What MITRE ATLAS AI Agent Context Poisoning (AML.T0080) - Adversarial Manipulation of AI Agent LLM Context to Alter Future Behaviour requires
This node addresses MITRE ATLAS technique AML.T0080 (AI Agent Context Poisoning). Adversaries may attempt to manipulate the context used by an AI agent's large language model (LLM) to influence the responses it generates or actions it takes. This allows an adversary to persistently change the behavior of the target agent and further their goals. Context poisoning can be accomplished by prompting the an LLM to add instructions or preferences to memory (See Memory) or by simply prompting an LLM that uses prior messages in a thread as part of its context (See Thread). Compliance frameworks including the EU AI Act, NIST AI RMF, and ISO/IEC 42001 require organisations to detect, monitor, and defend against this technique to preserve AI system integrity.
Pillar: AI Governance & Law · Authority: MITRE Corporation · Version: 1.0.1 · Last updated:
Primary source: https://raw.githubusercontent.com/mitre-atlas/atlas-data/main/dist/ATLAS.yaml
SHA-256 integrity: af3481601374679f00cc93b457eb25bda6458a830121fb13fe0917a3282ab83c
Primary Citations — 7 traced to source
- MITRE ATLAS - AI Agent Context Poisoning (AML.T0080), https://raw.githubusercontent.com/mitre-atlas/atlas-data/main/dist/ATLAS.yaml#AML.T0080, AML.T0080 technique entry, 2026
- EU AI Act - Regulation (EU) 2023/1689 on Artificial Intelligence, https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32024R1689, Article 15 (Accuracy, Robustness, Cybersecurity), 2024
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