Google’s AI Video Tool “Flow” Powers 100 Million Creations A New Era in Storytelling

Ineur Tech — Thu, 21 Aug 2025 08:55:07 +0000

Quick Summary

Google’s AI-driven video maker, Flow, has just crossed a staggering milestone: creators globally have produced 100 million videos using the platform since its debut at Google I/O 2025

What Sets Flow Apart ?

Flow isn't just another video editor. It blends cutting-edge AI models Veo, Imagen, and Gemini to transform simple text or image prompts into cinematic sequences with controllable camera angles, motion, and seamless transitions . This smart tool adapts to each creator’s unique style, enabling personalized, high-impact storytelling without needing traditional production resources .

Rapid Growth and Engagement

Remarkably, Flow achieved its 100 million video milestone in just three months translating to roughly 1.1 million creations per day Even more striking: videos made with Flow are seeing about 40% higher audience engagement, highlighting how compelling, authentic AI-augmented storytelling can be .

Helping Creators Create More

To meet soaring demand and encourage further innovation, Google has doubled the monthly AI credit allowance for AI Ultra subscribers from 12,500 to 25,000 . Existing users will see the new limit at their next billing renewal, while Workspace users can expect the increase in the coming days

These credits also extend to Whisk, a companion tool for AI-powered image generation (via both text and image prompts), which is being launched in 77 additional countries starting August 20, 2025 .

What’s Next ?

With Flow’s momentum, Google looks set to embed the tool deeper into its creative ecosystem potentially integrating directly with YouTube, so creators can ideate, edit, and publish within a unified platform . For Google, it's not just about the tool it’s about redefining how stories are brought to life, at scale, across the globe

Source

A new research paper reveals self-adaptive AI hacking loops as a next-generation cyber threat

Ineur Tech — Wed, 13 Aug 2025 00:23:01 +0000

In "Adaptive Composition Attacks in AI-Integrated Systems CEO Setaleur Momen Ghazouani presents a conceptual framework for understanding new cybersecurity threats that arise from the interaction of secure components in AI-integrated systems. The paper, completed in July 2025, argues that these "adaptive composition attacks" are not the result of individual system weaknesses but emerge from unintended interactions between large language models (LLMs) and other systems with execution permissions

The central idea of the paper is the "self-adaptive hacking loop". This is a two-phase process where an LLM iteratively refines a malicious input based on feedback from the target system. In the generation phase, the LLM creates an initial exploit, such as a phishing email or API call. In the evaluation phase, it receives feedback on the attempt's outcome, which it then uses to improve the next attempt. This process transforms the LLM from a passive content generator into an active, problem-solving agent capable of learning to bypass security constraints. The paper distinguishes this from traditional fuzzing by highlighting the LLM's use of semantic awareness and goal-directed refinement rather than brute-force randomness

A key vulnerability discussed in the paper is "permissions and trust composition". The author notes that while components like LLMs, email clients, and shell environments are designed with individual security constraints, their integration can create new vulnerabilities. This happens when trust granted to one module is implicitly extended to another without explicit authorization, a phenomenon termed "false trust propagation". The paper provides a scenario where an LLM with access to both a corporate inbox and shell commands could be manipulated by a phishing email to execute a malicious script, even though each component individually functioned as designed

The threat is systemic, not merely technical and necessitates a reevaluation of how permissions are defined and validated in multi-agent AI systems The research also addresses the evolution of LLMs from passive generators to "coordinated attackers". When given a feedback-rich environment, an LLM can become a strategic orchestrator that plans, adapts, and executes multi-step attack strategies, engaging in reconnaissance, deception, and privilege escalation. The paper argues that current defense mechanisms, which are often based on static, signature-based detection, are insufficient against these dynamically planned, adaptive attacks

To counter these threats, the author proposes a new defensive framework centered on a "Compositional Security Mediator" (CSM). The CSM would function as a proxy between the LLM and any execution interfaces, analyzing the intent behind a sequence of actions rather than just individual commands. It would also maintain a temporary record of interactions to identify and break self-adaptive hacking loops by providing generic error messages instead of detailed feedback. Additionally, the CSM would enforce "dynamic trust scoping," limiting permissions to a specific task and revoking them upon completion to combat false trust propagation

The paper concludes by advocating for a shift in cybersecurity thinking. Instead of evaluating systems as discrete entities, the author recommends treating the entire AI-integrated ecosystem—including the dynamic interconnections between components—as the true attack surface. The paper calls for the development of standardized testing environments, like the conceptual "planner/victim" model, to simulate and study these complex attack compositions. These simulations would help reveal the "combinatorial threat space" that is invisible in isolated testing. The ultimate goal is to build defenses that are resilient to intelligent, adaptive, and collaborative machine-based threats.

DEV Community: Ineur Tech

Google’s AI Video Tool “Flow” Powers 100 Million Creations A New Era in Storytelling

A new research paper reveals self-adaptive AI hacking loops as a next-generation cyber threat