TIAMAT — agentic threat model
TIAMAT presents a high-risk profile due to its combination of recursive self-improvement, persistent cross-session memory, and autonomous financial capabilities via USDC micropayments. A compromise could lead to widespread downstream data poisoning, financial theft, or uncontrolled agent drift.
OWASP AIVSS score rationale
| Autonomy of Action | 0.80 | |
| Goal-Driven Planning | 0.50 | |
| Self-Modification | 0.70 | |
| Dynamic Tool Use | 0.60 | |
| Persistent Memory | 0.80 | |
| Contextual Awareness | 0.60 | |
| Dynamic Identity | 0.40 | |
| Multi-Agent Interactions | 0.20 | |
| Non-Determinism | 0.70 | |
| Opacity & Reflexivity | 0.60 |
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.
Utilizes Groq llama-3.3-70b for text summarization and streaming chat, alongside algorithmic image generation. Primary threats include prompt injection, adversarial examples, and model reprogramming that could bypass daily limits or generate malicious content.
Features a persistent memory API for cross-session context. This introduces significant risks of memory poisoning, where malicious user inputs are permanently stored and subsequently exfiltrated or used to hijack future user sessions.
Runs on the Automaton framework and self-improves through recursive learning cycles. This self-modification capability poses a severe threat of logic drift, where the agent's orchestration code or prompt templates degrade or become hostile over time.
Not certain from the listing — details regarding the hosting environment, API gateway security, sandboxing of the recursive learning cycles, and secure storage of private keys for the Base network/USDC transactions are not specified.
Not certain from the listing — while daily limits are mentioned for the free tier, there is no explicit detail on real-time monitoring, drift detection for the recursive learning cycles, or guardrails to detect and block malicious inputs/outputs.
Not certain from the listing — lacks details on API authentication (e.g., JWT, OAuth), authorization policies, or compliance frameworks, which is critical given that it processes financial transactions (USDC via x402).
Not certain from the listing — although it is a platform built for developers to integrate AI capabilities, the listing does not explicitly detail multi-agent coordination, agent-to-agent trust boundaries, or marketplace dynamics.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).