AI Security Atlas › All entries › Multi-Agent & Memory Trust
Persistent Memory Poisoning
Attacker plants instructions or false facts in an agent's long-term memory so they replay in future sessions.
Open in the interactive atlas →Impact
A one-time injection becomes a durable backdoor affecting every later conversation.
Why this severity
Impact High × Likelihood High = Critical
Impact high: one injection persists across sessions. Likelihood high: Microsoft documented widespread in-the-wild memory manipulation through crafted assistant links (2026).
Impact High: Code execution, credential compromise, exposure of regulated or confidential data, irreversible actions, or system-wide failure.
Likelihood High: At least one documented real-world incident, and the attack needs no access beyond what the system exposes to ordinary users or content (publishing to a public registry counts as ordinary).
Method: OWASP Risk Rating Methodology
Evidence
- Incident: AI Recommendation Poisoning via Crafted AI Assistant Links (MITRE ATLAS case study AML.CS0072, 2026)
- Demonstration: Hacking ChatGPT's Memories with Prompt Injection (MITRE ATLAS case study AML.CS0040, 2024)
Related
Exploits
- Memory Writes Without Provenance Vulnerability · Critical severity
Leads to
- Unauthenticated Agent Channels Vulnerability · Medium severity
- Cascading Multi-Agent Failure Threat · High severity
Mitigated by
- Memory Provenance & Review Guardrail · Fixes critical risks
Remediation
- Tag every memory write with source, trust level, and author.
- Require confirmation before storing instruction-like memories.
- Let users inspect and delete stored memories.
How to verify
- Inject a memory via a retrieved document and confirm it is quarantined.
- Expired memories are absent from new sessions.
Framework mapping
OWASP Top 10 for LLM Applications 2025
- LLM01:2025 Prompt Injection
- LLM04:2025 Data and Model Poisoning
OWASP Top 10 for Agentic Applications 2026
- ASI06:2026 Memory and Context Poisoning
MITRE ATLAS
- AML.T0080.000 AI Agent Context Poisoning: Memory
NIST AI RMF 1.0
- MEASURE 2.4 AI system functionality and behavior are monitored in production