Agent4Rec — agentic threat model
Agent4Rec is a low-risk, open-source multi-agent simulator designed for recommender systems research. Its primary security risks are confined to simulation bias, dataset poisoning of the MovieLens-1M data, and local resource exhaustion from orchestrating 1,000 LLM agents.
OWASP AIVSS score rationale
| Autonomy of Action | 0.50 | |
| Goal-Driven Planning | 0.30 | |
| Self-Modification | 0.10 | |
| Dynamic Tool Use | 0.10 | |
| Persistent Memory | 0.40 | |
| Contextual Awareness | 0.50 | |
| Dynamic Identity | 0.10 | |
| Multi-Agent Interactions | 0.80 | |
| Non-Determinism | 0.60 | |
| Opacity & Reflexivity | 0.50 |
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 — The specific LLMs powering the 1,000 generative agents are not disclosed. Threats include adversarial prompt injection altering simulated user behavior or preferences.
The system relies on the MovieLens-1M dataset for agent initialization. Threats include dataset poisoning or manipulation of the source profiles, which would compromise the integrity of the simulation results.
Not certain from the listing — The orchestration framework managing the 1,000 agents is not detailed. Threats include state corruption or memory poisoning across the simulated agent population.
Not certain from the listing — No deployment or sandboxing details are provided. Running 1,000 LLM agents concurrently presents a high risk of local resource exhaustion (DoS) if not properly throttled.
Not certain from the listing — No observability, logging, or guardrail mechanisms are described to monitor agent drift or detect anomalous simulation behaviors.
Not certain from the listing — As an open-source research simulator, there are no mentioned security controls, access policies, or compliance alignments.
The framework simulates a dense ecosystem of 1,000 interacting agents. Threats include cascading feedback loops, emergent collusive behaviors, and systemic bias propagation within the simulated recommendation environment.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).
These scores are auto-generated from public information (the agent's own listing, docs, and repository) using the canonical OWASP AIVSS formula and the MAESTRO framework — an estimate for guidance, not a penetration test, audit, or certification. See the scoring methodology. Are you the vendor? Factual corrections are free.