Techniquedefense-evasionATLAS

AML.T0109AI Supply Chain Rug Pull

What it is

Adversaries may publish legitimate AI components or software, gain user adoption, then push an update with a malicious variant, leading to [AI Supply Chain Compromise](/techniques/AML.T0010). More scrutiny is often placed on a supply chain dependency when it is first being considered for inclusion in an AI system. Performing a rug pull may allow adversaries to bypass these defenses and be more likely to achieve [Initial Access](/tactics/AML.TA0004). Adversaries may publish malicious AI components via [Publish Poisoned Models](/techniques/AML.T0058), [Publish Poisoned Datasets](/techniques/AML.T0019), or [Publish Poisoned AI Agent Tool](/techniques/AML.T0104). Adversaries may use other techniques (See [AI Supply Chain Reputation Inflation](/techniques/AML.T0111)) to gain user trust and increase adoption before performing the rug pull.

References

  1. https://atlas.mitre.org/techniques/AML.T0109

Related by meaning· 6

Nearest entities by semantic similarity across the cs-graph corpus.

ATLAS
AI Supply Chain Reputation Inflation
ATLAS
AI Supply Chain Compromise
ATLAS
Publish Poisoned Models
ATLAS
User Execution
ATLAS
Publish Poisoned Datasets
ATLAS
Exfiltration via Cyber Means
Sourced from MITRE ATLAS — Adversarial Threat Landscape for AI Systems. Curated by Adam Lundqvist, SQUR.