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Анализ зависимостей бинарных файлов на основе ML

Всем привет! 👋 👋 👋 Мы стажеры-разработчики Тинькофф: Влад, Паша и Илья. В проекте по стажировкам в ИБ Summer of Code под руководством Ромы Лебедя мы реализовали анализатор бинарного кода на основе ML-подходов — Binary SCA. Наш проект совмещает две предметные области — информационную безопасность и ML, поэтому мы разделили статью на несколько частей.

В этой статье подробно расскажем о ML-стороне проекта: проведенные исследования, сложности, с которыми столкнулись в ходе работы, какой результат получили. В этой части делимся опытом использования Rizin и Milvus. Добро пожаловать!

Использование ML в SCA

Уровень абстракции в процессах разработки непрерывно повышается за счет большого количества готовых компонентов. С одной стороны, это приводит к ускорению реализации запроса, с другой — усложняет анализ на наличие проблем безопасности. Программисты и компании постоянно ищут инструменты, которые помогут им управлять зависимостями и гарантировать безопасность своего кода. В такие моменты на помощь приходит SCA, о котором рассказывал Влад:

Что не увидит SCA

Всем привет! 👋 👋 👋 Мы стажеры — разработчики Тинькофф: Влад , Паша и Илья. В проекте по стажировк...

habr.com

Насколько использование ML в SCA может быть оправданно? Рассмотрим случай, когда никакой метаинформации о зависимостях нет или ей нельзя доверять, но есть собранный бинарный файл. Это может быть, например, полученный от стороннего разработчика компонент.

Большинство существующих решений анализа программ на зависимости заточены как раз на наличие вспомогательной информации помимо бинарного файла, а рассматриваемый нами сценарий хоть и редкий, но самый сложный и в то же время вариативный на возможные пути решения. На помощь могут прийти способы сравнения дизассемблированного кода, разговор о которых мы вывели в отдельную статью.

Основные подходы для сравнения опираются на константы, эвристики и свойства графов, но все они легко ломаются простой обфускацией, сменой компилятора или настроек оптимизации. Кажется, что человек за какое-то неизвестное время сможет понять, что две программы, скомпилированные по-разному, на самом деле делают одно и то же, то есть должен быть какой-то способ различать бинарники независимо от способа их сборки.

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Можно вспомнить гипотезу Коллатца, ведь для этой короткой программы люди до сих пор не знают, эквивалентна ли она простому std::cout << 1; Задача проверки двух алгоритмов на эквивалентность алгоритмически неразрешима, но все же в нашем случае есть принципиальное ограничение — две программы на входе получены из некоторой начальной программы путем преобразований, гарантирующих алгоритмическую эквивалентность.

Люди при сравнении двух программ руководствуются в том числе и семантикой. И здесь на помощь приходит NLP, ведь именно этот раздел связан с выделением смысла из текстов. К сожалению, на тему использования ML именно для композиционного анализа практически нет исследований или работающих проектов. В первую очередь из-за того, что тема требует знаний как в ML, так и в ИБ, а множества людей, вовлеченных в каждую из областей, довольно слабо пересекаются. Кроме того, может возникнуть вопрос, насколько NLP-модели здесь применимы. Ведь они обучаются на человеческой речи и работают с ней, а мы хотим применить их к программам, да еще и на машинном коде.

Подходы для поиска бинарных клонов

Хоть и нет проверенного ML-решения именно для компонентного анализа, подходы для поиска бинарных клонов кода и оценки сходства кода с помощью ML-моделей все же есть. В нашей работе мы опирались на опыт двух архитектур — Asm2Vec и VulHawk.

Asm2Vec предлагает использовать Word2Vec для построения эмбеддингов инструкций ассемблера, но Word2Vec здесь доработан, чтобы учитывать особенности синтаксиса ассемблера. В итоге у них получилось, что близкие по функциям опкоды были близки в векторном пространстве.

Архитектура Asm2Vec

VulHawk предлагает более сложную и актуальную архитектуру, использующую трансформер RoBERTa. В такой архитектуре ассемблерный код подготавливается перед обработкой модели: адреса, константы и имена заменяются новыми спец-токенами. За большими подробностями рекомендуем обратиться к оригинальным статьям.

Архитектура VulHawk

Цель нашего проекта — разработать black-box-анализатор, который будет принимать пользовательский Jar-файл и выводить SBOM в формате CycloneDX. SBOM содержит информацию о библиотеках и их версиях, используемых в приложении.

Jar-файлы мы выбрали из-за удобной экосистемы, которая позволит быстро влиться в работу в рамках стажировки и облегчит проблему с данными и подсчетом метрик: в maven-репозитории есть подробная информация про java-пакеты, в том числе их зависимости. При желании и наличии датасета наше решение можно переложить на любую другую архитектуру или в другой формат. Помимо этого, мы должны учесть некоторые требования:

Устойчивость к смене компилятора, параметрам сборки, целевой платформы. Мы это делаем для того, чтобы наше решение могло работать с любыми файлами, предназначенными для разных архитектур и собранные разными способами

Возможность быстро пополнять базу известных зависимостей. Ежедневно база пополняется новыми библиотеками и их версиями. Мы должны гибко адаптироваться под эти изменения, чтобы предсказывать все новые и новые зависимости.

Точное определение набора зависимостей и их версий в собранном приложений без метаданных. Мы должны корректно находить библиотеки-кандидаты с максимальным количеством TP и минимальным числом FP.

Asm2Vec и VulHawk не смогут полностью решить нашу задачу: выделить из собранного проекта его компоненты. И с ходу непонятно, как локализовать потенциальные зависимости в коде и возможно ли это сделать как-то быстро и оптимально.

VulHawk сильно завязан на supervised-подходе, а Asm2Vec хоть и предлагает некоторый metric-learning, но использует в себе устаревший Word2Vec. Мы попытались совместить некоторые идеи из этих решений. Будем строить эмбеддинги для составных частей библиотек, благо в Java собранные проекты хорошо делятся на такие части.

Собранный проект на Java — это jar-файл, который, по сути, является zip-архивом. Внутри лежат бинарные .class-файлы, которые соответствуют классам исходной программы. На стартовом этапе исследования мы решили получать эмбеддинги для классов, но позже будем строить эмбеддинги и для методов.

Концепция SCAML

Сначала нам нужно было что-то, с чем можно сравниться. Как уже было отмечено, у нас не supervised-схема, но способ проверить приемлемость точности нашей модели должен быть. Так что мы придумали не ML-подход, который учитывает довольно мощную информацию из jar-файлов — константы. Все дело в том, что в константах jar-файлов без обфускации содержатся и полные имена методов и классов, так что точность подхода ожидается высокой.

Идея алгоритма в том, чтобы в качестве библиотек, входящих в проект, брать те, константы которых полностью содержатся во множестве констант проекта. Такой подход гарантирует отсутствие FN, то есть предсказание будет содержать все истинные зависимости. Но никаких гарантий относительно FP быть не может: например, константы библиотеки могут покрываться объединением констант других библиотек, тогда получим ложную библиотеку в предсказании. Но можно положиться на то, что среди констант есть названия методов и классов: такие специфичные константы могут помочь различать между собой разные библиотеки, но не всегда разные версии.

Реализовали подход с помощью Postgres базы данных: завели две таблички — под константы без повторений и под библиотеки, а также таблицу связи. Предварительно фильтровали константы: обрезали с конца по максимальной длине и не брали слишком мелкие, избавлялись от мусора. Тогда предсказание делается в один запрос: нужно по входным константам найти те библиотеки, константы которых полностью включаются во входное множество. Кроме того, можем получить неплохую асимптотику, если использовать индексы на столбцах таблицы, это $O(n\log m)$ , где m — количество записей в таблице, а n — количество входных констант.

Архитектура нашего проекта: определяем, какие библиотеки хотим выявить, потом части этих библиотек векторизируем алгоритмом, сближающим похожий по семантике код и отдаляющим разный. В итоге в векторном хранилище будут лежать векторы классов известных библиотек и информация о том, из какой библиотеки эти векторы. Все это — preprocessing, флоу данных показан красным на рисунке

Если мы хотим выявить из входного файла зависимости, то нам нужно опять векторизовать классы входного файла. Далее использовать блок Predition для поиска зависимости в проекте, флоу данных в этом случае обозначен синим на рисунке. Prediction обращается в хранилище и на основе некоторого алгоритма выдает наиболее вероятные библиотеки и их версии.

Выбор инструментов

Мы экспериментировали с разными моделями и пробовали построить Jar2Vec по аналогии с Asm2Vec, запускали BERT на константах, на сыром бинарнике и так далее. У каждой модели было свое преимущество, и мы пришли к такой концепции:

На вход принимаем JAR-файл.
Дизассемблируем с помощью Rizin и получаем CFG — Control-flow graph.
Обход по данному графу с помощью DeepWalk на методах классов и получаем «предложения» — возможные варианты исполнения метода. В этом же пункте мы убираем из кода имена вызываемых классов и адреса, как это было в VulHawk: программа должна концентрироваться именно на контексте исполнения программы и не должна переобучаться на какие-либо названия, которые могут легко измениться от сборки к сборке.
Получаем эмбеддинги методов с помощью модели-трансформера. Чтобы получить эмбеддинг класса, просто берем среднее эмбеддингов его методов.

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В качестве модели-трансформера мы выбрали RoBERTa, потому что она является улучшенной версией модели BERT, в которой дополнительные параметры и техники обучения были оптимизированы для достижения лучшей производительности. От обычного BERT он отличается по пяти пунктам:

Обучение на большем объеме данных: RoBERTa обучается на значительно большем объеме текстовых данных, чем BERT. Больший объем данных позволяет RoBERTa улучшить свое представление языка и семантическое понимание.
Оптимизация гиперпараметров: RoBERTa использует оптимизированные гиперпараметры, которые были тщательно подобраны для достижения более высокой производительности. Некоторые гиперпараметры, такие как размер пакета обучения и количество эпох, настроены в RoBERTa для более эффективного обучения.
Модель перестала обучаться на предсказание следующего предложения, что позволяет модели лучше и эффективнее обрабатывать контекст и информацию в предложениях.
Использование дополнительных значений предварительного обучения: RoBERTa использует дополнительные значения предварительного обучения, такие как длинные последовательности, динамическое обучение и т. д. Это помогает модели получать более точное представление языка и лучше справляться со сложными задачами обработки естественного языка.
Использует byte-level BPE-токенайзер, что лучше подходит под синтаксис.

В качестве инструмента для извлечения признаков из Jar-файлов мы выбрали rizin — open-source-инструмент и фреймворк для реверс-инжиниринга. Мы выбрали его, потому что:

у него открытый исходный код, поэтому он всегда будет доступен в России;
он поддерживает разные архитектуры и форматы файлов (не только Jar), что позволит в будущем облегчить анализ бинарных файлов для других платформ;
у него удобный API, для получения Control Flow Graph нужно всего несколько простых команд:

icm — получить список методов в классе 
s <addr> — переместиться на начало нужного метода 
af — проанализировать метод 
agf — вывести CFG

Мы начали пользоваться rizin'ом и поняли, что поддержка jvm в нем несовершенна:

некорректно обрабатывались опкоды tableswitch и lookupswitch, в результате некорректно дизассемблировались switch-case-конструкции;
rizin не понимал, что throw — это конец пути исполнения кода, и пытался дизассемблировать опкоды после него;
в целом в rizin не учитывается, что в jvm нельзя прыгать за пределы метода, из-за этого неправильно распаршенные опкоды часто дизассемблировались как прыжки в другие части class-файла;
и еще пара случаев, когда rizin некорректно парсил опкоды: иногда считал, что case-1 — это невалидный опкод или некорректно обрабатывал wide инструкции jvm.

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Код, дизасемблированный rizin до патча. Видно, как некорректно парсится опкод tableswitch

Код, дизасемблированный rizin после патча

Мы потратили пару недель на доработку инструмента. Возможно, в будущем откроем pull-реквест в апстрим.

Мы научились дизассемблировать библиотеки для получения фич, на их основе с помощью RoBERTa мы получаем вектора и складываем в некоторое хранилище. Пусть нам на вход пришел собранный проект для анализа, содержащий известную нам библиотеку X. Тогда заметим следующее:

Если этот проект при сборке не был обфусцирован, то множество его векторов включает в себя векторы X — красные точки. Если же проект как-либо обфусцирован, то мы ожидаем, что векторы, отвечающие классам библиотеки X, будут близки к оригинальным версиям

Алгоритм получился такой:

— для каждого вектора входного файла находим n_neighbours ближайших, лежащих в некоторой $\varepsilon$ -окрестности;

— получаем некоторое множество Y векторов из базы данных, для которых мы знаем, из какой библиотеки они пришли;

— находим те библиотеки, классы которых полностью включаются в извлеченное множество Y. Их и будем выдавать в качестве предсказания, ведь все их классы оказались рядом с классами входного файла.

Остается пара неясных моментов:

Какую базу данных для векторов использовать, чтобы иметь возможность делать быстрый поиск ближайших и пополнять новыми зависимостями?

Насколько эта схема вообще оправданна? Ведь она основывается на предположении, что при обфускации эмбеддинг класса окажется рядом с исходным, и нам нужен какой-то способ это проверить.

Для хранения векторов мы решили использовать Milvus — open source векторную базу данных, предоставляющую все необходимые для нашей задачи функции. Чтобы обеспечить быстрый поиск ближайших векторов, Milvus предлагает различные ANNS-алгоритмы, которые зачастую подразумевают предварительное построение на данных некой структуры — индекса.

Разные индексы предоставляют разный трейд-офф между скоростью, используемыми ресурсами и точностью поиска. Например, индекс, значащийся в документации как IVF_FLAT, кластеризует точки датасета и производит поиск в два этапа: сначала находит nprobe ближайших кластеров, а потом линейным поиском ищет ближайшие внутри этих кластеров.

Схематичный пример работы IVF_FLAT: q — точка запроса, c_0 и c_1 — ближайшие центры кластеров, тогда поиск будет проводиться среди точек v_0,...v_6

Milvus в реализации ANNS использует низкоуровневые оптимизации: например, векторы запроса кладут в L3-кеш процессора, чтобы уменьшить количество обращений в ОЗУ, реализуют поиск на нескольких ядрах с помощью OpenMP.

$Векторы запроса — q0,...,q_m — делятся на блоки, каждый из которых кладется в L3-кеш. Каждый из t потоков считает расстояние между векторами из базы v_0,...,v_{b-1} и текущим блоком в L3 — результаты кладутся в t соответствующих куч, итоговый результат получается слиянием куч$

Векторы запроса — q0,...,q_m — делятся на блоки, каждый из которых кладется в L3-кеш. Каждый из t потоков считает расстояние между векторами из базы v_0,...,v_{b-1} и текущим блоком в L3 — результаты кладутся в t соответствующих куч, итоговый результат получается слиянием куч

Будем складывать в milvus вектора с информацией, из какой библиотеки они пришли.

Тестирование и проверки, что подход рабочий

Мы искали способ убедиться, что наш алгоритм анализа действительно устойчив к обфускации. Для этого выделили датасет на 3000 maven-библиотек, получили векторы для них и положили на mivlus. На вход модели давали каждую из библиотек и ожидали на выходе получить ее же. Считали совпадение с точностью до версии и с точностью до библиотеки.

Модель может выдавать на входе несколько пакетов, по сути, это многоклассовая классификация, так что имеет смысл для каждой библиотеки считать precision и recall предсказания для нее. Мы смотрели лишь на precision — в нашем случае recall может быть либо 0, либо 1, эту информацию увидим из анализа precision.

Классические метрики многоклассовой классификации: precision показывает долю верно предсказанных зависимостей в предсказании, recall — долю верно предсказанных зависимостей среди истинных

Для проверки возможностей детектировать выбранным методом обфусцированные библиотеки решили подавать те же самые библиотеки, но обфусцированные с помощью Proguard. Хоть и даем на входе одну библиотеку, мы все еще имеем дело с многоклассовой классификацией, ведь на выходе можем получить несколько библиотек. Так что предлагается оценивать результаты поэлементно, для каждой библиотеки считая precision ее предсказания.

При первых попытках испытать модель выяснилось, что из-за повторов векторов часто возникали ситуации, что эмбеддинг истинной библиотеки не находился. К примеру, 10 векторов совпали с истинным, тогда мы можем не увидеть истинный. Пришлось добавить эвристики поиска, и первое, что пришло в голову, — учитывать только уникальные векторы. Вот результаты для n_neighbours=7:

Все еще много случаев с precision=0, так что мы продолжили исследовать частные примеры и выяснили, что разные версии одной и той же библиотеки часто оказываются неразличимы: множества их векторов оказываются достаточно близки или же вообще совпадают. Поэтому мы поменяли определение уникальности — теперь считаем уникальным тот класс, который встречается в пределах разных версий одной библиотеки. В итоге получили метрики:

Как видим, на библиотеках без обфускации все метрики выросли — если брать совпадение с точностью до библиотеки, то здесь средний preсision вообще 0.96, очень хороший результат. Среднее метрик при обфускации просело, но на самом деле это из-за учащения FP исходов, а не из-за случаев с precision=0 — их количество практически не изменилось, а где-то даже упало.

Стоит отметить, что в результатах выше была использована предобученная модель. Основная проблема с дообучением здесь в разметке данных: какие элементы мы можем считать похожими, а какие нет. Мы пробовали дообучать на триплетах, считая похожими изменившиеся классы в разных версиях одной библиотеки, но это не дало результата: метрики на этом же датасете стали даже хуже. Но поисследовать эмбеддинги можно будет позже, пока что нужно сконцентрироваться на том, чтобы протестировать модель на большом датасете.

Что касается бейзлайн-подхода, то также проверили аналогичные метрики на нашем датасете. Только метрик для обфусцированных версий на рисунке нет — там нулевая точность, потому что имена методов и классов затерты.

Выводы и несколько полезных ссылок

Использование полученных эмбеддингов имеет ряд допущений и, как следствие, набор ограничений. Если не брать продвинутые приемы обфускации кода, использование эмбеддингов кода для выявления зависимостей можно развивать. Основное преимущество такого подхода — относительно быстрый поиск по базе индексированного кода, быстрое добавление новых пакетов в индекс.

После вводных экспериментов мы приступили к раскатке модели на больший датасет размером порядка 105 тысяч библиотек. Мы столкнулись с множеством сложностей, начиная с несостоятельности нашей схемы хранения данных, заканчивая проблемами с данными и невозможностью точно оценить качество модели. Но о том, как мы преодолевали эти вызовы, расскажем в следующей статье.

На прощание несколько ссылок для тех, кто хочет больше погрузиться в тему:

Статья с задачей о схожести бинарного кода на sciencedirect.com. Предлагают использовать гибридную сетку — CNN + LSTM. Анализ разделяется на три этапа. Во-первых, используется IDA Pro для статического анализа и дизассемблирования. На втором этапе используется моделька Word2Vec + сетка, чтобы классифицировать архитектуру процессора и уровни оптимизации компилятора. После этого нейронная сеть Siamese NN детектирует схожесть.
В статье на ndss-symposium.org предлагают подход к решению задачи поиска похожих блоков кода в двух входных бинарных файлов. Строят эмбеддинги блоков с помощью Word2Vec, но дополнительно вводятся веса для инструкций — их TF-IDF. Далее приближенно решается задача о назначениях с помощью жадного алгоритма.
В статье на ieeexplore.ieee.org предлагают решение задачи поиска клонов кода на основе графовой информации: CFG, AST, PDG.

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28 февр. 2024 г.

07.09.23 16:24 CherryTeam

Cherry Team atlyginimų skaičiavimo programa yra labai naudingas įrankis įmonėms, kai reikia efektyviai valdyti ir skaičiuoti darbuotojų atlyginimus. Ši programinė įranga, turinti išsamias funkcijas ir patogią naudotojo sąsają, suteikia daug privalumų, kurie padeda supaprastinti darbo užmokesčio skaičiavimo procesus ir pagerinti finansų valdymą. Štai keletas pagrindinių priežasčių, kodėl Cherry Team atlyginimų skaičiavimo programa yra naudinga įmonėms: Automatizuoti ir tikslūs skaičiavimai: Atlyginimų skaičiavimai rankiniu būdu gali būti klaidingi ir reikalauti daug laiko. Programinė įranga Cherry Team automatizuoja visą atlyginimų skaičiavimo procesą, todėl nebereikia atlikti skaičiavimų rankiniu būdu ir sumažėja klaidų rizika. Tiksliai apskaičiuodama atlyginimus, įskaitant tokius veiksnius, kaip pagrindinis atlyginimas, viršvalandžiai, premijos, išskaitos ir mokesčiai, programa užtikrina tikslius ir be klaidų darbo užmokesčio skaičiavimo rezultatus. Sutaupoma laiko ir išlaidų: Darbo užmokesčio valdymas gali būti daug darbo jėgos reikalaujanti užduotis, reikalaujanti daug laiko ir išteklių. Programa Cherry Team supaprastina ir pagreitina darbo užmokesčio skaičiavimo procesą, nes automatizuoja skaičiavimus, generuoja darbo užmokesčio žiniaraščius ir tvarko išskaičiuojamus mokesčius. Šis automatizavimas padeda įmonėms sutaupyti daug laiko ir pastangų, todėl žmogiškųjų išteklių ir finansų komandos gali sutelkti dėmesį į strategiškai svarbesnę veiklą. Be to, racionalizuodamos darbo užmokesčio operacijas, įmonės gali sumažinti administracines išlaidas, susijusias su rankiniu darbo užmokesčio tvarkymu. Mokesčių ir darbo teisės aktų laikymasis: Įmonėms labai svarbu laikytis mokesčių ir darbo teisės aktų, kad išvengtų baudų ir teisinių problemų. Programinė įranga Cherry Team seka besikeičiančius mokesčių įstatymus ir darbo reglamentus, užtikrindama tikslius skaičiavimus ir teisinių reikalavimų laikymąsi. Programa gali dirbti su sudėtingais mokesčių scenarijais, pavyzdžiui, keliomis mokesčių grupėmis ir įvairių rūšių atskaitymais, todėl užtikrina atitiktį reikalavimams ir kartu sumažina klaidų riziką. Ataskaitų rengimas ir analizė: Programa Cherry Team siūlo patikimas ataskaitų teikimo ir analizės galimybes, suteikiančias įmonėms vertingų įžvalgų apie darbo užmokesčio duomenis. Ji gali generuoti ataskaitas apie įvairius aspektus, pavyzdžiui, darbo užmokesčio paskirstymą, išskaičiuojamus mokesčius ir darbo sąnaudas. Šios ataskaitos leidžia įmonėms analizuoti darbo užmokesčio tendencijas, nustatyti tobulintinas sritis ir priimti pagrįstus finansinius sprendimus. Pasinaudodamos duomenimis pagrįstomis įžvalgomis, įmonės gali optimizuoti savo darbo užmokesčio strategijas ir veiksmingai kontroliuoti išlaidas. Integracija su kitomis sistemomis: Cherry Team programinė įranga dažnai sklandžiai integruojama su kitomis personalo ir apskaitos sistemomis. Tokia integracija leidžia automatiškai perkelti atitinkamus duomenis, pavyzdžiui, informaciją apie darbuotojus ir finansinius įrašus, todėl nebereikia dubliuoti duomenų. Supaprastintas duomenų srautas tarp sistemų padidina bendrą efektyvumą ir sumažina duomenų klaidų ar neatitikimų riziką. Cherry Team atlyginimų apskaičiavimo programa įmonėms teikia didelę naudą - automatiniai ir tikslūs skaičiavimai, laiko ir sąnaudų taupymas, atitiktis mokesčių ir darbo teisės aktų reikalavimams, ataskaitų teikimo ir analizės galimybės bei integracija su kitomis sistemomis. Naudodamos šią programinę įrangą įmonės gali supaprastinti darbo užmokesčio skaičiavimo procesus, užtikrinti tikslumą ir atitiktį reikalavimams, padidinti darbuotojų pasitenkinimą ir gauti vertingų įžvalgų apie savo finansinius duomenis. Programa Cherry Team pasirodo esanti nepakeičiamas įrankis įmonėms, siekiančioms efektyviai ir veiksmingai valdyti darbo užmokestį. https://cherryteam.lt/lt/
08.10.23 01:30 davec8080

The "Shibarium for this confirmed rug pull is a BEP-20 project not related at all to Shibarium, SHIB, BONE or LEASH. The Plot Thickens. Someone posted the actual transactions!!!! https://bscscan.com/tx/0xa846ea0367c89c3f0bbfcc221cceea4c90d8f56ead2eb479d4cee41c75e02c97 It seems the article is true!!!! And it's also FUD. Let me explain. Check this link: https://bscscan.com/token/0x5a752c9fe3520522ea88f37a41c3ddd97c022c2f So there really is a "Shibarium" token. And somebody did a rug pull with it. CONFIRMED. But the "Shibarium" token for this confirmed rug pull is a BEP-20 project not related at all to Shibarium, SHIB, BONE or LEASH.
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HOW TO RECOVER MONEY LOST IN BITCOIN/USDT TRADING OR TO CRYPTO INVESTMENT !! Hi all, friends and families. I am writing From Alberton Canada. Last year I tried to invest in cryptocurrency trading in 2023, but lost a significant amount of money to scammers. I was cheated of my money, but thank God, I was referred to Hack Recovery Wizard they are among the best bitcoin recovery specialists on the planet. they helped me get every penny I lost to the scammers back to me with their forensic techniques. and I would like to take this opportunity to advise everyone to avoid making cryptocurrency investments online. If you have already lost money on forex, cryptocurrency or Ponzi schemes, please contact [email protected] or WhatsApp: +1 (757) 237–1724 at once they can help you get back the crypto you lost to scammers. BEST WISHES. Celina Garcia.
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23.09.24 18:56 matthewshimself

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02.10.24 22:27 Emily Hunter

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31.10.24 00:13 ytre89

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02.11.24 14:44 diannamendoza732

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12.11.24 00:50 TERESA

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17.11.24 09:31 Vivianlocke223

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21.11.24 08:02 Emily Hunter

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22.11.24 23:43 teresaborja

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I never could have imagined the nightmare of losing access to my digital wallet. All of my cryptocurrency holdings were abruptly imprisoned, inaccessible, and appeared to be lost forever following a catastrophic hardware breakdown. Years of meticulous investment and careful saving were reduced to nothing more than strings of code that I could no longer control, and I could feel the dread and sorrow that swept through me at that very instant. Thankfully, during my worst moment, I came into (TRUST GEEKS HACK EXPERT), a professional service devoted to recovering lost or inaccessible digital data. With optimism, I went out to their team of skilled technologists, laying bare the full nature of my issue. What followed was a laborious, multi-step process that required an almost surgical level of digital forensics and Bitcoin skill. In order to create a thorough profile of my wallet's contents and activities, the (TRUST GEEKS HACK EXPERT) team first thoroughly examined the transaction history and metadata connected to it. Next, they implemented a series of advanced recovery techniques, using cutting-edge software tools to bypass the access barriers that had left me locked out. The entire process was shrouded in secrecy and discretion, with the (TRUST GEEKS HACK EXPERT) team working tirelessly to protect the confidentiality of my sensitive financial information. After what felt like an eternity of nervous anticipation, the day finally arrived when I received the triumphant notification – my wallet had been successfully restored, and all of my precious digital assets had been returned to my control. The sense of relief was indescribable, as I could finally breathe easy knowing that the fruits of my financial discipline had been safeguarded. While the experience of losing access to my wallet was undoubtedly traumatic, (TRUST GEEKS HACK EXPERT) intervention allowed me to emerge from the ordeal with my cryptocurrency holdings intact, and a renewed appreciation for the importance of proactive digital asset management. You can contact Them through EMAIL: [email protected] - TELEGRAM: TRUSTGEEKSHACKEXPERT
25.11.24 02:19 briankennedy

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26.11.24 23:12 rickrobinson8

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27.11.24 00:39 [email protected]

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27.11.24 09:10 Michal Novotny

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01.12.24 17:21 KollanderMurdasanu

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02.12.24 23:02 ytre89

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04.12.24 22:24 andreygagloev

When I first heard about Bitcoin back in 2018, I was skeptical. The idea of a decentralized, digital currency seemed too good to be true. But I was intrigued as I learned more about the technology behind it and its potential. I started small, investing just a few hundred dollars, dipping my toes into the cryptocurrency waters. At first, it was exhilarating to watch the value of my investment grow exponentially. I felt like I was part of the future, an early adopter of this revolutionary new asset. But that euphoria was short-lived. One day, I logged into my digital wallet only to find it empty - my Bitcoin had vanished without a trace. It turned out that the online exchange I had trusted had been hacked, and my funds were stolen. I was devastated, both financially and emotionally. All the potential I had seen in Bitcoin was tainted by the harsh reality that with decentralization came a lack of regulation and oversight. My hard-earned money was gone, lost to the ether of the digital world. This experience taught me a painful lesson about the price of trust in the uncharted territory of cryptocurrency. While the technology holds incredible promise, the risks can be catastrophic if you don't approach it with extreme caution. My Bitcoin investment gamble had failed, and I was left to pick up the pieces, wiser but poorer for having placed my faith in the wrong hands. My sincere appreciation goes to MUYERN TRUST HACKER. You are my hero in recovering my lost funds. Send a direct m a i l ( muyerntrusted ( @ ) mail-me ( . )c o m ) or message on whats app : + 1 ( 4-4-0 ) ( 3 -3 -5 ) ( 0-2-0-5 )
12.12.24 00:35 amandagregory

HOW TO HIRE A HACKER TO RECOVER STOLEN BITCOIN WITH FASTFUND RECOVERY
12.12.24 00:35 amandagregory

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19.12.24 17:07 rebeccabenjamin

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