AgentForge — agentic threat model
AgentForge is a highly flexible, open-source agentic framework supporting multi-agent cognitive architectures and custom tool integration, presenting a high risk of tool misuse and prompt injection vulnerabilities if deployed without strict external sandboxing and input validation.
OWASP AIVSS score rationale
| Autonomy of Action | 0.70 | |
| Goal-Driven Planning | 0.80 | |
| Self-Modification | 0.60 | |
| Dynamic Tool Use | 0.70 | |
| Persistent Memory | 0.60 | |
| Contextual Awareness | 0.70 | |
| Dynamic Identity | 0.30 | |
| Multi-Agent Interactions | 0.80 | |
| 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 a wide variety of foundation models (OpenAI, Gemini, Claude, Ollama, LMStudio), making it susceptible to model-specific vulnerabilities, adversarial prompt injections, and misaligned outputs across different LLM providers.
Features Knowledge Graph Functionality for structured data operations, which introduces risks of knowledge-base poisoning, data exfiltration, and integrity issues if malicious data is ingested into the graph.
As an orchestration framework supporting 'Custom Tools & Actions' and 'On-The-Fly Prompt Editing', it is highly vulnerable to insecure tool integration, prompt injection leading to unauthorized tool execution, and framework-level orchestration bypasses.
Not certain from the listing — the framework runs locally (via Ollama/LMStudio) or connects to cloud APIs, but the listing does not specify built-in sandboxing, containerization, or secure credential storage for API keys.
Not certain from the listing — while designed for 'rapid development, testing, and iteration', there is no explicit mention of built-in guardrails, real-time monitoring, or security observability tools to detect anomalous agent behavior.
Not certain from the listing — as an open-source developer framework, it lacks built-in compliance certifications (like SOC2 or ISO) or enterprise-grade access controls, leaving security policy enforcement entirely to the deploying developer.
Designed for 'Cognitive Architectures' where 'different models' can run 'different agents', creating a multi-agent ecosystem vulnerable to cascading failures, agent-to-agent trust abuse, and complex, non-deterministic interaction loops.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).