Savery AI — agentic threat model
Savery AI presents a high-risk profile due to its autonomous multi-agent architecture, write-access integrations (GitHub, Google Cloud), and ability to modify codebases and run tests without explicit human-in-the-loop gates mentioned.
OWASP AIVSS score rationale
| Autonomy of Action | 0.90 | |
| Goal-Driven Planning | 0.90 | |
| Self-Modification | 0.40 | |
| Dynamic Tool Use | 0.80 | |
| Persistent Memory | 0.60 | |
| Contextual Awareness | 0.80 | |
| Dynamic Identity | 0.50 | |
| Multi-Agent Interactions | 1.00 | |
| Non-Determinism | 0.80 | |
| Opacity & Reflexivity | 0.80 |
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 — uses unspecified foundation models for code generation and AI model training, leaving it vulnerable to adversarial prompt injection or model reprogramming that could result in malicious code generation.
Not certain from the listing — processes proprietary codebases, documentation, and training datasets, but vector store or RAG specifics are not detailed, posing potential risks of data exfiltration or codebase poisoning.
Uses a multi-agent orchestration framework (PM, researcher, engineer, QA) to plan and execute code modifications, presenting risks of tool misuse and insecure integration with GitHub and GCP.
Not certain from the listing — requires execution environments to run unit tests and QA, but sandboxing, container isolation, or secrets management details for GCP/GitHub credentials are not specified.
Not certain from the listing — mentions embedded quality assurance before submission, but lacks details on security guardrails, logging, or drift monitoring to detect malicious or anomalous code modifications.
Not certain from the listing — targeted at enterprise IT but does not explicitly detail identity management, access controls, or compliance certifications (like SOC2) to govern autonomous code commits.
Employs a complex multi-agent network (PM, researcher, engineer, QA) operating in parallel, creating risks of cascading failures and trust abuse between synthetic personas during the development lifecycle.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).