- Maturity
- demonstrated
- Reference
- atlas.mitre.org/techniques/AML.T0080.000
Description
Adversaries may manipulate the memory of a large language model (LLM) in order to persist changes to the LLM to future chat sessions.
Memory is a common feature in LLMs that allows them to remember information across chat sessions by utilizing a user-specific database. Because the memory is controlled via normal conversations with the user (e.g. “remember my preference for …”) an adversary can inject memories via Direct or Indirect Prompt Injection. Memories may contain malicious instructions (e.g. instructions that leak private conversations) or may promote the adversary’s hidden agenda (e.g. manipulating the user).
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 relevant 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.