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В Spring MVC есть классная фича — autobinding. Но если пользоваться ей неправильно, могут появиться «незаметные» уязвимости, иногда с серьёзным импактом. Рассмотрим пару примеров, углубимся в тонкости появления autobinding-багов. Writeup [ENG]: http://agrrrdog.blogspot.ru/2017/03/autobinding-vulns-and-spring-mvc.html

[Defcon Russia #29] Алексей Тюрин - Spring autobinding

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Руткиты в мире основанных на ядре Linux операционных систем уже не являются редкостью. Рассказ будет о том, как попытки в современных реалиях определить то, скомпрометирована ли система, привели к неожиданному результату.

[Defcon Russia #29] Михаил Клементьев - Обнаружение руткитов в GNU/Linux

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Мы поговорим об общей проблеме валидации входных данных и качестве их обработки. Интерпретация входящих данных оказывает прямое влияние на решения, принимаемые в физической инфраструктуре: если какая-либо часть данных обрабатывается недостаточно аккуратно, это может повлиять на эффективность и безопасность процесса. В этой беседе мы обсудим атаки на процесс обработки данных и природу концепции «never trust your inputs» в контексте информационно-физических систем (в общем смысле, то есть любых подобных систем). Для иллюстрации проблемы мы используем уязвимости аналого-цифровых преобразователей (АЦП), которые можно заставить выдавать поддельный цифровой сигнал с помощью изменения частоты и фазы входящего аналогового сигнала: ошибка масштабирования такого сигнала может вызывать целочисленное переполнение и дает возможность эксплуатировать уязвимости в логике PLC/встроенного ПО. Также мы покажем реальные примеры использования подобных уязвимостей и последствия этих нападений.

[DCG 25] Александр Большев - Never Trust Your Inputs or How To Fool an ADC

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Cisco network equipment has always been an attractive attack target due to its prevalence and the key role that it plays in network structure and security. This equipment is based on a wide variety of OS (firmware) architectures, types, and versions, so it is much harder to develop a universal shellcode. Publicly available Cisco IOS shellcodes are tailored to specific equipment, have narrow functionality, and are not exactly useful for penetration testing. This talk is the presentation of a research initiated by our research center to create a shellcode which is as easily portable between different IOS firmwares as possible and which provides a lot of pentesting features because it can dynamically change the shellcode destination at the stage of post-exploitation. We will also consider the possibility of creating a worm which could spread across the infrastructure, from firewall to router, from router to switch, etc.

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Расскажу где и как iCloud Keychain хранит пароли, и какие потенциальные риски это несёт. Apple утверждает, что пароли надежно защищены, и даже её сотрудники не могут получить к ним доступ. Чтобы это подтвердить или опровергнуть, необходимо разобраться с внутренним устройством iCloud Keychain, чем мы и займемся.

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Все шире и шире получают распространение bugbounty программы - программы вознаграждения за уязвимости различных вендоров. И порой при поиске уязвимостей находятся места, которые явно небезопасны (например - self XSS), но доказать от них угрозу сложно. Но чем крупнее (хотя, скорее адекватнее) вендор, тем они охотнее обсуждают и просят показать угрозу от сообщенной уязвимости, и при успехе – вознаграждают 8). Мой доклад – подборка таких сложных ситуаций и рассказ, как же можно доказать угрозу.

Sergey Belov - Покажите нам Impact! Доказываем угрозу в сложных условиях

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Мои последние исселодования показали, что у SharePoint есть определенное количество вэб сервисов, которые могут помочь аудитору собрать крайне полезную информацию с сайта (списки пользователей, групп, ролей и т д). Также данные сервисы, в комбинации с хранимыми XSS, могут быть использованы для вертикального повышения привилегий или предоставить информацию для компрометации доменных записей AD.

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Retrieval augmented generation (RAG) is the most popular style of large language model application to emerge from 2023. The most basic style of RAG works by vectorizing your data and injecting it into a vector database like Milvus for retrieval to augment the text output generated by an LLM. This is just the beginning. One of the ways that we can extend RAG, and extend AI, is through multilingual use cases. Typical RAG is done in English using embedding models that are trained in English. In this talk, we’ll explore how RAG could work in languages other than English. We’ll explore French, Chinese, and Polish.

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