MemGPT — agentic threat model
MemGPT's advanced stateful execution and autonomous long-term memory management significantly increase its attack surface, as persistent memory poisoning can lead to enduring exploitation and unauthorized tool execution across sessions.
OWASP AIVSS score rationale
| Autonomy of Action | 0.80 | |
| Goal-Driven Planning | 0.70 | |
| Self-Modification | 0.90 | |
| Dynamic Tool Use | 0.80 | |
| Persistent Memory | 1.00 | |
| Contextual Awareness | 0.80 | |
| Dynamic Identity | 0.30 | |
| Multi-Agent Interactions | 0.80 | |
| Non-Determinism | 0.70 | |
| Opacity & Reflexivity | 0.70 |
Scored with the canonical OWASP AIVSS formula (AIVSS calculator reference); agentic risk factors estimated from the agent’s described capabilities.
MAESTRO 7-layer threat model
Per-layer threats for this agent. Layers tagged “not certain from listing” are general, caveated commentary where the public description didn’t pin that layer.
Not certain from the listing — MemGPT is model-agnostic and supports various LLMs. Threats include adversarial prompt injection bypassing memory boundaries or model reprogramming via poisoned context.
MemGPT connects to external data sources and manages long-term memory (archival/recall storage). Threats include memory poisoning, unauthorized data exfiltration from vector databases, and embedding inversion.
As an orchestration framework with OS-like memory management, threats include memory injection, state manipulation, and insecure tool execution where malicious inputs trigger unauthorized API calls.
Not certain from the listing — MemGPT is typically self-hosted or run locally. Infrastructure threats depend on deployment (e.g., lack of sandboxing for custom tool execution, exposed local API ports).
Not certain from the listing — The listing does not mention built-in guardrails or logging. Lack of observability into memory updates could allow silent, persistent drift or malicious memory modifications to go unnoticed.
Not certain from the listing — No built-in authentication, access control policies, or compliance frameworks are specified in the directory listing.
Supports multi-agent interactions. Threats include cascading failures, agent-to-agent trust abuse, and malicious agents poisoning the shared memory or state of other agents.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).
These scores are auto-generated from public information (the agent's own listing, docs, and repository) using the canonical OWASP AIVSS formula and the MAESTRO framework — an estimate for guidance, not a penetration test, audit, or certification. See the scoring methodology. Are you the vendor? Factual corrections are free.