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Odaily reports: Bitcoin Core developers have merged a security policy adjustment. For vulnerabilities that can be easily discovered or rediscovered through simple large language model (LLM) prompts, the previous long-term confidential disclosure mechanism will no longer be used. The new rules allow developers to mark such issues as "easy to find" during vulnerability classification, then backport fixes to all still-supported versions and release them as soon as possible through minor version updates, along with a security advisory and some vulnerability details.Previously, Bitcoin Core scheduled disclosure based on vulnerability severity, with medium- and high-risk vulnerabilities usually only made public after the affected versions stopped being maintained. This adjustment stems from AI tools lowering the threshold for vulnerability discovery: in July this year, a Bitcoin Red Team composed of 16 developers used AI models to scan about 390 Bitcoin-related projects and recorded 4,962 findings in one day, of which 85 were listed as critical issues. Developers noted that continuing to extend the confidentiality period for such vulnerabilities can no longer effectively prevent attacks and instead delays the release of fixed versions; however, the new rules also mean that fixes may lack longer testing time. Currently, no vulnerability has been officially marked as falling into the "easy to find" category under the new rules. (CoinDesk)
According to Cointelegraph, Bitcoin Core developer Antoine Poinsot stated on Thursday on the bitcoin-dev mailing list that large language models (LLMs) have made certain software vulnerabilities extremely easy to rediscover. Maintaining long-term secrecy is already of limited value and instead delays the release of patched versions, prompting the team to update its vulnerability disclosure policy. The new policy dictates that "easily discoverable" vulnerabilities, which can be identified with minimal cost such as through simple LLM prompts, will be patched as soon as possible across all supported major versions, alongside the simultaneous release of security advisories. Other vulnerabilities will continue to be disclosed based on severity: low-severity vulnerabilities will be disclosed two weeks after the release of the major version containing the fix; medium-to-high-severity vulnerabilities will be disclosed two weeks after the end-of-life (EOL) of the last affected version, typically around one year after the patch release; critical vulnerabilities will be handled through a dedicated process.
security firm CrowdStrike released an intelligence report stating that an unidentified hacker, between late September and early October, combined large language models (LLMs) with the domestic open-source AI penetration testing tool ARTEX to launch targeted cyberattacks against multiple South Korean financial institutions and steal data.After analyzing the hacker's exposed open directory, it was found that they employed a dual-server architecture centered on a Hong Kong IP, and used models including Claude Code, GLM-5.3, Grok 4.6, and DeepSeek v4.1-flash accessed through an API proxy to orchestrate the attack process. The hacker also asked Claude how to monetize the leaked South Korean data in Telegram groups. According to estimates by South Korean media, at least seven South Korean financial institutions were attacked, including KB Kookmin Bank, Shinhan Bank, and Hana Bank, with personal data of approximately 68,000 people leaked, involving sensitive information such as annual income and loan limits.
攻击手法方面，该组织已转向利用生成型 AI 制作高度仿真的虚拟资产与金融领域文档，用于精准鱼叉式钓鱼攻击，目标涵盖加密货币钱包信息、Gmail 账户及网站注册记录等敏感数据。Genians 安全中心负责人文钟铉警告，随着 AI 技术发展，社会工程攻击将持续精细化，建议企业部署以行为检测为核心的 EDR 威胁猎捕体系加以应对。
According to TechCrunch, the Open Safety AI Alliance (OSAA), led by Nvidia, has exceeded 120 member companies just one week after its establishment, including tech and financial giants such as Adobe, BlackRock, Cisco, Intel, Microsoft, and Visa. During the Black Hat Cybersecurity Conference held in Las Vegas this week, the alliance established a working group named "Shared AI Findings Exchange" (SAFE) and has submitted multiple proposals open for public comment, managed by the Linux Foundation. The proposals cover confidential reporting mechanisms for AI cybersecurity incidents, alert processes for affected parties, and no-fault attribution analysis frameworks. Meanwhile, member companies are also actively contributing open-source technologies: Nvidia open-sourced the LLM vulnerability scanning tool Garak, Amazon contributed the agent building tool Strands Agents and authorization language Cedar, and Okta and Red Hat are advancing agent identity authentication and governance technologies respectively. Notably, Anthropic, OpenAI, and Google have not yet joined the alliance, although OpenAI and Google previously co-signed the open letter that spurred the creation of the alliance.
Coinbase, a cryptocurrency trading platform, has disclosed in a technical sharing session that its internal multi-agent development tool "Mux" is reshaping software engineering workflows, transitioning the engineer's role from traditional code implementers to task orchestrators for AI agents.With the widespread internal adoption of AI programming tools such as Cursor, Copilot, OpenCode, and Claude Code, code generation efficiency has significantly improved. However, development workflows have long remained stuck in a traditional "single-task, single-branch, sequential execution" mode, creating a new collaboration bottleneck.Mux was born as an internal tool against this backdrop. By assigning each AI agent an independent git worktree, branch, and terminal environment, the system enables parallel multi-task development and conflict-free collaboration, allowing engineers to simultaneously direct multiple agents to handle tasks such as API development, test writing, vulnerability fixes, and code refactoring.Data shows that as of April 2026, Mux has covered over 600 users within Coinbase (including engineers, product managers, and designers), with 335 actively using it and 197 being high-frequency users. It has facilitated over 5,000 PR merges across 461 code repositories and 10 organizations. Engineers using Mux achieved an average of 39.6 PR merges, approximately 3.5 times the baseline of 11.4.Coinbase stated that Mux's success relies on its internal infrastructure capabilities, including an LLM Gateway, secure model access, and a code flow deployment system, enabling deep integration of multi-agent tools into real development workflows. This trend marks a structural shift in the software engineering paradigm: as AI reduces the cost of code generation, the core value of engineers is transitioning from "implementation capability" to "problem definition and agent orchestration capability."
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.