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A new strain of the notorious Dridex malware has been spotted using polymorphism antivirus evasion techniques in phishing emails. The Dridex credential-stealer that almost exclusively targets financial institutions continues to evolve and now uses application whitelisting techniques to infect systems and evade most antivirus products.

To better understand this trend of banking trojans being distributed via malicious droppers, we must look back at how droppers have been popping up on Google Play Store since the beginning of 2022, analyze how each of these droppers vary from one another and evolve, and learn how cybercriminals are disseminating them.

Cybercriminals are constantly finding ways to evade detection and infect as many devices as possible. In a half-year span, we have seen how banking trojans have evolved their technical routines to avoid being detected, such as hiding malicious payloads in droppers. As more banking trojans are made available via DaaS, malicious actors will have an easier and more cost-effective way of distributing malware disguised as legitimate apps. We foresee that this trend will continue and more banking trojans will be distributed on digital distribution services in the future.

Yes, the GrapheneOS code is reviewed by external security researchers, companies and organizations on a continuous basis. This is the main benefit of GrapheneOS being an open source project actively used by other organizations, but it is certainly not something to take for granted based on a project being open source. We put a lot of work into making our code well documented and easy to review. Auditing and code review cannot be done properly as a one time thing but rather need to be done continuously as the code changes. Most of the code review and auditing results for GrapheneOS can be seen from the public pull requests and issue trackers. For example, the French ANSSI organization uses a bunch of our work and has given us suggestions along with reporting issues including a couple issues in hardened_malloc where it could have a false positive detection of memory corruption and wrongly abort the process.[1][2] We've built relationships with security researchers and organizations interested in GrapheneOS or using it which results in a lot of this kind of collaboration. This is not a one-time event but rather something that happens regularly as the code evolves, features are added and we ported to new release. The benefits of a group unfamiliar with the code spending a short time doing a shallow review are greatly overstated in marketing. We instead focus on having people very familiar with areas of the code regularly auditing all our changes. The large number of upstream Android security vulnerabilities discovered by GrapheneOS despite us not actively seeking them out speaks to the results of our review and testing.

Text messages as a form of communication can often contain sensitive content and information. As Android phones have evolved over time, text messages have become even more susceptible to prying eyes. Your texts may appear on your lock screen or in a dropdown menu.

The next time your noggin needs a memory aide, just open up Maps and tap the blue dot that represents your current location. That'll pull up a big ol' honkin menu, within which you'll see the incredibly useful "Save parking" option. (And don't let yourself be fooled: While its official purpose may revolve around parking, you can just as easily use the feature to save any kind of location for any reason you want.)

Establishing secret command and control (C&C) channels from attackers is important in malware design. This paper presents design and analysis of malware architecture exploiting push notification services as C&C channels. The key feature of the push notification-based malware design is remote triggering, which allows attackers to trigger and execute their malware by push notifications. The use of push notification services as covert channels makes it difficult to distinguish this type of malware from other normal applications also using the same services. We implemented a backdoor prototype on Android devices as a proof-of-concept of the push notification-based malware and evaluated its stealthiness and feasibility. Our malware implementation effectively evaded the existing malware analysis tools such as 55 antimalware scanners from VirusTotal and SandDroid. In addition, our backdoor implementation successfully cracked about 98% of all the tested unlock secrets (either PINs or unlock patterns) in 5 seconds with only a fraction (less than 0.01%) of the total power consumption of the device. Finally, we proposed several defense strategies to mitigate push notification-based malware by carefully analyzing its attack process. Our defense strategies include filtering subscription requests for push notifications from suspicious applications, providing centralized management and access control of registration tokens of applications, detecting malicious push messages by analyzing message contents and characteristic patterns demonstrated by malicious push messages, and detecting malware by analyzing the behaviors of applications after receiving push messages.

To show the feasibility of our malware design, we implemented backdoor (as a representative type of push notification-based malware) on Android 5.1 and evaluated its stealthiness and feasibility. The evaluation results showed that existing malware detection tools based on static and/or dynamic analysis were ineffective to detect our backdoor implementation. Specifically, all the tested antimalware scanners (VirusTotal [5] and SandDroid [6]) failed to identify our backdoor as malware. In addition, we observed that our backdoor successfully cracked about 98% of all the tested unlock secrets (either 4-digit PINs or patterns) in 5 seconds with only a fraction (less than 0.01%) of the total power consumption of the device.

Table 1 summarizes the experiment results. For 4-digit PINs, the dictionary attacks cracked 52.68% of all the tested PINs within 1 second, whereas the brute-force attacks cracked only 10.60%. The dictionary attacks show on average 1.5 times faster execution time than the brute-force attacks. For screen lock patterns, the dictionary attacks cracked 85.34% of all the tested unlock patterns within 1 second, whereas the brute-force attacks cracked only 44.10%. The brute-force attacks show on average 4.93 times slower execution time than the dictionary attacks.

Since 1994, much has evolved. AI programs have been incorporated with the goal of passing the Turing Test, and this, along with other applications of the tech, has led to an entire industry of solutions tailored to help organizations optimize sales and marketing activities for bottom-line profits. 2b1af7f3a8