https://www.adweek.com/brand-marketing/ana-global-cmo-growth-council-meeting/

On Tuesday, the French news publisher Le Monde launched a cross-offer initiative with The New York Times, an arrangement that will allow Le Monde—as well as its sister publications Courrier International and Le Nouvel Obs—to offer their subscribers a one-year New York Times subscription for no additional cost.

The deal is not a partnership, per se. The bundle will not be available to all Le Monde readers; instead, the French outlet will selectively present it to those with a high propensity to respond to it, such as readers who engage with the international section, according to Le Monde’s chief digital operations officer Lou Grasser. It also only goes one way: The New York Times will not be offering its readers an opportunity to subscribe to Le Monde. 

“We have seen encouraging results from other bundles, but this is fundamentally a test,” Grasser said. “The philosophy is the same that guides our deals with OpenAI and Perplexity, in that the faster we go with testing the sooner we will know what works.”

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The bundle includes The New York Times’ full digital subscription, which includes products like Cooking and Games. It is also meant to complement Le Monde in English, a translated version of Le Monde that the publisher debuted in 2022, to entice European readers who do not speak French. 

For Le Monde, the initiative is part of a larger effort to scale its subscription business. The French news publisher, the largest in the country, now has 665,000 total subscribers, 600,000 of which are digital-only, according to Grasser. (Le Monde in English has brought in around 13,000 subscribers, Grasser said, a fraction of the 150,000 English-speaking customers it had set as its 2025 goal.)

More broadly, the outlet had set for itself the ambitious goal of netting 1 million subscribers by 2025, a mark that it is also bound to miss. But it continues to work diligently toward its stated aim, and has made ample use of innovative partnerships to do so, including tie-ups with Spotify and HBO in the last year.

Overall, Le Monde Group generated €310 million ($360 million) in 2024, roughly half of which, around €150 million, came from Le Monde alone, according to Grasser. Subscriptions accounted for approximately 50% of that business, or around €75 million, which breaks down further into digital (35%) and print (15%). By the end of 2025, Le Monde anticipates generating around €70 million ($81 million) from digital subscribers, a growth of 33%.

For The Times, the initiative accomplishes several objectives. It nets it a cut of the generated revenue, for one. But more critically, it provides the news publisher, which itself aims to have 15 million subscribers by 2027, a valuable source of data about the international appetite for its subscription product. It also gains access to this data at virtually no cost, as the structure of the Le Monde deal does not require The Times to manage the infrastructure of the bundle.

“This provides The New York Times with exposure to a new market without any customer acquisition cost,” according to Felix Danczak, a subscription marketing analyst who is now head of AI and growth at venture firm Pembroke VCT. “They also get behavioral and engagement data on international readers for free, which will tell them whether or not it makes sense to do this expansion elsewhere.”

Indeed, the tie-up with Le Monde is not the first such arrangement the Gray Lady has inked. 

Last fall, four European news publishers—El País (Spain), Politiken (Denmark), the Irish Times (Ireland), and Corriere della Sera (Italy)—sweetened their subscription deals by offering complimentary access to The New York Times. The Times has these deals with more than 20 publishers, according to Nieman Lab. 

The tie-ups are part of a broader strategy for the publisher, which in March 2022 set itself the goal of becoming “the essential subscription for every curious, English-speaking person seeking to understand and engage with the world.”

All the news that’s translated to print

Lately, even a goal as lofty as serving “every curious, English-speaking person” has begun to feel limited. 

Crucially, instantaneous language translation has become more widespread. The technology has been gradually improving for years, and AI has only accelerated that rate of growth. 

Le Monde in English, for instance, became economically feasible only because AI could do the bulk of the translation work (although the final copy is reviewed by a team of editors), according to the company, which is majority owned by French billionaire Xavier Niel. (Niel is also an investor in several AI startups.)

So far, The Times’ forays into international markets have come in the form of untranslated products. Enough people speak English worldwide that it has not yet been a materially limiting factor. 

But with nearly costless translation now readily accessible, The Times could soon begin to evolve its international expansion strategy, offering its product in consumers’ native language and dramatically increasing its total addressable market, according to Danczak.

A spokesperson for The New York Times declined to comment.

The company has also faced pressure from activist investors to increase its use of the technology. In August, investment firm Fivespan announced it had built up a position in The Times, pushing the publisher to more aggressively embrace AI. Fivespan founders Dylan Haggart and Sarah Coyne pointed to AI-enabled translation as a key growth accelerant that The Times has left underutilized. 

“AI is a clear tailwind for The New York Times,” Fivespan said in a letter obtained by Bloomberg in August. “Our work shows it can more than double the company’s long-term revenue and profit potential: enhancing growth by reaching broader audiences, converting more readers into paying subscribers, and unlocking new, lucrative profit pools.”

If The Times believes it can generate 15 million English-speaking subscribers by the end of next year, imagine how much larger its potential market could be once language is no longer a barrier, Danczak said. Translation, properly handled, could dramatically balloon The Times’ subscriber base, transforming its business into a truly international resource.

The benefits extend even beyond subscribers. Translation renders not only new reporting more valuable, but archival content as well, according to Jeff Kaloski, managing director and partner at L.E.K. Consulting. 

A similar phenomenon is playing out right now in the entertainment industry, Kaloski said. Backlogs of content going back decades—more than a century in some cases—can now be localized at next to no cost, unlocking intellectual property that was once cost-prohibitive to translate.

As AI firms work to secure licensing deals with trusted information providers, The Times’ ability to translate its archive into other languages could make its data exponentially more valuable. Rather than limit its utility to English-based queries, Times content could be used to inform answers in any language, according to Danczak.

Of course, this all remains hypothetical at the moment. 

The Times, alongside Le Monde and others, has moved slowly in its embrace of AI-enabled translation in order to ensure that its quality of reporting does not suffer. Automated translation tools still produce errors particularly in less-popular languages, and competitors that have moved more quickly, like Bloomberg, have hit snags trying to inject AI-generated summaries and localization into their coverage. 

For The Times to be “the essential subscription” for the non-English speaking world, it will first need to be trusted in other languages.

https://www.adweek.com/media/new-york-times-le-monde-bundle-ai-translation-onbackground/

Walmart is the latest company to strike a deal with OpenAI, following a string of commerce-related AI announcements over the last few weeks.

The tie-up allows Walmart shoppers to buy products within ChatGPT, OpenAI’s popular chatbot, starting this fall.

It follows deals between OpenAI and other ecommerce players including Shopify and Etsy, showing growing demand for the ability to complete a wide variety of purchases on behalf of users through a chatbot, a growing trend referred to as agentic commerce. OpenAI first debuted shopping within ChatGPT back in April.

“For many years now, ecommerce shopping experiences have consisted of a search bar and a long list of item responses. That is about to change,” Doug McMillon, Walmart president and CEO, said in a statement. “There is a native AI experience coming that is multi-media, personalized and contextual.”

Walmart has already made significant investments in generative AI technology, giving its store employees access to AI tools and launching an AI-powered shopping assistant, Sparky, in June.

“We’re excited to partner with Walmart to make everyday purchases a little simpler,” Sam Altman, cofounder and CEO of OpenAI, said in a statement. “It’s just one way AI will help people every day under our work together.”

The deal with OpenAI is consistent with Walmart’s track record of jumping early into new technology, UBS analyst Michael Lasser wrote in a note.

“ChatGPT is being increasingly used for product discovery,” Lasser continued. “This should provide incrementality and differentiation vs. the rest of retail.”

https://www.adweek.com/commerce/shoppers-will-soon-be-able-to-buy-walmart-products-on-chatgpt/

Perplexity has paused accepting new advertisers as it rethinks how ads fit into its AI search experience, signaling a change in its monetization push.

At Advertising Week in New York City, Jessica Chan, head of publisher partnerships at Perplexity, said that the company is not “taking any new advertisers,” and that ads aren’t currently on the roadmap for Perplexity’s AI browser, Comet.

The move comes after the August departure of ad sales head Taz Patel, signaling a shift from Perplexity’s previous plan to scale its ad business beyond the U.S. In December, Perplexity added The Independent, LA Times, and Blavity to its Perplexity’s Publisher Program that pays publishers when content appears next to ads. At the time, Chan cited international partnerships as key to Perplexity’s growth.

Perplexity launched its ad offering last year and began testing campaigns with brands including Indeed, PMG, Universal McCann, and Whole Foods Market. Those early campaigns let brands sponsor follow-up questions in search results.

“That still exists today, but we didn’t want to inundate our user experience with a ton of ads overnight,” Chan said during the panel. “We’re continuing to scale it very thoughtfully and methodically—probably not at the scale everybody’s hoping for.”

She also noted that brands are rethinking how to spend their budgets. Chan said many advertisers are moving away from performance-focused, traditional search and towards top-of-funnel brand awareness—an area Perplexity may pursue down the line.

Perplexity did not respond to request for additional comment beyond Chan’s comments at Advertising Week.

Advertising appears to make up only a small portion of Perplexity’s revenue. Last year, the company generated just $20,000 in ad revenue out of $34 million in total revenue, according to The Information.

As Perplexity reassesses its ads ambitions, questions remain about how much revenue its model is actually driving for publishers. 

Two ad buyers told ADWEEK that they’ve struggled to measure performance metrics like click-through rates and return on ad spend on Perplexity because the platform doesn’t offer tools akin to established ad players. One publisher participating in Perplexity’s revenue-sharing program said the company has generated little meaningful income so far.

“The digital business models that have proven most successful are performance-based,” said Debra Aho Williamson, founder and chief analyst, Sonata Insights. “Companies that launched with a brand awareness model like Perplexity have struggled to maintain momentum.”

Separately from advertising, Perplexity continues to build out its ecosystem around its web browser Comet. Perplexity describes Comet as a browser for “trusted publishers and journalists” including CNN, Condé Nast, The Washington Post, the Los Angeles Times, Fortune, Le Monde, and Le Figaro. Last month, Perplexity made Comet free to all users.

https://www.adweek.com/media/perplexity-pauses-new-advertising-deals-to-reassess-ambitions/

https://www.adweek.com/programmatic/zeta-group-quadruple-client-revenue-ai-athena/

https://www.adweek.com/agencies/agencies-are-done-only-dabbling-in-ai-now-comes-the-hard-part/

The annual DataTribe Challenge uncovers the most promising cybersecurity startups, and gives them an opportunity to pitch their firms and products to an audience of venture and private equity firms and security experts.

DataTribe itself is a seed stage venture foundry focused on early stage cybersecurity firms – startups. Each year the firm holds the DataTribe Challenge – effectively a pitch competition for startups. From these, five finalists are chosen and allowed to deliver pitches during DataTribe’s Cyber Innovation Day conference (this year, set for November 4, 2025). 

The foundry element comes from the nature of DataTribe and the common willingness of startups to accept as much help and advice as they can get. “We call ourselves a startup foundry,” explains Leo Scott  (MD and chief innovation officer at DataTribe). “What that means is our team will jump in and co-build with the founders to help accelerate growth and de-risk things. Our whole team at DataTribe are all former startup operators. We’re just some crazy people that kept building startups over and over — and then eventually ended up joining DataTribe to help others do the same thing.  We enjoy jumping in and getting our hands dirty.” And the startups benefit from their experience.

Each of the five finalists in the Challenge are automatically awarded $5,000 (designed to amply cover expenses). But the real reward is being able to pitch to the Innovation Day conference audience before the ultimate winner is chosen.

This conference is attended by cybersecurity venture and private equity firms, researchers, government officials and defense agencies, and a large sprinkling of CISOs. It is a focused channel for getting attractive cybersecurity startups in front of potential investors – including, of course, DataTribe itself. “Historically, it’s not guaranteed, but historically DataTribe has invested in the winners. But if it’s not DataTribe, there’s a whole audience filled with the top cybersecurity investors in the industry,” said Scott.

The five finalists are now chosen. By definition, they are emerging security vendors to watch – and not surprisingly, this year four of the five directly involve AI. The firms are Ackuity, Cytadel, Evercoast, Starseer, and Tensor Machines. 

Tensor Machines is the only finalist not directly focused on AI. This firm provides a hardware security solution that enables printed circuit boards, sensors, and other hardware components to continuously self-certify authenticity while in use. It offers more granularity with fewer development issues than other approaches to the same problem.

Ackuity provides security for AI agents. It collects data from all the interactions occurring inside an agentic system, whether it’s between the user and the agent, between different agents, or the agent and other tools. It builds this telemetry in real time providing real time threat detection and response capabilities. 

“They’re doing a kind of pre-SIEM processing, because existing SIEMs don’t really have the context to make sense of this new kind of agent telemetry,” comments Scott. “It can generate events, actions or alerts that can be directly evaluated or sent into a SIEM if you have one.”

Cytadel provides AI-driven autonomous red teaming. It is based in London, UK, and its founder and CEO has a background in GCHQ and was head of red teaming  at the Bank of England.

“He has an automated red teaming platform leveraging AI to do decision making to build a full attack path at a level of complexity that historically could only be done by human red teamers. You can validate your defenses, quantify your resistance to ransomware, generate a list of actions that can be taken, and even produce an executive report on weaknesses and possible solutions,” explains Scott.

Evercoast is described as more cyber adjacent than cybersecurity focused. It provides the data used in embodied AI; that is, the AI that controls robots. The firm started by servicing the entertainment industry; by capturing the real movement of an actor and allowing that to be included in a fake CGI scene. “They had multiple cameras and other sensors attached to the subject that would collect the data of movement that could then be added to the movie,” explains Scott.

But as robots and AI have evolved, and mobile robots – bi-pedaled and wheeled – are emerging, the firm is adapting its process to teach robots how to move independently through their AI training rather than just manual control. Evercoast is training robots to collect all the movement data from manually controlled robots or humans doing different tasks and allowing the AI to use that as its own training data. It’s not that simple, but by the end of the process, the robot has clean data from which to learn. “And the better the learning, the more difficult it will be to hijack the robot for nefarious purposes,” adds Scott.

Starseer, the fifth finalist, provides security for new AI models. The rush to adopt AI as rapidly as possible is creating a brand new threat surface that is little understood and frequently ignored. Starseer provides a platform that enables AI system builders to reduce system risk and improve resilience before deployment.

“It treats AI-systems as probabilistic, analyzing the models, the training data, the refinement steps, and the surrounding libraries and system architectures to surface exploitable weaknesses and operational risks – ensuring production systems are safer and more reliable,” explains DataTribe. 

The DataTribe challenge is a win-win for all concerned. The most exciting new developments in cybersecurity get to pitch their companies to multiple VC firms on Cyber Innovation Day, while DataTribe itself has had early access to all the finalists. And we shouldn’t dismiss the relevance of glimpsing the future for all the security professionals in the audience.

Related: Former NSA Director Rob Joyce Joins DataTribe as Venture Partner

Related: Frenos Raises $3.88M in Seed Funding for OT Security Assessment Platform

Related: Email Security Startup AegisAI Launches With $13 Million in Funding

Related: Offensive AI Startup Dreadnode Secures $14M to Stress-Test AI Systems

https://www.securityweek.com/ai-takes-center-stage-at-datatribes-cyber-innovation-day/

A relatively new security layer, AI security posture management (AI-SPM) can help organizations identify and reduce risks related to their use of AI, especially large language models. It constantly finds, evaluates, and fixes security and compliance risks throughout the organization’s AI footprint.

By making opaque AI interactions transparent and manageable, AI-SPM allows businesses to innovate with confidence, knowing their AI systems are secure, governed, and in line with policy.

AI-SPM Is Key to Safe AI Adoption

To ensure AI is adopted securely and responsibly, AI-SPM functions like a security stack, inspecting and controlling related traffic for preventing unauthorized access, unsafe outputs and policy violations. It offers clear visibility into models, agents, and AI activities across the business; making real-time security and compliance checks to keep AI usage within set limits, and follows accepted frameworks like OWASP, NIST, and MITRE. Eventually, we’ll see AI-SPM integrated into existing security controls with the aim of enabling better detection and response to AI-related ops and incidents.

Mapping OWASP Top Risks for LLMs to Practical Defenses with AI-SPM

The open source nonprofit OWASP published a list of threats posed by LLM applications including risks linked to generative AI. These threats include prompt injection, data exposure, agent misuse, and misconfigurations. AI security posture management provides specific, practical defenses that turn these complicated risks into enforceable protections. Let’s look at how AI-SPM counters key LLM security risks:

• Prompt injection and jailbreaking: Malicious inputs can manipulate LLM behavior, bypassing safety protocols and causing models to generate harmful or unauthorized outputs.

AI-SPM is designed to detect injection attempts, clean up risky inputs, and block anything unsafe from reaching users or external platforms. Essentially, it prevents jailbreaks and keeps models operating within defined security boundaries. For developers, AI-SPM monitors code assistants and IDE plugins to detect unsafe prompts and unauthorized outputs to facilitate secure use of AI tools.

• Sensitive data disclosure: LLMs may expose personal, financial, or proprietary data through their outputs, leading to privacy violations and intellectual property loss.

AI-SPM prevents sensitive data from being shared with public models (or used for external model training) by blocking or anonymizing inputs before transmission. It separates different AI application plans and enforces rules on the basis of user identity, usage context, and model capabilities.

• Data and model poisoning: Manipulates training data to embed vulnerabilities, biases, or backdoors, compromising model integrity, performance, and downstream system security.

By continuously monitoring AI assets, AI-SPM helps ensure that only trusted data sources are used during model development. Runtime security testing and red-team exercises detect vulnerabilities caused by malicious data. The system actively identifies abnormal model behavior, such as biased, toxic, or manipulated output, and brings them up for remediation prior to production release.

• Excessive agency: Autonomous agents and plugins can execute unauthorized actions, escalate privileges, or interact with sensitive systems.

AI-SPM catalogues agent workflows and enforces detailed runtime controls over their actions and reasoning paths. It locks down sensitive APIs to access and makes sure that agents run under least-privilege principles. For homegrown agents, it adds an extra layer of protection by offering real-time visibility and proactive governance, helping catch misuse early while still supporting more complex, dynamic workflows.

• Supply chain and model provenance risks: Third-party models or components may introduce vulnerabilities, poisoned data, or compliance gaps into AI pipelines.

AI-SPM keeps a central inventory of AI models and their version history. Built-in scanning tools run checks for common problems, like misconfigurations or risky dependencies. If a model doesn’t meet certain guidelines, such as compliance or verification standards, it gets flagged before reaching production.

• System prompt leakage: Exposes sensitive data or logic embedded in prompts, enabling attackers to bypass controls and exploit application behavior.

AI-SPM continuously checks system requests and user inputs to find dangerous patterns before they lead to security problems, like attempts to remove or change built-in directives. It also uses protection against prompt injection and jailbreak attacks, which are common ways to access or alter system-level commands. By finding unapproved AI tools and services, it stops the use of insecure or poorly set up LLMs that could reveal system prompts. This reduces the chance of leaking sensitive information through uncontrolled environments.

Prompt injection/jailbreaking is about misusing the model through crafted inputs. Attackers or even regular users input something malicious to make the model behave in unintended ways.

System prompt leakage is about exposing or altering the model’s internal instructions (system prompts) that guide the model’s behavior.

Shadow AI: The Unseen Risk

Shadow AI is starting to get more attention, and for good reason. Like shadow IT, employees are using public AI tools without authorization. That might mean uploading sensitive data or sidestepping governance rules, often without realizing the risks. The problem isn’t just the tools themselves, but the lack of visibility around how and where they’re being used.

AI-SPM should work to identify all AI tools in play (whether officially sanctioned or not) across networks,

endpoints, cloud platforms, and dev environments, mapping  how data moves between them, which is often the missing piece when trying to understand exposure risks. From there, it puts guardrails in place, such as blocking risky uploads, isolating unknown agents, routing activity through secure gateways, and setting up role-based approvals.

End-to-end Visibility into AI Interactions

When organizations lack visibility in how AI is being used it can hamper detection and response efforts. AI-SPM helps them pull together key data like prompts, responses, and agent actions, and sends it to existing SIEM and observability tools, making it easier for security teams to triage AI-related incidents and conduct forensic analysis.

The fast growth of AI is moving faster than any previous technology wave. It brings new threats and increases attack surfaces that old tools cannot manage. AI-SPM is designed to protect this new area, making AI a clear asset rather than an unseen risk. Whether as part of a converged platform such as SASE or deployed alone, AI-SPM is the vehicle to unlock safe, scalable, and compliant adoption of AI.

Related: Top 25 MCP Vulnerabilities Reveal How AI Agents Can Be Exploited

Related: The Wild West of Agentic AI – An Attack Surface CISOs Can’t Afford to Ignore

Related: Beyond GenAI: Why Agentic AI Was the Real Conversation at RSA 2025

Related: How Hackers Manipulate Agentic AI With Prompt Engineering

https://www.securityweek.com/will-ai-spm-become-the-standard-security-layer-for-safe-ai-adoption/

Google’s DeepMind artificial intelligence research lab this week unveiled an AI agent designed to autonomously find and fix vulnerabilities.

Google has several projects focusing on the use of AI for the discovery of vulnerabilities in software. The tech giant recently reported that its Big Sleep agent discovered a critical SQLite vulnerability and thwarted efforts to exploit it in the wild.

Its latest product is CodeMender, an AI agent that not only finds security holes but also patches them. The company argues that such tools are needed because as AI gets better at discovering flaws, it will be difficult for humans to keep up with patching. 

Related: CISO Conversations: John ‘Four’ Flynn, VP of Security and Privacy at Google DeepMind

Deepmind says CodeMender, which leverages Gemini DeepThink models, is capable of rewriting and securing existing code in order to eliminate entire classes of security bugs to prevent future exploits. 

CodeMender includes checks designed to ensure that the changes it makes do not cause regressions or other issues. 

The AI agent can reason about code — understanding and predicting the behavior of a program without actually running it — and effectively validate changes through the use of advanced program analysis and multi-agent systems. 

Advanced program analysis includes static and dynamic analysis, fuzzing, differential testing, and SMT solvers to identify the root cause of vulnerabilities and architectural weaknesses. 

As for multi-agent systems, DeepMind explained, “We developed special-purpose agents that enable CodeMender to tackle specific aspects of an underlying problem. For example, CodeMender uses a large language model-based critique tool that highlights the differences between the original and modified code in order to verify that the proposed changes do not introduce regressions, and self-correct as needed.”

Over the past six months, CodeMender has provided 72 security fixes to open source projects, some of which have millions of lines of code. However, DeepMind says it’s being cautious and all patches are reviewed before being submitted. 

Related: Google Patches Gemini AI Hacks Involving Poisoned Logs, Search Results

Related: California Gov. Gavin Newsom Signs Bill Creating AI Safety Measures

Related: Salesforce AI Hack Enabled CRM Data Theft

https://www.securityweek.com/google-deepminds-new-ai-agent-finds-and-fixes-vulnerabilities/

Google this week announced a new dedicated AI Vulnerability Reward Program (VRP) that builds on the 2023 Abuse VRP extension covering issues and vulnerabilities in its AI systems.

To date, bug hunters have earned more than $430,000 in rewards for AI-product related vulnerabilities, and the new VRP builds on that momentum and has been shaped based on the feedback received from participating researchers.

One of the most important aspects of the new AI bug bounty program is that prompt injections, jailbreaks, and alignment issues are not in its scope. However, Google encourages researchers to report these content-related issues as well.

“We don’t believe a Vulnerability Reward Program is the right format for addressing content-related issues. The primary goal of our VRP is to encourage researchers to report security vulnerabilities and abuse issues directly to Google, and to provide timely, valuable rewards to incentivize those reports,” Google explains.

All Google AI products, the company says, have in-product functionality that can be used to report content-based issues. Such reports should include information on the used model, context, and other metadata.

Within the AI VRP scope, however, the company has included attacks that modify a victim’s account or data, leak sensitive information without user approval, exfiltrate model parameters, lead to the persistent manipulation of a victim’s AI environment, lead to the exfiltration of data, enable server-side features without authorization, or cause persistent denial-of-service (DoS).

Attacks that enable phishing through persistent, cross-user injection of HTML code on Google-branded sites without a “user-generated content” warning are also within scope, if they are deemed a convincing attack vector.

As part of the program, Google’s AI products are split into three tiers, namely flagship (includes AI features on Google Search, Workspace core applications, and Gemini Apps), standard (AI features in AI Studio, Jules, and Google Workspace non-core applications), and other (other AI integrations in Google products, with certain exceptions).

The highest rewards offered as part of the new VRP are $20,000 for attacks leading to victim account or data modifications in flagship products. For similar attacks in standard products, researchers can earn rewards of up to $15,000.

The highest reward for sensitive data exfiltration from flagship and standard products is of $15,000. Researchers who find these issues in products from the ‘other’ tier can earn rewards of up to $10,000.

“Going forward, a unified reward panel will review all rewards, and will issue the highest reward possible across the abuse and security tables,” Google says.

Additional information on the AI VRP can be found on the program’s rules page.

Related: $4.5 Million Offered in New Cloud Hacking Competition

Related: Researchers Earn $150,000 for L1TF Exploit Leaking Data From Public Cloud

Related: Google Paid Out $12 Million via Bug Bounty Programs in 2024

Related: Microsoft Boosts .NET Bounty Program Rewards to $40,000

https://www.securityweek.com/google-offers-up-to-20000-in-new-ai-bug-bounty-program/