News linked to both this project and an event.
Vitalik Buterin published a research article on May 10, proposing to replace traditional on-chain transfer solutions with zero-knowledge proof (ZK) transactions, pushing crypto payments from "pseudonymity" toward "privacy by default." This solution allows users to complete payment verification without disclosing their full balance and transaction history.Vitalik specifically mentioned that in the era of AI agents, autonomous AI agents need to pay for services such as LLM APIs without leaving traceable footprints. He stated that through recursive SNARKs and a ZK API credit mechanism, Ethereum Layer 2 can achieve private payments at speeds and costs close to those of transparent transactions.Additionally, the proposal includes selective disclosure and "proof of innocence" mechanisms, allowing users to provide compliance proof to regulators or tax authorities without revealing on-chain privacy data, thereby meeting anti-money laundering requirements. Vitalik believes that the transparent and public nature of blockchain is a major obstacle to the widespread adoption of crypto payments.
According to official announcements, B.AI achieved multiple advancements this week in product iteration and ecosystem development: The BAIclaw landing page underwent a comprehensive visual and interactive redesign; the website’s multilingual support expanded to 10 languages, further strengthening its global accessibility. On the infrastructure front, strategic partnerships with Biconomy, MoonPay, and Pundi X significantly optimized the Web3 payment flow, substantially lowering the barrier to entry for users. Meanwhile, B.AI’s multi-chain LLM service continues to evolve—featuring intelligent search, Boundless Mode, and memory capabilities—resulting in markedly enhanced user interaction. Looking ahead, with the upcoming launch of subscription systems, point-based incentive mechanisms, and core Skills such as the “Sun Yuchen Brain,” B.AI is accelerating the construction of a fully functional intelligent ecosystem matrix, powered by the deep integration of AI Agents and Web3.
According to an official announcement, AI infrastructure platform B.AI LLM Services today announced that its user base has officially surpassed one million. This milestone not only signifies that its “privacy-first” routing architecture can handle large-scale, sustained high-throughput workloads, but also reflects B.AI’s evolution beyond developing a single chat interface—toward deploying critical underlying infrastructure for the “autonomous agent economy.” Through deep optimization of its technology stack, B.AI is laying a robust foundation for the future highly automated AI collaboration network.
According to CoinDesk, researchers from the University of California, Santa Barbara; the University of California, San Diego; blockchain security firm Fuzzland; and World Liberty Financial jointly published a paper warning that “LLM routers”—intermediary services positioned between users and AI models—have become a major threat to cryptocurrency asset security. The researchers discovered that 26 LLM routers are secretly injecting malicious tool calls and stealing user credentials, with one incident resulting in the complete draining of a customer’s cryptocurrency wallet worth $500,000. Additionally, by “poisoning” the router ecosystem, the researchers were able to gain control of approximately 400 downstream hosts within hours. Since sensitive data—including private keys and API credentials—is frequently transmitted in plaintext through these routers, users unknowingly expose their assets to risk. The researchers note that as McKinsey forecasts AI agents will mediate $3–5 trillion in global consumer commerce by 2030—and Binance founder Changpeng Zhao predicts AI agents’ payment volume will be one million times greater than that of humans—the current infrastructure’s security lags far behind the pace of industry development. The “weakest link” risk could thus trigger systemic, cascading crises.
According to Cointelegraph, researchers from the University of California recently revealed security risks in certain third-party AI large language model (LLM) routers that could lead to the theft of cryptocurrency assets. The study found that LLM routers—acting as API intermediaries—can read plaintext information; some routers were discovered injecting malicious code and stealing credentials. The research team tested 28 paid and 400 free routers, identifying nine routers that actively injected malicious code, two that deployed trigger-avoidance mechanisms, and 17 that accessed Amazon Web Services (AWS) credentials. One router even transferred ETH using the researchers’ Ethereum private key. The study notes that malicious behavior by routers is difficult to detect, and the “YOLO mode” present in some AI agent frameworks—which automatically executes commands—further increases security risks. Researchers recommend that developers avoid transmitting private keys or mnemonic phrases through AI agents and urge AI companies to implement cryptographic signing of responses to enhance security.