Хакер вывел из протокола Abracadabra токены на $1,8 млн

cryptonews.ruPubblicato 2025-09-05Pubblicato ultima volta 2025-10-06

Протокол децентрализованных финансов Abracadabra потерял токены Magic Internet Money (MIM) на сумму около $1,8 млн из-за хакерской атаки — злоумышленник воспользовался уязвимостью в смарт-контракте.

Компания BlockSec Phalcon, специализирующаяся на информационной безопасности, выяснила, что хакер смог обойти проверку платежеспособности и этапы верификации. Это позволило ему извлечь из протокола 1,79 млн токенов MIM. После атаки злоумышленник обменял украденные активы на эфиры, а затем перевел их в криптомиксер Tornado Cash.

Участник DAO, использующий псевдоним 0xMerlin, сообщил, что казначейство совершило обратный выкуп украденных токенов MIM. Средства пользователей не пострадали. В некоторых устаревших контрактах был выявлен потенциальный вектор атаки, но теперь проблема уже устранена, написал 0xMerlin. Он также сообщил, что команда Abracadabra пересматривает внутреннюю работу протокола, чтобы предотвратить возникновение подобных проблем в будущем.

По данным Abracadabra, сейчас в обращении находится почти 44 млн токенов MIM, большая часть которых торгуется в блокчейне Эфириума и в решении второго уровня Arbitrum. Общая стоимость заблокированных активов в протоколе (TVL) составляет $154 млн.

Это уже третья серьезная атака на проект Abracadabra. В январе 2024 года он потерял $6,5 млн в эфирах из-за аналогичной уязвимости в смарт-контракте, которая также позволила обойти проверки на неплатежеспособность. В марте 2025 года хакеры совершили еще одну атаку на протокол, украв токены MIM на $13 млн посредством атаки мгновенного займа. В общей сложности, с 2024 года протокол потерял из-за эксплойтов более $21 млн.

По данным аналитиков PeckShield, в сентябре больше всего от хакеров пострадала платформа UXLINK, которая потеряла $44 млн. Хакеры нацелились на сектор DeFi из-за высокой ликвидности этого рынка, пояснили в PeckShield.

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IPO Imminent, OpenAI Faces Major Personnel Upheaval

OpenAI, preparing for a potential IPO, is experiencing significant leadership turmoil. In mid-August 2026, longtime "GPU geek" Scott Gray quietly left, and within three days, Chief Operating Officer Brad Lightcap (8-year veteran) and Chief Revenue Officer Denise Dresser (8-month tenure) departed. This follows a broader exodus of at least 10 senior executives in 2026, including heads of product, safety, and ethics. Analysts view this as a strategic "surgery" to transform from a research lab into a sales-driven enterprise company before going public. Revenue now tilts toward enterprise clients, surpassing consumer income sooner than expected, with annualized revenue reaching $40 billion. The new CRO, Dali Rajic, is a veteran enterprise sales leader. Concurrently, OpenAI has disbanded independent safety teams like "Preparedness," which assessed catastrophic risks, integrating their functions into core research. Critics warn this removes dedicated "brakes" on AI development. The leadership vacuum raises questions about who is the clear second-in-command after former apps CEO Fidji Simo moved to an advisory role. Co-founder Greg Brockman appears to be consolidating power. As OpenAI races against rival Anthropic ($47B annualized revenue), it faces the dual challenge of commercial execution while managing the departure of foundational technical talent and ensuring responsible AI development remains a priority.

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The article discusses research from the University of Science and Technology of China and Oxford, revealing that allowing AI to recognize and act upon simulated "internal emotions" can significantly improve its performance. The study demonstrates a coherent pairing between specific emotional states in AI agents and their subsequent skill choices. For instance, an agent feeling curious and desirous will search for products, while one feeling confused and tense will rephrase queries. This mirrors human decision-making influenced by emotions. Statistical validation showed a 76.5% semantic consistency in these pairings. Crucially, the research challenges the traditional view of AI errors as flaws to be eliminated. It found that "bad" emotions like confusion, tension, or frustration serve as useful metacognitive signals, indicating a mismatch between the current strategy and the environment. By responding to these signals, AI can proactively adjust before a failure occurs. This is particularly effective in complex tasks prone to failure. For example, in tasks like "heating an item" and "picking up two items," success rates surged from 9.6% to 56.9% and 4.4% to 31.3%, respectively, when using the emotion-driven skill selection method (EMOTION2SKILL). The AI's "nervous" state about a closed microwave, for instance, prompted it to check and open it first, preventing failure. The article also mentions related work from Tianjin University, which embeds emotional prediction into world models (Large Emotional World Model, LEWM), significantly improving prediction accuracy in human-centric environments. Removing emotional data was found to degrade performance even in unrelated logical reasoning tasks. These studies build on earlier findings, like those from Anthropic, that identifiable emotional representations exist within large language models (LLMs). The focus is shifting from philosophical debate about AI emotion to practically harnessing these internal states as functional signals to enhance AI robustness and capability.

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