iAgent Pro — agentic threat model

Not certain from the listing — the exact foundation models (e.g., vision-language models or reinforcement learning architectures) used to process gameplay footage are unspecified. Potential threats include model stealing of these proprietary player-replicated models and adversarial examples in gameplay inputs.

Training data consists of user-submitted gameplay footage. Key threats include data poisoning (maliciously crafted gameplay to bias the agent) and data exfiltration of private user streams or metadata.

Not certain from the listing — the specific orchestration framework is not detailed. However, replicating player strategies implies a planning and execution framework where threats include insecure tool integration with game APIs and memory poisoning of behavioral states.

Deploys on decentralized computing infrastructure. This introduces significant infrastructure threats, including untrusted node execution, host compromise, lack of sandboxing on peer nodes, and potential exposure of secrets across a distributed network.

Not certain from the listing — there is no mention of evaluation, logging, or guardrails. Gaps here could lead to undetected drift in agent behavior or evaluation gaming where agents exploit game mechanics maliciously.

Not certain from the listing — compliance and identity controls are unspecified. The integration of blockchain for digital assets requires robust smart contract audits and identity verification to prevent unauthorized asset transfers or monetization fraud.

The agent ecosystem involves trading and monetizing agents as digital assets. Primary threats include rogue or compromised agents executing unauthorized trades, market manipulation, and cascading failures in decentralized marketplaces.