Meta has introduced a new feature called Scam Alert for WhatsApp. This system uses artificial intelligence directly on the user's device to detect suspicious messages and issue warnings about potential scam attempts.
Meta has introduced a new feature called Scam Alert for WhatsApp. This system uses artificial intelligence directly on the user's device to detect suspicious messages and issue warnings about potential scam attempts.
Currently, Scam Alert is in a testing phase and will initially be made available to a limited group of beta program participants. As reported to UOL, this feature is not yet accessible in Brazil.
WhatsApp is recognized as a primary channel for scams both in Brazil and globally. In Brazil, 2025 Febraban data indicates that 34% of Brazilians have received contacts from individuals posing as acquaintances. In the United States, the Federal Trade Commission (FTC) points out that in 2025, estimated losses of US$ 725 million occurred due to actions carried out through the Meta application.
The operation of Scam Alert is based on a machine learning model that operates locally on the device, eliminating the need for cloud connection. This model is specifically trained to recognize message patterns frequently used in fraud schemes.
When something is deemed suspicious, the tool notifies the message recipient, ensuring that the potential scammer does not become aware of the alert. The user has the option to block or report the contact, or choose to proceed with the conversation normally.
If the user indicates that the alert was triggered erroneously, the model registers that interaction as trustworthy and stops issuing future security warnings. Additionally, there is a functionality that allows the user to share the last five received messages with Meta, assisting the company in improving its protection mechanism.
According to Meta, the technology performs a probabilistic classification that considers the structure of the conversation and linguistic indicators, focusing more on how the dialogue develops than on isolated keywords. However, the system only analyzes messages from numbers not listed in the user's contacts, which, although logical, creates a vulnerability for scams executed with cloned numbers.
Meta has repeatedly emphasized that the AI model is processed on the device itself. This implies that messages do not need to be sent to a server to be examined. The company clarifies that only the alert counts and the actions taken by the user are transmitted, and this data is always aggregated and anonymized.
On another security front, the company announced that it will make the weights of the machine learning model used in message analysis available to researchers, allowing them to test the AI with various scenarios and inputs. Finally, Meta assured that it does not have means to direct specific models to individual users, besides providing verification tools, aiming to prevent any use of the feature for surveillance purposes.
Meta removed over 756,000 profiles suspected of belonging to Australian citizens under the age of 16 between December and June. This blocking was implemented following the enactment of legislation that imposes restrictions on adolescent accounts on social media platforms.
According to Reuters, the total number of deactivated accounts amounted to 462,000 on Instagram and 294,000 on Facebook. Meta itself stated that this work will continue as pressure from Australian authorities on technology companies increases.
This new volume of removals exceeds the data Meta disclosed in January, when the company reported the deletion of 331,000 Instagram accounts and 173,000 Facebook accounts.
The company assures its commitment to complying with Australian law, despite having expressed opposition to the measure along with other platforms. The government, in turn, accuses social networks of being insufficient in combating the continuous use of services by minors.
The legislation came into effect on December 10, created in response to authorities' concerns about the impact of social media on the physical and mental health of children and adolescents.
Data provided by Meta indicates that the legal dispute may intensify in the coming months. The Australian government suggested increasing the maximum fine for non-compliance with the law to A$99 million (approximately R$362.7 million) and granting greater power to the regulatory body to request documents from companies.
Furthermore, Australia alleges that the platforms have created circumstances that could hinder the enforcement of the ban. This issue also draws the attention of other governments that are evaluating the adoption of similar restrictions for younger users.
A parliamentary hearing has been scheduled for Friday, where representatives from Meta, TikTok, Google (owner of YouTube), and Snapchat will testify before regulatory and governmental authorities.
Meta employs artificial intelligence to identify profiles that may belong to individuals under the age of 16. Instead of relying solely on the age information provided by the user, the system analyzes various indicators present in the profile.
Among the analyzed elements are references to birthdays and mentions of school series, as well as reports regarding potential minor accounts. The company also uses age estimation tools based on photographs. Previously, the method consisted of trying to deduce the age based on the user's behavior and activity on the platform.
Another change implemented prevents anyone from attempting to create a new account if a previous profile was removed on suspicion of belonging to a minor.
Meta communicated that monitoring will continue, implying that the number of accounts removed in Australia is expected to keep growing.
Meta has introduced Muse Code, a new artificial intelligence agent designed for programming and game creation. This model is based on the recently launched Muse Spark 1.2 and has the capability to execute large-scale coding tasks.
With this launch, the company aims to compete with giants in the generative AI sector, such as Anthropic and OpenAI. On X, Mark Zuckerberg shared results from internal tests, comparing the new agent with various Large Language Models (LLMs) on the market, including Claude Opus 5 and GPT-5.6 (in Sol and Terra versions).
Preliminary results indicate that Muse Code's performance is still at an intermediate level when compared to its direct competitors. A notable feature of Muse Code is its ability to handle extensive projects, which it achieves by dividing tasks among multiple agents.
Two games developed with this new AI were presented on Meta's blog, displaying simplified mechanics and graphics. Muse Code is already available in beta version. Muse Spark 1.2 is specifically aimed at coding activities, ranging from bug fixing to data interpretation, while maintaining the qualities of Muse Spark 1.1 for general AI agents.
Although related, no information was provided regarding the model's improvements in cybersecurity or scientific research. The figures presented by Meta suggest that the AI falls slightly short of current Anthropic models, although it manages to surpass GPT-5.6 in certain situations. In analyses conducted by Terminal Bench 2.1, Muse Spark 1.2 ranks second, with performance close to Claude Opus 5, contrary to what Meta's internal benchmark shows.
Among the points highlighted by Zuckerberg's company are the ability to manage prolonged tasks and maintain context understanding throughout the entire process. Additionally, the AI can map and improve its own training, surpassing Muse Spark 1.1 in this aspect. Focused on programming, the new agent promises good performance in game development, which often requires multiple layers of code and various logical conditions.
In the release of Muse Spark 1.2 on its blog, Meta also demonstrated the AI's potential with three examples, two of which were simple games. The first, named Embervault, features an isometric perspective and a board format where the player controls a robot that must eliminate constantly pursuing enemies; the gameplay demands fluidity, but the website showed freezes during the test. The second game, Avo Lawn, is a 2D tower defense type, where zombies advance slowly towards the player. In this game, the user collects seeds and acquires fruits to strengthen their defenses, lasting five attack waves, presenting a slow pace but with potential to be a good mobile game.
