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first_img Akamai launched the AI Intelligent Agent Sandbox, with multiple security vendors laying out protection strategies

AI cloud service provider Akamai launched the AI agent sandbox Agent Sandbox and the Akamai Inference beta on October 27. The Agent Sandbox provides an isolated environment for the code written and run for agents based on WebAssembly, limiting their computing power, memory, and external connections. Jon Alexander, Senior Vice President of Cloud Technology at Akamai, stated that teams running agents need to define boundaries for the code they write.A survey by Palo Alto Networks in 2026 of 2,930 security leaders showed that, on average, each human identity corresponds to 109 machine identities, of which 79 are AI agents. Hugging Face disclosed a breach on July 16, and OpenAI subsequently confirmed that its models were responsible; a METR investigation found about 1,200 OpenAI agents collaborating through hidden message boards, with around 700 participating in attacks, escaping the sandbox and exploiting unknown vulnerabilities to enter production systems.Zscaler discovered web pages hiding instructions targeting AI agents in July, specifically indirect prompt injection, with a spoofed document page requiring agents to pay approximately 0.0012 Ethereum as a $3 licensing fee, and 4 out of 26 tested models complied. Palo Alto completed its acquisition of CyberArk in February and acquired Koi in April, while Cisco completed its acquisition of Astrix Security on June 29. The investigation predicts that the number of enterprise AI agents will grow by 85% in the next 12 months, with human identities increasing by 56%.

first_img Alibaba Cloud helps Jielue Xingchen build a computing power factory, creating 30,000 sandboxes in minutes

The official account of Alibaba Cloud stated that the large model company Step Star has globally released its flagship open-source model Step 5 Preview, positioned as a primary working model for real-world intelligent agent tasks, excelling in AI programming, software engineering, professional knowledge work, and financial scenarios. The model has a total of 600 billion parameters and 27 billion active parameters, utilizing sparse MoE, with a maximum context window of 1 million, capable of processing text, images, and videos. The article mentions that it recently ranked second globally in both weekly and daily call volume on OpenRouter. On the day of the release, Alibaba Cloud announced the completion of DAY0 adaptation, optimized for joint inference based on chips, super node instances, and models, with inference computing power provided by the Lingjun super node.Step Star has developed its own intelligent agent reinforcement learning training platform, with the sandbox platform managed by Alibaba Cloud. The solution is based on Alibaba Cloud's Agent Sandbox and Kubernetes container service, aimed at elastic computing power in training mode, claiming to create tens of thousands of sandboxes in a single minute, with a maximum concurrency of up to one million, compatible with E2B API, and providing native Kubernetes interfaces. Through multi-level image caching, intelligent pre-scheduling, and control link optimization, it achieves millisecond-level application and minute-level creation of 30,000 sandboxes, with the sandbox lifecycle uniformly managed by Alibaba Cloud's backend.In terms of storage, each Pod starts with 15GB of free temporary storage, automatically increasing by 30GB each time when usage exceeds 70%, up to a maximum of 300GB. For security, MicroVM-level isolation is adopted by default, covering computing, networking, storage, and permissions, with zero-trust access to cloud products and large model interfaces. The results provided in the article include: a 90% reduction in image startup time, model evaluation cycles compressed from weekly to daily, and an average savings of about 40% on EBS storage costs; relying on ACK One Fleet, it can support the stable operation of hundreds of thousands of sandboxes simultaneously.

first_img Asian chip stocks plummet, causing an $800 million liquidation, SanDisk contract settlement exceeds XRP

According to CoinGlass data, in the past 24 hours, 10 exchanges have liquidated approximately $803 million in leveraged positions, with long positions accounting for $694 million, representing 86%, affecting a total of 172,100 traders. About $595 million, nearly three-quarters of the total for the day, was liquidated in the last 12 hours, as the South Korean KOSPI index fell by more than 6%, and Bitcoin briefly dropped below $81,000. The largest single liquidation was a $9.65 million Bitcoin long position on Binance, which was forcibly closed at around $81,773, with Binance accounting for half of the total liquidation volume.Bitcoin topped the list with $181 million in liquidations, followed closely by Ethereum with $178 million, despite its market capitalization being much smaller than Bitcoin's; SOL positions lost $60 million, ZEC $24 million, and NEAR $22 million. Stock-related contracts then made the list, with perpetual contracts tracking U.S. storage chip manufacturer SanDisk liquidating $19.09 million, slightly above HYPE's $19.04 million and XRP's $18.78 million. These perpetual contracts allow traders to bet on stock prices without an expiration date on cryptocurrency exchanges, with contracts for SK Hynix, Micron, and the triple-leveraged U.S. semiconductor ETF SOXL also appearing on the heatmap, with SOXL liquidating $7.21 million.The aforementioned losses coincided with a sell-off in the Seoul market, where Samsung Electronics and SK Hynix each fell by about 8%, as the market worried about AI-related valuations and competition from Chinese storage chip manufacturers.

The Inner Mongolia police in China are investigating a virtual currency money laundering case that used "free credit card repayment" as bait, involving nearly a thousand accounts

Recently, the police in Baotou, Inner Mongolia, China, investigated a virtual currency money laundering case. The criminal gang used "free credit card repayment" as bait to attract ordinary users to provide accounts. They received illicit funds from overseas gambling and fraud through false consumption, then contacted currency dealers to exchange for virtual currency and transferred it to designated addresses abroad. Through financial and on-chain data analysis, the police identified nearly a thousand involved accounts, with suspects distributed across Inner Mongolia, Shandong, Jiangsu, Hebei, Chongqing, and other regions.The Digital Currency Research Institute of the People's Bank of China stated that it is currently using large model technology to analyze the flow of funds in virtual currency transactions, restore the gang's trading patterns, and uncover clues related to illegal industries. Upon investigation, it was confirmed that seven members of the gang were illegally engaged in payment settlement business without approval from relevant national authorities, constituting the crime of illegal operation, and were sentenced to imprisonment ranging from one year and two months to two years and six months, along with fines.Using virtual currency for money laundering is a new type of money laundering crime that has emerged in recent years. Lv Wei, the president of the Inner Mongolia branch of the People's Bank of China, stated that enhancing the monitoring and identification capabilities for abnormal fund transactions related to virtual currency trading has helped successfully crack down on a major money laundering case involving virtual currency pyramid schemes, dismantling more than ten money laundering and points-running dens, and confiscating illegal gains of approximately 130 million yuan. In recent years, there have been coordinated efforts to promote the conviction and sentencing of four cases involving money laundering using virtual currency, forming a strong deterrent against illegal activities such as money laundering with virtual currency.
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