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first_img OpenAI's new model Astra can autonomously discover and exploit software vulnerabilities, rated as "critical" in cybersecurity capability level

OpenAI stated that its upcoming Astra model can autonomously discover previously unknown software vulnerabilities and convert them into usable attack vectors without human intervention, making it the company's first model to reach the "Critical" cybersecurity capability level threshold. In a blog post released on Tuesday, OpenAI mentioned that according to its Preparedness Framework, reaching this level means the model can discover zero-day vulnerabilities and develop usable exploit code in hardened real systems without human involvement, or design and execute attacks based solely on a high-level objective.In testing, Astra achieved a 100% score in benchmark tests for developing exploit code based on known vulnerabilities and discovered two previously unknown vulnerabilities in another internal test. Additionally, the model successfully broke through a hardened browser sandbox and executed commands on the host machine, while gaining root access by exploiting multiple weaknesses in the operating system. OpenAI stated that it has delayed some of Astra's development progress to enhance security measures and plans to make its advanced cybersecurity capabilities available only to selected testers.This capability is particularly relevant to the cryptocurrency industry, as software vulnerabilities can be converted into financial losses within minutes. CoinDesk reported in June that increasingly powerful AI models can compress the process of searching code, discovering misconfigurations, and assembling attacks from days or weeks to machine speed. Security researchers noted at the time that the significant change was not the emergence of new categories of attacks, but rather the dramatically increased speed at which existing vulnerabilities are discovered and exploited.

Gemini receives arbitration support: no liability for the collapse of the Earn lending program

According to CNBC, Gemini Space Station won a legal victory in August, with arbitrators ruling that the cryptocurrency exchange platform did not mislead users and is not responsible for the collapse of its Earn lending program. The claim was made by a user of the digital asset company's lending program Earn at the end of 2024. According to the ruling, there was insufficient evidence to prove that Gemini lied to customers or was negligent in its due diligence with its main lending partner, Genesis Global Capital.The Earn program was launched in 2021, allowing users to earn up to 7.4% annualized returns by lending cryptocurrency. Under this program, Gemini lent assets to institutional borrowers, with Genesis acting as an intermediary. However, in November 2022, Gemini suspended withdrawals from the Earn program, angering some of its more than 300,000 users. This move came shortly after Genesis suspended new loan issuance and redemptions due to a liquidity crisis caused by the downturn in the cryptocurrency market that year. After the freeze on Earn withdrawals, several customers filed legal complaints against Gemini. The New York Attorney General also sued Gemini over the Earn program and reached a $50 million settlement with the company in 2024.In February 2024, Gemini announced that the company had reached a "principled settlement" with Genesis and other creditors regarding the Genesis bankruptcy case. Three months later, Earn users received $2.18 billion in digital assets in physical form, equivalent to 97% of the digital assets owed to Earn users, which is $1 billion more than when Genesis suspended withdrawals in 2022.

Vice Governor of the Central Bank Lu Lei: The boundaries of responsibility for intelligent payment systems cannot be ambiguous, and a self-discipline convention will be released

According to Mobile Payment Network, Lu Lei, a member of the Party Committee and Vice President of the People's Bank of China, stated at the 15th China Payment Clearing Forum that intelligent agent payments must not blur the boundaries of responsibility between consumers, operating institutions, and algorithm systems. Lu Lei believes that the essence of payment is the transfer of fund ownership, which objectively requires that the results of transactions are predictable, responsibilities are definable, and traces are traceable. Large models and autonomous intelligent agents have characteristics such as output randomness and insufficient transparency of logic. If transaction decision-making authority is blindly or excessively granted to intelligent agents, it will affect the trust foundation of fund transactions. The current governance rules of the payment industry and dispute resolution mechanisms are built around "humans as the final decision-makers in transactions." The new model of intelligent agents automatically initiating and assisting in transactions easily blurs the boundaries of responsibility, and the existing governance rules need to be optimized and improved.Regarding the issue of insufficient compatibility of protocol standards in the field of intelligent agent payments, Lu Lei emphasized that the dispute over protocols is essentially a dispute over business rules and technical standards, as well as a struggle for dominance in the era of artificial intelligence. The People's Bank of China continues to strengthen its tracking research on technological innovation, especially intelligent agent payments, guiding the Payment Clearing Association to leverage its advantages in industry self-regulation. Based on extensive soliciting of opinions, they will formulate and publish the "Self-Regulatory Convention for Intelligent Agent Payment Applications," and will continue to work on coordinating protocols and standards, as well as innovating risk governance. Lu Lei proposed three hopes to market institutions: actively respond to and implement the industry self-regulatory convention, with payment security and risk prevention as the bottom line, and consumer rights protection as the focal point; continuously track the trends of cutting-edge technologies such as large models and intelligent agents both domestically and internationally, and build technical reserves and application capabilities; adhere to the principle of rules and standards first, strengthen coordination and compatibility among different protocols and standards, and cooperate with regulatory authorities to promote the construction of a foundational protocol and technical standard system for intelligent agent payments.
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