BondAI — agentic threat model
BondAI is a highly versatile, open-source multi-agent framework that presents elevated agentic risk due to its support for complex multi-agent orchestration, persistent memory, and diverse integrations. Without built-in guardrails or strict deployment sandboxing, it is highly susceptible to prompt injection, tool misuse, and cascading multi-agent failures.
OWASP AIVSS score rationale
| Autonomy of Action | 0.70 | |
| Goal-Driven Planning | 0.80 | |
| Self-Modification | 0.40 | |
| Dynamic Tool Use | 0.70 | |
| Persistent Memory | 0.80 | |
| Contextual Awareness | 0.80 | |
| Dynamic Identity | 0.30 | |
| Multi-Agent Interactions | 0.90 | |
| 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.
Not certain from the listing — BondAI supports OpenAI, Azure, Google, and other external providers, meaning foundation model risks like adversarial alignment, data poisoning, or model-specific vulnerabilities depend entirely on the user's chosen LLM backend.
BondAI supports vector/semantic search and memory management, making it susceptible to vector database poisoning, memory injection, and unauthorized data exfiltration if retrieval sources are untrusted.
As a Python-based orchestration framework, it manages planning, memory, and tool integrations, presenting risks of tool misuse, insecure tool execution, and prompt injection bypassing agent logic.
Can be deployed via CLI, Docker, or codebase integration. Docker provides container-level sandboxing, but misconfigurations could lead to host compromise or credential exposure (e.g., API keys for OpenAI/Azure).
Not certain from the listing — The description mentions 'error handling' but does not detail built-in evaluation, logging, or guardrail mechanisms to detect drift or malicious agent behavior.
Not certain from the listing — Being a free, open-source framework, it lacks built-in compliance certifications (like SOC2) or centralized access controls, leaving security policy enforcement to the deployer.
Explicitly supports multi-agent systems, introducing threats of agent-to-agent trust abuse, cascading failures, and malicious agent coordination within the deployed ecosystem.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).