Attacks on the education sector are surging: How can cyber-defenders respond?

Academic institutions have a unique set of characteristics that makes them attractive to bad actors. What’s the right antidote to cyber-risk?

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Watch out for these traps lurking in search results

Here’s how to avoid being hit by fraudulent websites that scammers can catapult directly to the top of your search results

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So your friend has been hacked: Could you be next?

When a ruse puts on a familiar face, your guard might drop, making you an easy mark. Learn how to tell a friend apart from a foe.

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Google announces Sec-Gemini v1, a new experimental cybersecurity model

Posted by Elie Burzstein and Marianna Tishchenko, Sec-Gemini team

Today, we’re announcing Sec-Gemini v1, a new experimental AI model focused on advancing cybersecurity AI frontiers. 


As outlined a year ago, defenders face the daunting task of securing against all cyber threats, while attackers need to successfully find and exploit only a single vulnerability. This fundamental asymmetry has made securing systems extremely difficult, time consuming and error prone. AI-powered cybersecurity workflows have the potential to help shift the balance back to the defenders by force multiplying cybersecurity professionals like never before.


 

Effectively powering SecOps workflows requires state-of-the-art reasoning capabilities and extensive current cybersecurity knowledge. Sec-Gemini v1 achieves this by combining Gemini’s advanced capabilities with near real-time cybersecurity knowledge and tooling. This combination allows it to achieve superior performance on key cybersecurity workflows, including incident root cause analysis, threat analysis, and vulnerability impact understanding.


We firmly believe that successfully pushing AI cybersecurity frontiers to decisively tilt the balance in favor of the defenders requires a strong collaboration across the cybersecurity community. This is why we are making Sec-Gemini v1 freely available to select organizations, institutions, professionals, and NGOs for research purposes.


Sec-Gemini v1 outperforms other models on key cybersecurity benchmarks as a result of its advanced integration of Google Threat Intelligence (GTI), OSV, and other key data sources. Sec-Gemini v1 outperforms other models on CTI-MCQ, a leading threat intelligence benchmark, by at least 11% (See Figure 1). It also outperforms other models by at least 10.5% on the CTI-Root Cause Mapping benchmark (See Figure 2):

Figure 1: Sec-Gemini v1 outperforms other models on the CTI-MCQ Cybersecurity Threat Intelligence benchmark.

Figure 2: Sec-Gemini v1 has outperformed other models in a Cybersecurity Threat Intelligence-Root Cause Mapping (CTI-RCM) benchmark that evaluates an LLM’s ability to understand the nuances of vulnerability descriptions, identify vulnerabilities underlying root causes, and accurately classify them according to the CWE taxonomy.



Below is an example of the comprehensiveness of Sec-Gemini v1’s answers in response to key cybersecurity questions. First, Sec-Gemini v1 is able to determine that Salt Typhoon is a threat actor (not all models do) and provides a comprehensive description of that threat actor, thanks to its deep integration with Mandiant Threat intelligence data.

Next, in response to a question about the vulnerabilities in the Salt Typhoon description, Sec-Gemini v1 outputs not only vulnerability details (thanks to its integration with OSV data, the open-source vulnerabilities database operated by Google), but also contextualizes the vulnerabilities with respect to threat actors (using Mandiant data). With Sec-Gemini v1, analysts can understand the risk and threat profile associated with specific vulnerabilities faster.

If you are interested in collaborating with us on advancing the AI cybersecurity frontier, please request early access to Sec-Gemini v1 via this form.

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Taming the Wild West of ML: Practical Model Signing with Sigstore

Posted by Mihai Maruseac, Google Open Source Security Team (GOSST)

In partnership with NVIDIA and HiddenLayer, as part of the Open Source Security Foundation, we are now launching the first stable version of our model signing library. Using digital signatures like those from Sigstore, we allow users to verify that the model used by the application is exactly the model that was created by the developers. In this blog post we will illustrate why this release is important from Google’s point of view.


With the advent of LLMs, the ML field has entered an era of rapid evolution. We have seen remarkable progress leading to weekly launches of various applications which incorporate ML models to perform tasks ranging from customer support, software development, and even performing security critical tasks.


However, this has also opened the door to a new wave of security threats. Model and data poisoning, prompt injection, prompt leaking and prompt evasion are just a few of the risks that have recently been in the news. Garnering less attention are the risks around the ML supply chain process: since models are an uninspectable collection of weights (sometimes also with arbitrary code), an attacker can tamper with them and achieve significant impact to those using the models. Users, developers, and practitioners need to examine an important question during their risk assessment process: “can I trust this model?”


Since its launch, Google’s Secure AI Framework (SAIF) has created guidance and technical solutions for creating AI applications that users can trust. A first step in achieving trust in the model is to permit users to verify its integrity and provenance, to prevent tampering across all processes from training to usage, via cryptographic signing. 



The ML supply chain

To understand the need for the model signing project, let’s look at the way ML powered applications are developed, with an eye to where malicious tampering can occur.


Applications that use advanced AI models are typically developed in at least three different stages. First, a large foundation model is trained on large datasets. Next, a separate ML team finetunes the model to make it achieve good performance on application specific tasks. Finally,  this fine-tuned model is embedded into an application.


The three steps involved in building an application that uses large language models.


These three stages are usually handled by different teams, and potentially even different companies, since each stage requires specialized expertise. To make models available from one stage to the next, practitioners leverage model hubs, which are repositories for storing models. Kaggle and HuggingFace are popular open source options, although internal model hubs could also be used.


This separation into stages creates multiple opportunities where a malicious user (or external threat actor who has compromised the internal infrastructure) could tamper with the model. This could range from just a slight alteration of the model weights that control model behavior, to injecting architectural backdoors — completely new model behaviors and capabilities that could be triggered only on specific inputs. It is also possible to exploit the serialization format and inject arbitrary code execution in the model as saved on disk — our whitepaper on AI supply chain integrity goes into more details on how popular model serialization libraries could be exploited. The following diagram summarizes the risks across the ML supply chain for developing a single model, as discussed in the whitepaper.


The supply chain diagram for building a single model, illustrating some supply chain risks (oval labels) and where model signing can defend against them (check marks)


The diagram shows several places where the model could be compromised. Most of these could be prevented by signing the model during training and verifying integrity before any usage, in every step: the signature would have to be verified when the model gets uploaded to a model hub, when the model gets selected to be deployed into an application (embedded or via remote APIs) and when the model is used as an intermediary during another training run. Assuming the training infrastructure is trustworthy and not compromised, this approach guarantees that each model user can trust the model.



Sigstore for ML models

Signing models is inspired by code signing, a critical step in traditional software development. A signed binary artifact helps users identify its producer and prevents tampering after publication. The average developer, however, would not want to manage keys and rotate them on compromise.


These challenges are addressed by using Sigstore, a collection of tools and services that make code signing secure and easy. By binding an OpenID Connect token to a workload or developer identity, Sigstore alleviates the need to manage or rotate long-lived secrets. Furthermore, signing is made transparent so signatures over malicious artifacts could be audited in a public transparency log, by anyone. This ensures that split-view attacks are not possible, so any user would get the exact same model. These features are why we recommend Sigstore’s signing mechanism as the default approach for signing ML models.


Today the OSS community is releasing the v1.0 stable version of our model signing library as a Python package supporting Sigstore and traditional signing methods. This model signing library is specialized to handle the sheer scale of ML models (which are usually much larger than traditional software components), and handles signing models represented as a directory tree. The package provides CLI utilities so that users can sign and verify model signatures for individual models. The package can also be used as a library which we plan to incorporate directly into model hub upload flows as well as into ML frameworks.



Future goals

We can view model signing as establishing the foundation of trust in the ML ecosystem. We envision extending this approach to also include datasets and other ML-related artifacts. Then, we plan to build on top of signatures, towards fully tamper-proof metadata records, that can be read by both humans and machines. This has the potential to automate a significant fraction of the work needed to perform incident response in case of a compromise in the ML world. In an ideal world, an ML developer would not need to perform any code changes to the training code, while the framework itself would handle model signing and verification in a transparent manner.


If you are interested in the future of this project, join the OpenSSF meetings attached to the project. To shape the future of building tamper-proof ML, join the Coalition for Secure AI, where we are planning to work on building the entire trust ecosystem together with the open source community. In collaboration with multiple industry partners, we are starting up a special interest group under CoSAI for defining the future of ML signing and including tamper-proof ML metadata, such as model cards and evaluation results.

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New security requirements adopted by HTTPS certificate industry

Posted by Chrome Root Program, Chrome Security Team

The Chrome Root Program launched in 2022 as part of Google’s ongoing commitment to upholding secure and reliable network connections in Chrome. We previously described how the Chrome Root Program keeps users safe, and described how the program is focused on promoting technologies and practices that strengthen the underlying security assurances provided by Transport Layer Security (TLS). Many of these initiatives are described on our forward looking, public roadmap named “Moving Forward, Together.”

At a high-level, “Moving Forward, Together” is our vision of the future. It is non-normative and considered distinct from the requirements detailed in the Chrome Root Program Policy. It’s focused on themes that we feel are essential to further improving the Web PKI ecosystem going forward, complementing Chrome’s core principles of speed, security, stability, and simplicity. These themes include:

  • Encouraging modern infrastructures and agility
  • Focusing on simplicity
  • Promoting automation
  • Reducing mis-issuance
  • Increasing accountability and ecosystem integrity
  • Streamlining and improving domain validation practices
  • Preparing for a “post-quantum” world

Earlier this month, two “Moving Forward, Together” initiatives became required practices in the CA/Browser Forum Baseline Requirements (BRs). The CA/Browser Forum is a cross-industry group that works together to develop minimum requirements for TLS certificates. Ultimately, these new initiatives represent an improvement to the security and agility of every TLS connection relied upon by Chrome users.

If you’re unfamiliar with HTTPS and certificates, see the “Introduction” of this blog post for a high-level overview.

Multi-Perspective Issuance Corroboration

Before issuing a certificate to a website, a Certification Authority (CA) must verify the requestor legitimately controls the domain whose name will be represented in the certificate. This process is referred to as “domain control validation” and there are several well-defined methods that can be used. For example, a CA can specify a random value to be placed on a website, and then perform a check to verify the value’s presence has been published by the certificate requestor.

Despite the existing domain control validation requirements defined by the CA/Browser Forum, peer-reviewed research authored by the Center for Information Technology Policy (CITP) of Princeton University and others highlighted the risk of Border Gateway Protocol (BGP) attacks and prefix-hijacking resulting in fraudulently issued certificates. This risk was not merely theoretical, as it was demonstrated that attackers successfully exploited this vulnerability on numerous occasions, with just one of these attacks resulting in approximately $2 million dollars of direct losses.

Multi-Perspective Issuance Corroboration (referred to as “MPIC”) enhances existing domain control validation methods by reducing the likelihood that routing attacks can result in fraudulently issued certificates. Rather than performing domain control validation and authorization from a single geographic or routing vantage point, which an adversary could influence as demonstrated by security researchers, MPIC implementations perform the same validation from multiple geographic locations and/or Internet Service Providers. This has been observed as an effective countermeasure against ethically conducted, real-world BGP hijacks.

The Chrome Root Program led a work team of ecosystem participants, which culminated in a CA/Browser Forum Ballot to require adoption of MPIC via Ballot SC-067. The ballot received unanimous support from organizations who participated in voting. Beginning March 15, 2025, CAs issuing publicly-trusted certificates must now rely on MPIC as part of their certificate issuance process. Some of these CAs are relying on the Open MPIC Project to ensure their implementations are robust and consistent with ecosystem expectations.

We’d especially like to thank Henry Birge-Lee, Grace Cimaszewski, Liang Wang, Cyrill Krähenbühl, Mihir Kshirsagar, Prateek Mittal, Jennifer Rexford, and others from Princeton University for their sustained efforts in promoting meaningful web security improvements and ongoing partnership.

Linting

Linting refers to the automated process of analyzing X.509 certificates to detect and prevent errors, inconsistencies, and non-compliance with requirements and industry standards. Linting ensures certificates are well-formatted and include the necessary data for their intended use, such as website authentication.

Linting can expose the use of weak or obsolete cryptographic algorithms and other known insecure practices, improving overall security. Linting improves interoperability and helps CAs reduce the risk of non-compliance with industry standards (e.g., CA/Browser Forum TLS Baseline Requirements). Non-compliance can result in certificates being “mis-issued”. Detecting these issues before a certificate is in use by a site operator reduces the negative impact associated with having to correct a mis-issued certificate.

There are numerous open-source linting projects in existence (e.g., certlint, pkilint, x509lint, and zlint), in addition to numerous custom linting projects maintained by members of the Web PKI ecosystem. “Meta” linters, like pkimetal, combine multiple linting tools into a single solution, offering simplicity and significant performance improvements to implementers compared to implementing multiple standalone linting solutions.

Last spring, the Chrome Root Program led ecosystem-wide experiments, emphasizing the need for linting adoption due to the discovery of widespread certificate mis-issuance. We later participated in drafting CA/Browser Forum Ballot SC-075 to require adoption of certificate linting. The ballot received unanimous support from organizations who participated in voting. Beginning March 15, 2025, CAs issuing publicly-trusted certificates must now rely on linting as part of their certificate issuance process.

What’s next?

We recently landed an updated version of the Chrome Root Program Policy that further aligns with the goals outlined in “Moving Forward, Together.” The Chrome Root Program remains committed to proactive advancement of the Web PKI. This commitment was recently realized in practice through our proposal to sunset demonstrated weak domain control validation methods permitted by the CA/Browser Forum TLS Baseline Requirements. The weak validation methods in question are now prohibited beginning July 15, 2025.

It’s essential we all work together to continually improve the Web PKI, and reduce the opportunities for risk and abuse before measurable harm can be realized. We continue to value collaboration with web security professionals and the members of the CA/Browser Forum to realize a safer Internet. Looking forward, we’re excited to explore a reimagined Web PKI and Chrome Root Program with even stronger security assurances for the web as we navigate the transition to post-quantum cryptography. We’ll have more to say about quantum-resistant PKI later this year.

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Titan Security Keys now available in more countries

Posted by Christiaan Brand, Group Product Manager

We’re excited to announce that starting today, Titan Security Keys are available for purchase in more than 10 new countries:

  • Ireland

  • Portugal

  • The Netherlands

  • Denmark

  • Norway

  • Sweden

  • Finland

  • Australia

  • New Zealand

  • Singapore

  • Puerto Rico

This expansion means Titan Security Keys are now available in 22 markets, including previously announced countries like Austria, Belgium, Canada, France, Germany, Italy, Japan, Spain, Switzerland, the UK, and the US.

What is a Titan Security Key?

A Titan Security Key is a small, physical device that you can use to verify your identity when you sign in to your Google Account. It’s like a second password that’s much harder for cybercriminals to steal.

Titan Security Keys allow you to store your passkeys on a strong, purpose-built device that can help protect you against phishing and other online attacks. They’re easy to use and work with a wide range of devices and services as they’re compatible with the FIDO2 standard.

How do I use a Titan Security Key?

To use a Titan Security Key, you simply plug it into your computer’s USB port or tap it to your device using NFC. When you’re asked to verify your identity, you’ll just need to tap the button on the key.

Where can I buy a Titan Security Key?

You can buy Titan Security Keys on the Google Store.

We’re committed to making our products available to as many people as possible and we hope this expansion will help more people stay safe online.


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Announcing OSV-Scanner V2: Vulnerability scanner and remediation tool for open source

Posted by Rex Pan and Xueqin Cui, Google Open Source Security Team

In December 2022, we released the open source OSV-Scanner tool, and earlier this year, we open sourced OSV-SCALIBR. OSV-Scanner and OSV-SCALIBR, together with OSV.dev are components of an open platform for managing vulnerability metadata and enabling simple and accurate matching and remediation of known vulnerabilities. Our goal is to simplify and streamline vulnerability management for developers and security teams alike.

Today, we’re thrilled to announce the launch of OSV-Scanner V2.0.0, following the announcement of the beta version. This V2 release builds upon the foundation we laid with OSV-SCALIBR and adds significant new capabilities to OSV-Scanner, making it a comprehensive vulnerability scanner and remediation tool with broad support for formats and ecosystems. 



What’s new

Enhanced Dependency Extraction with OSV-SCALIBR

This release represents the first major integration of OSV-SCALIBR features into OSV-Scanner, which is now the official command-line code and container scanning tool for the OSV-SCALIBR library. This integration also expanded our support for the kinds of dependencies we can extract from projects and containers:

Source manifests and lockfiles:

  • .NET: deps.json

  • Python: uv.lock

  • JavaScript: bun.lock

  • Haskell: cabal.project.freeze, stack.yaml.lock

Artifacts:

  • Node modules

  • Python wheels

  • Java uber jars

  • Go binaries


Layer and base image-aware container scanning

Previously, OSV-Scanner focused on scanning of source repositories and language package manifests and lockfiles. OSV-Scanner V2 adds support for comprehensive, layer-aware scanning for Debian, Ubuntu, and Alpine container images. OSV-Scanner can now analyze container images to provide:

  • Layers where a package was first introduced

  • Layer history and commands

  • Base images the image is based on (leveraging a new experimental API provided by deps.dev).

  • OS/Distro the container is running on

  • Filtering of vulnerabilities that are unlikely to impact your container image


This layer analysis currently supports the following OSes and languages:

Distro Support:

  • Alpine OS

  • Debian

  • Ubuntu

Language Artifacts Support:

  • Go

  • Java

  • Node

  • Python

Interactive HTML output

Presenting vulnerability scan information in a clear and actionable way is difficult, particularly in the context of container scanning. To address this, we built a new interactive local HTML output format. This provides more interactivity and information compared to terminal only outputs, including:

  • Severity breakdown

  • Package and ID filtering

  • Vulnerability importance filtering

  • Full vulnerability advisory entries


And additionally for container image scanning:

  • Layer filtering

  • Image layer information

  • Base image identification


Illustration of HTML output for container image scanning


Guided remediation for Maven pom.xml

Last year we released a feature called guided remediation for npm, which streamlines vulnerability management by intelligently suggesting prioritized, targeted upgrades and offering flexible strategies. This ultimately maximizes security improvements while minimizing disruption. We have now expanded this feature to Java through support for Maven pom.xml.

With guided remediation support for Maven, you can remediate vulnerabilities in both direct and transitive dependencies through direct version updates or overriding versions through dependency management.


We’ve introduced a few new things for our Maven support:

  • A new remediation strategy override.

  • Support for reading and writing pom.xml files, including writing changes to local parent pom files. We leverage OSV-Scalibr for Maven transitive dependency extraction.

  • A private registry can be specified to fetch Maven metadata.

  • A new experimental subcommend to update all your dependencies in pom.xml to the latest version.


We also introduced machine readable output for guided remediation that makes it easier to integrate guided remediation into your workflow.


What’s next?

We have exciting plans for the remainder of the year, including:

  • Continued OSV-SCALIBR Convergence: We will continue to converge OSV-Scanner and OSV-SCALIBR to bring OSV-SCALIBR’s functionality to OSV-Scanner’s CLI interface.

  • Expanded Ecosystem Support: We’ll expand the number of ecosystems we support across all the features currently in OSV-Scanner, including more languages for guided remediation, OS advisories for container scanning, and more general lockfile support for source code scanning.

  • Full Filesystem Accountability for Containers: Another goal of osv-scanner is to give you the ability to know and account for every single file on your container image, including sideloaded binaries downloaded from the internet.

  • Reachability Analysis: We’re working on integrating reachability analysis to provide deeper insights into the potential impact of vulnerabilities.

  • VEX Support: We’re planning to add support for Vulnerability Exchange (VEX) to facilitate better communication and collaboration around vulnerability information.


Try OSV-Scanner V2

You can try V2.0.0 and contribute to its ongoing development by checking out OSV-Scanner or the OSV-SCALIBR repository. We welcome your feedback and contributions as we continue to improve the platform and make vulnerability management easier for everyone.

If you have any questions or if you would like to contribute, don’t hesitate to reach out to us at osv-discuss@google.com, or post an issue in our issue tracker.

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Vulnerability Reward Program: 2024 in Review

Posted by Dirk Göhmann

In 2024, our Vulnerability Reward Program confirmed the ongoing value of engaging with the security research community to make Google and its products safer. This was evident as we awarded just shy of $12 million to over 600 researchers based in countries around the globe across all of our programs.



Vulnerability Reward Program 2024 in Numbers


You can learn about who’s reporting to the Vulnerability Reward Program via our Leaderboard – and find out more about our youngest security researchers who’ve recently joined the ranks of Google bug hunters.



VRP Highlights in 2024

In 2024 we made a series of changes and improvements coming to our vulnerability reward programs and related initiatives:


  • The Google VRP revamped its reward structure, bumping rewards up to a maximum of $151,515, the Mobile VRP is now offering up to $300,000 for critical vulnerabilities in top-tier apps, Cloud VRP has a top-tier award of up $151,515, and Chrome awards now peak at $250,000 (see the below section on Chrome for details).

  • We rolled out InternetCTF – to get rewarded, discover novel code execution vulnerabilities in open source and provide Tsunami plugin patches for them.

  • The Abuse VRP saw a 40% YoY increase in payouts – we received over 250 valid bugs targeting abuse and misuse issues in Google products, resulting in over $290,000 in rewards.

  • To improve the payment process for rewards going to bug hunters, we introduced Bugcrowd as an additional payment option on bughunters.google.com alongside the existing standard Google payment option. 

  • We hosted two editions of bugSWAT for training, skill sharing, and, of course, some live hacking – in August, we had 16 bug hunters in attendance in Las Vegas, and in October, as part of our annual security conference ESCAL8 in Malaga, Spain, we welcomed 40 of our top researchers. Between these two events, our bug hunters were rewarded $370,000 (and plenty of swag).

  • We doubled down on our commitment to support the next generation of security engineers by hosting four init.g workshops (Las Vegas, São Paulo, Paris, and Malaga). Follow the Google VRP channel on X to stay tuned on future events.



More detailed updates on selected programs are shared in the following sections.



Android and Google Devices

In 2024, the Android and Google Devices Security Reward Program and the Google Mobile Vulnerability Reward Program, both part of the broader Google Bug Hunters program, continued their mission to fortify the Android ecosystem, achieving new heights in both impact and severity. We awarded over $3.3 million in rewards to researchers who demonstrated exceptional skill in uncovering critical vulnerabilities within Android and Google mobile applications. 



The above numbers mark a significant change compared to previous years. Although we saw an 8% decrease in the total number of submissions, there was a 2% increase in the number of critical and high vulnerabilities. In other words, fewer researchers are submitting fewer, but more impactful bugs, and are citing the improved security posture of the Android operating system as the central challenge. This showcases the program’s sustained success in hardening Android.



This year, we had a heightened focus on Android Automotive OS and WearOS, bringing actual automotive devices to multiple live hacking events and conferences. At ESCAL8, we hosted a live-hacking challenge focused on Pixel devices, resulting in over $75,000 in rewards in one weekend, and the discovery of several memory safety vulnerabilities. To facilitate learning, we launched a new Android hacking course in collaboration with external security researchers, focused on mobile app security, designed for newcomers and veterans alike. Stay tuned for more.



We extend our deepest gratitude to the dedicated researchers who make the Android ecosystem safer. We’re proud to work with you! Special thanks to Zinuo Han (@ele7enxxh) for their expertise in Bluetooth security, blunt (@blunt_qian) for holding the record for the most valid reports submitted to the Google Play Security Reward Program, and WANG,YONG (@ThomasKing2014) for groundbreaking research on rooting Android devices with kernel MTE enabled. We also appreciate all researchers who participated in last year’s bugSWAT event in Málaga. Your contributions are invaluable! 



Chrome

Chrome did some remodeling in 2024 as we updated our reward amounts and structure to incentivize deeper research. For example, we increased our maximum reward for a single issue to $250,000 for demonstrating RCE in the browser or other non-sandboxed process, and more if done directly without requiring a renderer compromise. 



In 2024, UAF mitigation MiraclePtr was fully launched across all platforms, and a year after the initial launch, MiraclePtr-protected bugs are no longer being considered exploitable security bugs. In tandem, we increased the MiraclePtr Bypass Reward to $250,128. Between April and November, we also launched the first and second iterations of the V8 Sandbox Bypass Rewards as part of the progression towards the V8 sandbox, eventually becoming a security boundary in Chrome. 



We received 337 reports of unique, valid security bugs in Chrome during 2024, and awarded 137 Chrome VRP researchers $3.4 million in total. The highest single reward of 2024 was $100,115 and was awarded to Mickey for their report of a MiraclePtr Bypass after MiraclePtr was initially enabled across most platforms in Chrome M115 in 2023. We rounded out the year by announcing the top 20 Chrome VRP researchers for 2024, all of whom were gifted new Chrome VRP swag, featuring our new Chrome VRP mascot, Bug.



Cloud VRP

The Cloud VRP launched in October as a Cloud-focused vulnerability reward program dedicated to Google Cloud products and services. As part of the launch, we also updated our product tiering and improved our reward structure to better align our reports with their impact on Google Cloud. This resulted in over 150 Google Cloud products coming under the top two reward tiers, enabling better rewards for our Cloud researchers and a more secure cloud.



Since its launch, Google Cloud VRP triaged over 400 reports and filed over 200 unique security vulnerabilities for Google Cloud products and services leading to over $500,000 in researcher rewards. 



Our highlight last year was launching at the bugSWAT event in Málaga where we got to meet many of our amazing researchers who make our program so successful! The overwhelming positive feedback from the researcher community continues to propel us to mature Google Cloud VRP further this year. Stay tuned for some exciting announcements!



Generative AI

We’re celebrating an exciting first year of AI bug bounties.  We received over 150 bug reports – over $55,000 in rewards so far – with one-in-six leading to key improvements. 



We also ran a bugSWAT live-hacking event targeting LLM products and received 35 reports, totaling more than $87,000 – including issues like “Hacking Google Bard – From Prompt Injection to Data Exfiltration” and “We Hacked Google A.I. for $50,000”.



Keep an eye on Gen AI in 2025 as we focus on expanding scope and sharing additional ways for our researcher community to contribute. 

Looking Forward to 2025

In 2025, we will be celebrating 15 years of VRP at Google, during which we have remained fully committed to fostering collaboration, innovation, and transparency with the security community, and will continue to do so in the future. Our goal remains to stay ahead of emerging threats, adapt to evolving technologies, and continue to strengthen the security posture of Google’s products and services. 



We want to send a huge thank you to our bug hunter community for helping us make Google products and platforms more safe and secure for our users around the world – and invite researchers not yet engaged with the Vulnerability Reward Program to join us in our mission to keep Google safe! 



Thank you to Dirk Göhmann, Amy Ressler, Eduardo Vela, Jan Keller, Krzysztof Kotowicz, Martin Straka, Michael Cote, Mike Antares, Sri Tulasiram, and Tony Mendez.

Tip: Want to be informed of new developments and events around our Vulnerability Reward Program? Follow the Google VRP channel on X to stay in the loop and be sure to check out the Security Engineering blog, which covers topics ranging from VRP updates to security practices and vulnerability descriptions (30 posts in 2024)!

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New AI-Powered Scam Detection Features to Help Protect You on Android

Posted by Lyubov Farafonova, Product Manager, Phone by Google; Alberto Pastor Nieto, Sr. Product Manager Google Messages and RCS Spam and Abuse

Google has been at the forefront of protecting users from the ever-growing threat of scams and fraud with cutting-edge technologies and security expertise for years. In 2024, scammers used increasingly sophisticated tactics and generative AI-powered tools to steal more than $1 trillion from mobile consumers globally, according to the Global Anti-Scam Alliance. And with the majority of scams now delivered through phone calls and text messages, we’ve been focused on making Android’s safeguards even more intelligent with powerful Google AI to help keep your financial information and data safe.

Today, we’re launching two new industry-leading AI-powered scam detection features for calls and text messages, designed to protect users from increasingly complex and damaging scams. These features specifically target conversational scams, which can often appear initially harmless before evolving into harmful situations.

To enhance our detection capabilities, we partnered with financial institutions around the world to better understand the latest advanced and most common scams their customers are facing. For example, users are experiencing more conversational text scams that begin innocently, but gradually manipulate victims into sharing sensitive data, handing over funds, or switching to other messaging apps. And more phone calling scammers are using spoofing techniques to hide their real numbers and pretend to be trusted companies.

Traditional spam protections are focused on protecting users before the conversation starts, and are less effective against these latest tactics from scammers that turn dangerous mid-conversation and use social engineering techniques. To better protect users, we invested in new, intelligent AI models capable of detecting suspicious patterns and delivering real-time warnings over the course of a conversation, all while prioritizing user privacy.

Scam Detection for messages

We’re building on our enhancements to existing Spam Protection in Google Messages that strengthen defenses against job and delivery scams, which are continuing to roll out to users. We’re now introducing Scam Detection to detect a wider range of fraudulent activities.

Scam Detection in Google Messages uses powerful Google AI to proactively address conversational scams by providing real-time detection even after initial messages are received. When the on-device AI detects a suspicious pattern in SMS, MMS, and RCS messages, users will now get a message warning of a likely scam with an option to dismiss or report and block the sender.

As part of the Spam Protection setting, Scam Detection on Google Messages is on by default and only applies to conversations with non-contacts. Your privacy is protected with Scam Detection in Google Messages, with all message processing remaining on-device. Your conversations remain private to you; if you choose to report a conversation to help reduce widespread spam, only sender details and recent messages with that sender are shared with Google and carriers. You can turn off Spam Protection, which includes Scam Detection, in your Google Messages at any time.

Scam Detection in Google Messages is launching in English first in the U.S., U.K. and Canada and will expand to more countries soon.

Scam Detection for calls

More than half of Americans reported receiving at least one scam call per day in 2024. To combat the rise of sophisticated conversational scams that deceive victims over the course of a phone call, we introduced Scam Detection late last year to U.S.-based English-speaking Phone by Google public beta users on Pixel phones.

We use AI models processed on-device to analyze conversations in real-time and warn users of potential scams. If a caller, for example, tries to get you to provide payment via gift cards to complete a delivery, Scam Detection will alert you through audio and haptic notifications and display a warning on your phone that the call may be a scam.

During our limited beta, we analyzed calls with Gemini Nano, Google’s built-in, on-device foundation model, on Pixel 9 devices and used smaller, robust on-device machine-learning models for Pixel 6+ users. Our testing showed that Gemini Nano outperformed other models, so as a result, we’re currently expanding the availability of the beta to bring the most capable Scam Detection to all English-speaking Pixel 9+ users in the U.S.

Similar to Scam Detection in messaging, we built this feature to protect your privacy by processing everything on-device. Call audio is processed ephemerally and no conversation audio or transcription is recorded, stored on the device, or sent to Google or third parties. Scam Detection in Phone by Google is off by default to give users control over this feature, as phone call audio is more ephemeral compared to messages, which are stored on devices. Scam Detection only applies to calls that could potentially be scams, and is never used during calls with your contacts. If enabled, Scam Detection will beep at the start and during the call to notify participants the feature is on. You can turn off Scam Detection at any time, during an individual call or for all future calls.

According to our research and a Scam Detection beta user survey, these types of alerts have already helped people be more cautious on the phone, detect suspicious activity, and avoid falling victim to conversational scams.

Keeping Android users safe with the power of Google AI

We’re committed to keeping Android users safe, and that means constantly evolving our defenses against increasingly sophisticated scams and fraud. Our investment in intelligent protection is having real-world impact for billions of users. Leviathan Security Group, a cybersecurity firm, conducted a funded evaluation of fraud protection features on a number of smartphones and found that Android smartphones, led by the Pixel 9 Pro, scored highest for built-in security features and anti-fraud efficacy1.

With AI-powered innovations like Scam Detection in Messages and Phone by Google, we’re giving you more tools to stay one step ahead of bad actors. We’re constantly working with our partners across the Android ecosystem to help bring new security features to even more users. Together, we’re always working to keep you safe on Android.

Notes

  1. Based on third-party research funded by Google LLC in Feb 2025 comparing the Pixel 9 Pro, iPhone 16 Pro, Samsung S24+ and Xiaomi 14 Ultra. Evaluation based on no-cost smartphone features enabled by default. Some features may not be available in all countries. ↩

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