O Google apresentou hoje novas variantes para a geração 3.0 dos modelos de inteligência artificial generativa do Gemini. Mais especificamente, foram lançados o Gemini 3.6 Flash, o 3.5 Flash-Lite e o 3.5 Flash Cyber.

A maior novidade é justamente o 3.6 Flash, que agora é o principal modelo “rápido” da empresa. Ele foi projetado com base em feedbacks dos usuários do 3.5 Flash e visa oferecer um equilíbrio ideal entre eficiência e qualidade.

Today we’re expanding the Gemini family with three new models built to be faster, more token efficient, and reliable at scale.

Meet the new Gemini models ↓ pic.twitter.com/hVYNQqxgjd

— Google (@Google) July 21, 2026

Na prática, o 3.6 Flash gasta 17% menos tokens de saída para tarefas semelhantes, bem como precisa de menos etapas de raciocínio e menos chamadas de ferramentas para concluir fluxos de trabalho compostos por várias etapas.

Essa eficiência também vem acompanhada de um preço menor. Custando US$1,50 por milhão de tokens de entrada e US$7,50 por milhão de tokens de saída, o 3.6 Flash reduz o custo total por tarefa executada por agentes, tornando-os mais baratos para desenvolver e operar.

Ele também é mais preciso em programação, avança significativamente em ML ¹Machine learning, ou aprendizado de máquina., apresenta maior capacidade de interação com computadores e um melhor desempenho em tarefas envolvendo conhecimento.

Além disso, o novo modelo tem proteções reforçadas contra usos indevidos relacionados a materiais químicos, biológicos, radiológicos e nucleares (CBRN) e ataques cibernéticos.

3.5 Flash-Lite e 3.5 Flash Cyber

Continuando os investimentos na família de modelos 3.5, o Google apresentou hoje o 3.5 Flash-Lite como o “modelo mais rápido da família”, desenvolvido para tarefas de baixa latência e alto volume de processamento.

Altamente superior ao Gemini 3.1 Flash-Lite, seu antecessor direto, ele gera 350 tokens de saída por segundo e custa apenas US$0,30 por milhão de tokens de entrada e US$2,50 por milhão de tokens de saída.

O 3.5 Flash Cyber, por sua vez, é o 3.5 Flash rodando no sistema de IA do Google voltado à segurança (o CodeMender) e ajustado para encontrar e corrigir falhas de cibersegurança com um baixo custo por token.

Ele será disponibilizado inicialmente apenas para governos e parceiros confiáveis por meio de um programa piloto de acesso limitado para que eles encontrem e corrijam possíveis vulnerabilidades críticas.

Além disso, o Google também afirmou que sua equipe de desenvolvedores já está focada na construção do Gemini 4 — o pré-treinamento mais ambicioso para essa nova geração, inclusive, já começou.

Notas de rodapé

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  Machine learning, ou aprendizado de máquina.

Prompt injections, the malicious commands attackers embed into content to entice large language models to follow them, have been attackers’ go-to tool for turning AI platforms against their users. A well-phrased command sneaked into an email or calendar invitation is often all it takes to cause the LLM to exfiltrate sensitive data or follow other harmful actions.

Now, defenders are embracing the prompt injection, too.

A strong, sharp effect

Researchers from Tracebit on Monday said they found that placing prompt injections alongside passwords, cryptographic keys, and other secrets stored on Amazon Web Services was often all that was needed to shut down attacks from AI hacking agents. The prompts direct the attacking LLM to perform an action forbidden by its guardrails, the safety barriers AI developers erect to prevent it from taking harmful actions. The LLM responds by shutting down.

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In the brief history of AI security, the prompt injection has quickly become the top threat. Large language models are inherently unable to distinguish between legitimate instructions provided by users and malicious ones sneaked into emails, source code, and other third-party content the models are processing. This makes it trivial to surreptitiously inject malicious commands that the LLM readily follows.

With no way to enforce this crucial boundary between trusted and untrusted sources, AI engine developers are left to erect elaborate guardrails designed to mitigate the damage rather than solve the root cause.

To date, most prompt injections have fallen into a class known as push, in which each potential victim is targeted. For example, the adversary injects malicious instructions into an individual email or calendar invitation. Because the injection must then be sent (or pushed) to each specific target, the scale of the attack is limited, hampering mass exploits that hit the Internet at large.

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Makers of AI browsers make lofty promises. With a single prompt, users can ask one to find a restaurant in a particular part of town, reserve a table, invite a colleague to lunch, and email a confirmation. These makers are much more reticent about the risks of blurring the once fine line between browsing sites and asking a large language model a question or instructing it to take potentially sensitive actions.

LLM developers’ answer so far has been to build guardrails that make some requests off-limits. Developing software exploits, stealing credentials, or teaching how to build a pipe bomb are examples. The problem with this approach is that the guardrails are reactive and treat the symptoms rather than solve the root cause. It’s tantamount to the manufacturer of an unsafe vehicle advocating for new road designs rather than fixing the flaws that make it prone to accidents.

Lulling LLMs into an alternate reality

New research puts this predicament on sharp display. It demonstrates how a website can lull AI browsers into a false reality where the rules governing its behavior no longer apply. After that, an attacker has free rein to invoke all kinds of destructive actions, such as extracting code from a private repository or extracting credentials from the built-in password manager.

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Last year, we featured a lengthy interview with tech journalist/science fiction author Cory Doctorow about his book, Enshittification: Why Everything Suddenly Got Worse and What To Do About It. The prolific Doctorow is back with a provocative new book that serves as a follow-up of sorts, focusing on AI and related issues: The Reverse Centaur's Guide to Life After AI.

Doctorow doesn't actually enjoy talking about AI, but he's constantly being asked to comment on it. "I made the tactical error of being sick of talking about AI," Doctorow told Ars. "So I wrote a book about why I think it's a dumb thing to keep asking people to talk about, and now I have to talk about it." Reverse Centaur is Doctorow's attempt to "sort out the bullshit from the material reality."

In automation theory, per Doctorow, a "centaur" describes a human augmented with a technology, like machine learning, or even just driving a car or using autocomplete. A reverse centaur "is a machine head on a human body, a person who is serving as a squishy meat appendage for an uncaring machine," Doctorow said in a speech last December. He gave the example of an Amazon delivery driver, surrounded by AI cameras monitoring their driving, who essentially serves as a peripheral to the delivery van.

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© Copyright Julia Galdo and Cody Cloud (JUCO)/CC-BY 3.0

Last year, we featured a lengthy interview with tech journalist/science fiction author Cory Doctorow about his book, Enshittification: Why Everything Suddenly Got Worse and What To Do About It. The prolific Doctorow is back with a provocative new book that serves as a follow-up of sorts, focusing on AI and related issues: The Reverse Centaur's Guide to Life After AI.

Doctorow doesn't actually enjoy talking about AI, but he's constantly being asked to comment on it. "I made the tactical error of being sick of talking about AI," Doctorow told Ars. "So I wrote a book about why I think it's a dumb thing to keep asking people to talk about, and now I have to talk about it." Reverse Centaur is Doctorow's attempt to "sort out the bullshit from the material reality."

In automation theory, per Doctorow, a "centaur" describes a human augmented with a technology, like machine learning, or even just driving a car or using autocomplete. A reverse centaur "is a machine head on a human body, a person who is serving as a squishy meat appendage for an uncaring machine," Doctorow said in a speech last December. He gave the example of an Amazon delivery driver, surrounded by AI cameras monitoring their driving, who essentially serves as a peripheral to the delivery van.

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© Copyright Julia Galdo and Cody Cloud (JUCO)/CC-BY 3.0

Last Tuesday, Microsoft patched a vulnerability it rated as max critical in its M365 Copilot AI platform. On Monday, the researchers who discovered the vulnerability and reported it to Microsoft revealed how their proof-of-concept exploit could retrieve 2FA codes and other sensitive data from emails accessible to Copilot.

Microsoft and other LLM providers have been unable to prevent their products from complying with malicious requests to reveal data. The root cause: AI bots are unable to distinguish between instructions provided by users and those snuck into third-party content the models are summarizing, drafting responses to, or using to perform other actions on behalf of the user. With no way to secure this crucial boundary, Microsoft and its peers are left to erect complicated and ad hoc guardrails designed to rein in the consequences of this incurable gullibility.

Jumping over guardrails

One guardrail built into Copilot and most other LLMs prevents them from submitting web forms, sending emails, and taking similar actions that can be used to exfiltrate data from the user. To work around this, LLM hackers turned to markup language, which, among other things, allows users to add formatting elements such as headings, lists, and links to text without the need for HTML tags. Another workaround is to wrap sensitive data inside HTML tags such as <img> and <form>. In either case, a web request showing the data hits the attacker’s web server, where the secret information is captured in logs.

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