PraisonAI — agentic threat model
PraisonAI is a highly flexible, open-source multi-agent framework integrating AutoGen and CrewAI with deep codebase access. Its primary security risks stem from the orchestration of multiple autonomous agents with access to sensitive code repositories, lacking built-in sandboxing or guardrails in its default configuration.
OWASP AIVSS score rationale
| Autonomy of Action | 0.70 | |
| Goal-Driven Planning | 0.80 | |
| Self-Modification | 0.30 | |
| Dynamic Tool Use | 0.70 | |
| Persistent Memory | 0.50 | |
| Contextual Awareness | 0.80 | |
| Dynamic Identity | 0.40 | |
| Multi-Agent Interactions | 1.00 | |
| 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.
Supports 100+ LLMs, inheriting model-level vulnerabilities such as prompt injection, adversarial reprogramming, and misaligned outputs across a diverse set of foundation models.
Interacts directly with entire codebases, presenting significant risks of codebase data exfiltration, source code poisoning, and unauthorized access to intellectual property if malicious prompts are processed.
Combines AutoGen and CrewAI frameworks to orchestrate multi-agent systems. Vulnerabilities include insecure tool integration, framework-level execution bugs, and malicious tool misuse during task execution.
Not certain from the listing — as an open-source framework, deployment and infrastructure security (such as container sandboxing, secrets management, and network isolation) are entirely dependent on the user's self-hosted environment.
Not certain from the listing — there is no explicit mention of built-in evaluation, logging, or guardrail mechanisms to monitor agent behavior or detect anomalous multi-agent interactions.
Not certain from the listing — being a free, open-source technology framework, it lacks native compliance certifications (e.g., SOC2, ISO) or centralized identity and access management policies.
Designed specifically for multi-agent collaboration, creating a high risk of agent-to-agent trust abuse, cascading failures, and rogue agent behavior where one compromised agent compromises the entire swarm.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).