- Tactics
- Defense Evasion
- Maturity
- demonstrated
- Reference
- atlas.mitre.org/techniques/AML.T0111
Description
AI Supply Chain Reputation Inflation is the process of building or leveraging genuinely credible-looking trust signals to increase the perceived legitimacy of AI supply chain components, with the goal of driving adoption of malicious or compromised assets.
Adversaries use established developer accounts with a history of legitimate projects and contributions to publish AI models, datasets, packages, and MCP servers that appear trustworthy. They build reputation through real adoption signals such as downloads, GitHub stars, forks, and inclusion in dependency chains, often releasing benign versions before introducing malicious updates via AI Supply Chain Rug Pull.
By relying on authentic history and usage patterns, these components pass both human and automated trust checks, increasing the likelihood they are adopted without scrutiny.
How GTK Cyber trains on this
GTK Cyber's hands-on AI security courses cover adversarial-AI techniques across the MITRE ATLAS framework, including the Defense Evasion tactic this technique falls under. Our practitioner-led training is taught by Charles Givre and other field-tested SMEs and focuses on real adversarial scenarios, not slide decks.
Related techniques
- AML.T0015 — Evade AI Model
- AML.T0054 — LLM Jailbreak
- AML.T0067 — LLM Trusted Output Components Manipulation
- AML.T0068 — LLM Prompt Obfuscation
- AML.T0071 — False RAG Entry Injection
- AML.T0073 — Impersonation
- AML.T0074 — Masquerading
- AML.T0076 — Corrupt AI Model
- AML.T0081 — Modify AI Agent Configuration
- AML.T0092 — Manipulate User LLM Chat History
- AML.T0094 — Delay Execution of LLM Instructions
- AML.T0097 — Virtualization/Sandbox Evasion