, and continue to persist on the system. Adversaries may target a Local AI Agent which if compromised grants them the capabilities and permissions of the agent, or [AI Artif...
Overview
A source-backed snapshot of this AI security technique.
Adversaries may compromise a machine by exploiting or manipulating AI-enabled components on the system. Compromising a victim system allows the adversary to execute arbitrary code, steal credentials, exfiltrate data, and continue to persist on the system.
Adversaries may target a Local AI Agent which if compromised grants them the capabilities and permissions of the agent, or AI Artifacts which can contain embedded malware.
Technique details
Identifiers, maturity, and source taxonomy for this technique.
- ATLAS ID
- AML.T0112
- Maturity
- demonstrated
- Priority score
- 45
Attack flow
How to read the public records connected to this technique.
Impact
Why this technique may deserve attention in the current dataset.
- Evidence leveldemonstrated
- Mapped defenses0 ATLAS mitigation records
- Public examples0 linked case study records
- Research risks5 related MIT AI Risk records above the confidence threshold
- Vulnerabilities0 linked CVE records
Mitigations
Defenses that may help against this attack.
Case studies
Examples from public reports and exercises.
Source evidence
Original public records and references for this page.
Original source
Original source links
Open the public records and source datasets used for this page.