Seraphnet AI — agentic threat model
Seraphnet AI presents a complex, high-exposure agentic risk profile due to its decentralized multi-agent architecture and blockchain integration. While fully homomorphic encryption (FHE) provides strong data privacy mitigations, the dynamic orchestration of Multi-Agent Pods introduces significant risks of agent-to-agent trust abuse and cascading failures.
OWASP AIVSS score rationale
| Autonomy of Action | 0.80 | |
| Goal-Driven Planning | 0.80 | |
| Self-Modification | 0.30 | |
| Dynamic Tool Use | 0.70 | |
| Persistent Memory | 0.60 | |
| Contextual Awareness | 0.80 | |
| Dynamic Identity | 0.60 | |
| 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.
Not certain from the listing — the specific foundation models are not disclosed, but the platform supports model deployment and training via the Forge SDK. Threats include model poisoning during training and adversarial manipulation of the deployed models.
Uses Fully Homomorphic Encryption (FHE) for data privacy and integrates on-chain/off-chain data. Key threats include data poisoning of off-chain sources, smart contract data feed manipulation, and potential cryptographic implementation flaws in the FHE layer.
Orchestrated by the Swarm Manager (intent-sensitive orchestrator) and Multi-Agent Pods. Threats include intent-hijacking, prompt injection bypassing the orchestrator, and insecure tool/API execution during collaborative task resolution.
Deployed in containerized environments via the Forge SDK on a decentralized base layer. Threats include container escape, smart contract vulnerabilities in the decentralized layer, and unauthorized node access.
Not certain from the listing — while it claims 'ideologically-transparent' and 'unbiased' operations, specific evaluation, logging, or drift detection frameworks are not detailed. Gaps in decentralized logging could lead to auditability blind spots.
Not certain from the listing — relies heavily on cryptographic privacy (FHE) and blockchain transparency for security, but lacks explicit details on access control (IAM), regulatory compliance (e.g., EU AI Act, GDPR), or traditional enterprise security policies.
Features a highly collaborative multi-agent ecosystem (Multi-Agent Pods) managed by the Swarm Manager. Primary threats include agent-to-agent trust abuse, cascading failures across pods, and rogue agent behavior within the decentralized swarm.
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.