50,000 home cameras reportedly hacked, footage posted online

Some footage has already appeared on adult sites, with cybercriminals offering lifetime access to the entire loot for US$150

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50,000 home cameras reportedly hacked, footage posted online

Some footage has already appeared on adult sites, with cybercriminals offering lifetime access to the entire loot for US$150

The post 50,000 home cameras reportedly hacked, footage posted online appeared first on WeLiveSecurity

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Attackers chain Windows, VPN flaws to target US government agencies

Bad actors have accessed US elections support systems, although there’s no evidence to suggest that election data has been compromised, say FBI and CISA

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ESET takes part in global operation to disrupt Trickbot

Throughout its monitoring, ESET analyzed thousands of malicious samples every month to help this effort

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Fuzzing internships for Open Source Software

Posted by Abhishek Arya, Chrome Security team

Open source software is the foundation of many modern software products. Over the years, developers increasingly have relied on reusable open source components for their applications. It is paramount that these open source components are secure and reliable, as weaknesses impact those that build upon it.

Google cares deeply about the security of the open source ecosystem and recently launched the Open Source Security Foundation with other industry partners. Fuzzing is an automated testing technique to find bugs by feeding unexpected inputs to a target program. At Google, we leverage fuzzing at scale to find tens of thousands of security vulnerabilities and stability bugs. This summer, as part of Google’s OSS internship initiative, we hosted 50 interns to improve the state of fuzz testing in the open source ecosystem.

The fuzzing interns worked towards integrating new projects and improving existing ones in OSS-Fuzz, our continuous fuzzing service for the open source community (which has 350+ projects, 22,700 bugs, 89% fixed). Several widely used open source libraries including but not limited to nginx, postgresql, usrsctp, and openexr, now have continuous fuzzing coverage as a result of these efforts.

Another group of interns focused on improving the security of the Linux kernel. syzkaller, a kernel fuzzing tool from Google, has been instrumental in finding kernel vulnerabilities in various operating systems. The interns were tasked with improving the fuzzing coverage by adding new descriptions to syzkaller like ip tunnels, io_uring, and bpf_lsm for example, refining the interface description language, and advancing kernel fault injection capabilities.

Some interns chose to write fuzzers for Android and Chrome, which are open source projects that billions of internet users rely on. For Android, the interns contributed several new fuzzers for uncovered areas – network protocols such as pppd and dns, audio codecs like monoblend, g722, and android framework. On the Chrome side, interns improved existing blackbox fuzzers, particularly in the areas: DOM, IPC, media, extensions, and added new libprotobuf-based fuzzers for Mojo.

Our last set of interns researched quite a few under-explored areas of fuzzing, some of which were fuzzer benchmarking, ML based fuzzing, differential fuzzing, bazel rules for build simplification and made useful contributions.

Over the course of the internship, our interns have reported over 150 security vulnerabilities and 750 functional bugs. Given the overall success of these efforts, we plan to continue hosting fuzzing internships every year to help secure the open source ecosystem and teach incoming open source contributors about the importance of fuzzing. For more information on the Google internship program and other student opportunities, check out careers.google.com/students. We encourage you to apply.

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Week in security with Tony Anscombe

Why deleting your personal data from social media may be impossible – How do you reset your face after a data breach? – The perils of working from a hotel

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55 security flaws found in various Apple services

Five ethical hackers have earned almost US$300,000 in bug bounty rewards – so far

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So you thought your personal data was deleted? Not so fast

It may be impossible to delete your personal information from Houseparty and other social media services – despite privacy legislation!

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Google adds password breach alerts to Chrome for Android, iOS

The feature is part of the browser’s security improvements that were first built into its desktop version

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Privacy-Preserving Smart Input with Gboard

Posted by Yang Lu, Software Engineer, Angana Ghosh, Group Product Manager, and Xu Liu, Director of Engineering, Gboard team

Google Keyboard (a.k.a Gboard) has a critical mission to provide frictionless input on Android to empower users to communicate accurately and express themselves effortlessly. In order to accomplish this mission, Gboard must also protect users’ private and sensitive data. Nothing users type is sent to Google servers. We recently launched privacy-preserving input by further advancing the latest federated technologies. In Android 11, Gboard also launched the contextual input suggestion experience by integrating on-device smarts into the user’s daily communication in a privacy-preserving way.

Before Android 11, input suggestions were surfaced to users in several different places. In Android 11, Gboard launched a consistent and coordinated approach to access contextual input suggestions. For the first time, we’ve brought Smart Replies to the keyboard suggestions – powered by system intelligence running entirely on device. The smart input suggestions are rendered with a transparent layer on top of Gboard’s suggestion strip. This structure maintains the trust boundaries between the Android platform and Gboard, meaning sensitive personal content cannot be not accessed by Gboard. The suggestions are only sent to the app after the user taps to accept them.

For instance, when a user receives the message “Have a virtual coffee at 5pm?” in Whatsapp, on-device system intelligence predicts smart text and emoji replies “Sounds great!” and “👍”. Android system intelligence can see the incoming message but Gboard cannot. In Android 11, these Smart Replies are rendered by the Android platform on Gboard’s suggestion strip as a transparent layer. The suggested reply is generated by the system intelligence. When the user taps the suggestion, Android platform sends it to the input field directly. If the user doesn’t tap the suggestion, gBoard and the app cannot see it. In this way, Android and Gboard surface the best of Google smarts whilst keeping users’ data private: none of their data goes to any app, including the keyboard, unless they’ve tapped a suggestion.

Additionally, federated learning has enabled Gboard to train intelligent input models across many devices while keeping everything individual users type on their device. Today, the emoji is as common as punctuation – and have become the way for our users to express themselves in messaging. Our users want a way to have fresh and diversified emojis to better express their thoughts in messaging apps. Recently, we launched new on-device transformer models that are fine-tuned with federated learning in Gboard, to produce more contextual emoji predictions for English, Spanish and Portuguese.

Furthermore, following the success of privacy-preserving machine learning techniques, Gboard continues to leverage federated analytics to understand how Gboard is used from decentralized data. What we’ve learned from privacy-preserving analysis has let us make better decisions in our product.

When a user shares an emoji in a conversation, their phone keeps an ongoing count of which emojis are used. Later, when the phone is idle, plugged in, and connected to WiFi, Google’s federated analytics server invites the device to join a “round” of federated analytics data computation with hundreds of other participating phones. Every device involved in one round will compute the emoji share frequency, encrypt the result and send it a federated analytics server. Although the server can’t decrypt the data individually, the final tally of total emoji counts can be decrypted when combining encrypted data across devices. The aggregated data shows that the most popular emoji is 😂 in Whatsapp, 😭 in Roblox(gaming), and ✔ in Google Docs. Emoji 😷 moved up from 119th to 42nd in terms of frequency during COVID-19.

Gboard always has a strong commitment to Google’s Privacy Principles. Gboard strives to build privacy-preserving effortless input products for users to freely express their thoughts in 900+ languages while safeguarding user data. We will keep pushing the state of the art in smart input technologies on Android while safeguarding user data. Stay tuned!

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