Tuesday, August 12, 2025

Study Finds Security Gaps as AI “OS Agents” Gain Power Over Digital Devices

A new academic survey has examined the emergence of AI systems that can directly operate computers, smartphones, and web browsers, warning that the same capabilities driving productivity could also expose users and businesses to new security risks.

The 36-page review, produced by Zhejiang University in collaboration with the OPPO AI Center and other institutions, outlines the design, training, and evaluation of so-called “OS agents”, large-language-model-driven assistants capable of controlling devices by interacting with their graphical interfaces. Unlike traditional voice assistants, these agents can observe the screen, interpret interface elements, plan a sequence of actions, and execute them without human input.


Researchers describe a surge of activity since 2023, with more than 60 foundation models and over 50 agent frameworks now targeting computer control. Major technology firms have begun moving these concepts into commercial products, such as OpenAI’s Operator, Anthropic’s Computer Use, Apple’s enhancements to Apple Intelligence, and Google’s Project Mariner.

How OS Agents Work

An OS agent typically captures the current state of a device through screenshots or structured interface descriptions, uses multimodal AI models to interpret what it sees, and translates its plans into clicks, swipes, keystrokes, or navigation commands. The most capable systems can handle multi-step processes across several applications, for example, making a booking, logging it in a calendar, and creating a reminder.

The survey details how these agents are built, often combining pre-trained vision-language models with custom components that handle high-resolution interface images and HTML structures. Training pipelines use public datasets, synthetic interaction records, and simulated environments to improve grounding, the mapping between instructions and on-screen actions, as well as planning skills.

Developers adopt a range of strategies to boost performance, including supervised fine-tuning with curated task sequences and reinforcement learning to improve reliability and error recovery. Frameworks usually include modules for perception, planning, memory, and action execution, with some designs incorporating personalization so the agent can adapt to a user’s habits over time.

Performance and Limitations

Benchmark tests show that current systems perform well on simple, clearly defined actions but remain inconsistent when faced with complex, context-dependent tasks. Success rates vary widely depending on the platform and the type of task, with agents often struggling to adapt to unexpected changes in the interface. As a result, early deployments tend to focus on repetitive, high-volume activities where rules are predictable.

Security and Privacy Risks

While the potential for automation is considerable, the report stresses that these systems introduce an attack surface most organizations have yet to secure. Documented threats include prompt-injection techniques hidden in web pages, as well as environmental manipulation that can trick an agent into disclosing sensitive data or executing unauthorized actions.

Because OS agents operate with the access level of their host user, a compromised agent could move through corporate email, databases, and financial records without triggering the same warning signs that might alert a human. Existing AI security guidelines offer only partial coverage, and defenses tailored specifically to OS agents are still limited.

Personalization Challenges

The authors note that future OS agents will likely evolve from stateless tools into persistent digital assistants that learn from each interaction. This shift could improve efficiency but raises questions about how to store and process personal preferences without creating an exhaustive surveillance record of a user’s digital life.

Looking Ahead

The research concludes that OS agents remain in an early stage, yet progress is accelerating. Advancements in multimodal models, memory systems, and interface understanding are likely to close current performance gaps, but without equal attention to safety, privacy, and evaluation standards, deployment risks will grow alongside capabilities.

The team maintains an open-source repository to track new models, frameworks, and benchmarks, reflecting a field that is expanding at a pace unusual even for the technology sector. For now, the technology is moving toward the point where it can interact with digital environments much as a human user would, and that, the authors suggest, means the window for building adequate safeguards is already narrowing.

Notes: This post was edited/created using GenAI tools.

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by Asim BN via Digital Information World

Meta Expands Scam Ad Reporting Across Facebook and Instagram

Meta is giving businesses more tools to tackle fraudulent adverts, adding large-scale scam ad reporting to its Brand Rights Protection system.

The update means companies and brands can now flag suspicious campaigns even when the ads do not directly lift their logos or imagery. That covers cases where a brand’s name is used without permission or where false claims are tied to the brand in an attempt to mislead. The move follows a string of incidents in which prominent journalists, broadcasters, and entrepreneurs were impersonated in online investment schemes.

First launched in October 2021 and once known as the Commerce & Ads IP Tool, Brand Rights Protection lets registered trademark owners monitor and report misuse of their brands on Facebook and Instagram. It scans ads, posts, pages, and accounts for potential infringements, using image-matching technology to spot content that resembles uploaded reference files. Businesses can store up to 10 such images (logos, product shots, or other brand markers) to help the system flag suspicious material.

Meta has also reworked the tool’s navigation. The Drafts tab, previously called Requests, now splits reports into categories for copyright, counterfeit, impersonation, and trademark violations. In the Reports tab, users can filter results by keywords, trademark names, report owner details, or unique email report IDs. Those targeting scam ads specifically are advised to choose the “Other” violation type in the Ads section.

The system’s reach currently covers Facebook and Instagram, though scam activity has also been spotted on WhatsApp, where the same enforcement measures are not in place. Publishers and brand owners say that removing harmful ads can feel like an endless cycle, with new ones appearing soon after the old ones are removed.

Meta says its automated review and detection systems remove millions of fraudulent posts and accounts. In 2024 alone, the company took down more than 157 million pieces of advertising content worldwide for breaking its rules on fraud, scams, and deceptive business practices. The company believes that the expanded Brand Rights Protection features will give businesses more control over their identities online and help reduce the spread of misleading material.


Notes: This post was edited/created using GenAI tools.

Read next: Anthropic Expands Claude With On Demand Memory Retrieval for Subscribers
by Asim BN via Digital Information World

Anthropic Expands Claude With On Demand Memory Retrieval for Subscribers

Anthropic has introduced a memory tool for its Claude chatbot that can pull details from earlier conversations when a user asks. The update is being made available to subscribers on the Max, Team, and Enterprise plans, with wider access planned later.

The new option is intended for situations where someone needs to return to a task or review past research without going over old ground. When prompted, Claude searches earlier chats linked to the same workspace and project. It then returns a summary and offers to continue the work. The feature does not run automatically and can be switched off in the settings menu.

Anthropic’s design keeps the function limited in scope compared with memory systems from other AI providers. OpenAI’s ChatGPT, for example, stores all conversation history to shape later responses. Google’s Gemini can also draw on earlier discussions and, in some cases, search history. Claude’s feature instead focuses on retrieval at the user’s request, avoiding a constant record.
In a demonstration, Anthropic showed Claude reviewing a series of exchanges from before a user’s holiday, summarizing the progress, and offering to pick up the same project. The approach aims to keep context available while maintaining tighter control over stored information.

The rollout has started for eligible plans, with additional tiers expected to gain access in the coming months.

Claude Adds Privacy Focused Memory Retrieval to Support Ongoing Work

Notes: This post was edited/created using GenAI tools.

Read next: Mobile Devices Cement Lead in Global Web Traffic as Desktop Use Declines
by Irfan Ahmad via Digital Information World

Monday, August 11, 2025

Mobile Devices Cement Lead in Global Web Traffic as Desktop Use Declines

Over the past decade and a half, the balance of global web traffic has shifted dramatically from desktop computers toward mobile devices, with tablets playing a smaller but steady role. Data from January 2009 to July 2025 shows how smartphones have transformed the way people access the internet, overtaking desktop usage in late 2016 and maintaining a clear lead ever since.

In early 2009, desktop devices dominated almost all internet activity, holding 99.3% of global web traffic, while mobile accounted for just 0.7% and tablets registered no measurable share. The following years saw a gradual erosion of desktop’s lead as mobile adoption accelerated, crossing the 10% mark by mid-2012. That year also marked the arrival of tablets as a measurable traffic source, though their share never exceeded single digits.

Between 2013 and 2015, mobile traffic grew steadily from under 20% to nearly 40%, while desktop’s share fell below two-thirds. By October 2016, mobile usage finally edged past desktop for the first time, with tablets contributing just under 5%. The gap widened quickly in subsequent years, reflecting the rise of mobile-first browsing habits and wider smartphone penetration across both developed and emerging markets.

From 2017 through 2021, mobile consistently held above 50% of global traffic, peaking at nearly 57% in December 2020. Desktop usage during that period stayed in the low-to-mid-40% range, while tablets gradually slid from about 5% to under 3%.

The trend continued through the early 2020s, with mobile surpassing 60% of traffic in mid-2022 and hovering near that level for much of the following two years. Tablets saw a modest but persistent decline, settling around 2% of traffic. Desktop usage, meanwhile, stabilized at just under 40% during most of this period.

As of July 2025, mobile accounts for 60.5% of all web traffic worldwide, compared to 39.5% for desktop and 1.6% for tablets. While seasonal and short-term fluctuations occasionally narrow the gap, the long-term trajectory suggests mobile will remain the dominant platform for internet access in the foreseeable future.

Mobile Traffic Becomes Main Channel for Internet Use

Date (Year-Month) Desktop (%) Mobile (%) Tablet (%)
2009-01 99.33 0.67 0
2009-02 99.31 0.69 0
2009-03 99.2 0.8 0
2009-04 99.14 0.86 0
2009-05 99.14 0.86 0
2009-06 99.06 0.94 0
2009-07 98.95 1.05 0
2009-08 98.88 1.12 0
2009-09 98.88 1.12 0
2009-10 98.85 1.15 0
2009-11 98.79 1.21 0
2009-12 98.72 1.28 0
2010-01 98.44 1.56 0
2010-02 98.28 1.72 0
2010-03 98.04 1.96 0
2010-04 97.82 2.18 0
2010-05 97.68 2.32 0
2010-06 97.43 2.57 0
2010-07 97.14 2.86 0
2010-08 96.79 3.21 0
2010-09 96.5 3.5 0
2010-10 96.19 3.81 0
2010-11 95.98 4.02 0
2010-12 95.9 4.1 0
2011-01 95.7 4.3 0
2011-02 95.55 4.45 0
2011-03 95.3 4.7 0
2011-04 94.79 5.21 0
2011-05 94.25 5.75 0
2011-06 93.47 6.53 0
2011-07 92.98 7.02 0
2011-08 92.88 7.12 0
2011-09 93.26 6.74 0
2011-10 93.45 6.55 0
2011-11 93.05 6.95 0
2011-12 91.96 8.04 0
2012-01 91.51 8.49 0
2012-02 91.47 8.53 0
2012-03 91.01 8.99 0
2012-04 90.42 9.58 0
2012-05 89.89 10.11 0
2012-06 89.6 10.4 0
2012-07 88.91 11.09 0
2012-08 85.69 11.44 2.86
2012-09 85.33 11.66 3.01
2012-10 85.02 11.92 3.06
2012-11 84.13 12.66 3.22
2012-12 82.42 14.04 3.54
2013-01 82.41 13.56 4.03
2013-02 81.86 13.71 4.43
2013-03 81.69 13.79 4.52
2013-04 82.43 13.31 4.27
2013-05 81.68 13.98 4.33
2013-06 80.01 15.33 4.66
2013-07 78.66 16.51 4.83
2013-08 78.08 17.14 4.78
2013-09 78.39 16.98 4.63
2013-10 76.68 18.78 4.54
2013-11 76.13 19.08 4.78
2013-12 72.55 22.18 5.27
2014-01 71.95 22.39 5.66
2014-02 70.99 23.25 5.76
2014-03 70.23 23.93 5.84
2014-04 70.61 23.56 5.83
2014-05 68.55 25.41 6.04
2014-06 66.93 26.66 6.4
2014-07 65.83 27.51 6.66
2014-08 64.65 28.57 6.78
2014-09 63.84 29.36 6.8
2014-10 62.77 30.67 6.56
2014-11 61.92 31.54 6.54
2014-12 61.68 31.82 6.5
2015-01 62.38 31.06 6.55
2015-02 62.62 30.81 6.57
2015-03 62.26 31.58 6.17
2015-04 62.71 31.56 5.73
2015-05 61.49 32.82 5.69
2015-06 60.11 34.21 5.68
2015-07 57 37.15 5.85
2015-08 55.22 39.18 5.6
2015-09 55.91 38.78 5.31
2015-10 55.86 39.01 5.14
2015-11 57.21 37.62 5.17
2015-12 56.25 38.62 5.13
2016-01 55.86 38.88 5.26
2016-02 55.82 38.96 5.22
2016-03 54.19 40.6 5.2
2016-04 53.54 41.32 5.14
2016-05 51.46 43.5 5.04
2016-06 53.22 41.61 5.16
2016-07 50.1 44.75 5.15
2016-08 50.61 44.41 4.98
2016-09 50.28 44.91 4.81
2016-10 48.74 46.53 4.73
2016-11 46.93 48.25 4.82
2016-12 44.79 50.31 4.9
2017-01 45.27 49.6 5.13
2017-02 45.23 49.73 5.04
2017-03 44.36 50.75 4.88
2017-04 43.23 51.95 4.82
2017-05 43.59 51.7 4.71
2017-06 42.19 53.03 4.78
2017-07 41.22 53.99 4.79
2017-08 42.75 52.64 4.62
2017-09 43.29 52.29 4.42
2017-10 44.78 50.87 4.35
2017-11 45.68 50.02 4.3
2017-12 43.26 52.48 4.26
2018-01 43.87 51.92 4.21
2018-02 44.12 51.82 4.06
2018-03 44.27 51.56 4.18
2018-04 44.66 51.2 4.14
2018-05 44.1 52 3.89
2018-06 43.63 52.52 3.85
2018-07 43.11 52.95 3.94
2018-08 43.33 52.54 4.13
2018-09 44.12 51.7 4.18
2018-10 47.78 48.2 4.03
2018-11 52.07 44.19 3.74
2018-12 47.2 49.06 3.74
2019-01 47.02 49.11 3.87
2019-02 48.21 47.96 3.83
2019-03 47.04 48.98 3.98
2019-04 47.79 48.32 3.9
2019-05 48.27 47.9 3.84
2019-06 45.53 50.71 3.76
2019-07 45.18 51.11 3.71
2019-08 44.6 51.65 3.75
2019-09 44.57 51.78 3.65
2019-10 44.59 52.48 2.93
2019-11 45.17 52.03 2.8
2019-12 43.99 53.29 2.72
2020-01 45.29 52.02 2.7
2020-02 45.66 51.69 2.65
2020-03 45.32 52.03 2.65
2020-04 43.27 53.81 2.92
2020-05 46.51 50.48 3
2020-06 47.06 50.13 2.81
2020-07 46.39 50.88 2.74
2020-08 45.9 51.33 2.78
2020-09 47.17 50.21 2.62
2020-10 48.88 48.62 2.5
2020-11 44.22 52.95 2.83
2020-12 41.46 55.73 2.81
2021-01 41.45 55.68 2.87
2021-02 42.63 54.46 2.91
2021-03 42.93 54.22 2.85
2021-04 42.66 54.57 2.76
2021-05 41.98 55.3 2.72
2021-06 41.96 55.4 2.64
2021-07 41.36 55.89 2.74
2021-08 40.39 56.86 2.75
2021-09 42.87 54.61 2.52
2021-10 43.15 54.37 2.48
2021-11 43.55 53.98 2.48
2021-12 42.65 54.86 2.49
2022-01 42.54 54.98 2.47
2022-02 41.59 55.98 2.43
2022-03 41.15 56.45 2.4
2022-04 39.37 58.16 2.47
2022-05 38.65 59.02 2.33
2022-06 37.99 59.74 2.27
2022-07 37 60.73 2.27
2022-08 38.53 59.25 2.22
2022-09 39.27 58.64 2.09
2022-10 39.72 58.27 2.02
2022-11 39 59.02 1.98
2022-12 37.71 60.29 2
2023-01 39.41 58.52 2.07
2023-02 38.6 59.36 2.04
2023-03 40.8 57.18 2.02
2023-04 44.4 53.61 1.99
2023-05 47.41 50.71 1.88
2023-06 42.2 55.87 1.92
2023-07 42.4 55.67 1.93
2023-08 43.72 54.41 1.87
2023-09 45.13 53.03 1.84
2023-10 45.54 52.69 1.77
2023-11 44.52 53.66 1.82
2023-12 40.08 57.97 1.95
2024-01 39.76 58.21 2.03
2024-02 38 59.9 2.11
2024-03 37.8 60.01 2.2
2024-04 38.41 59.57 2.02
2024-05 37.85 60.08 2.07
2024-06 37.1 60.74 2.17
2024-07 36.1 61.74 2.16
2024-08 35.72 62.15 2.13
2024-09 36.36 61.66 1.98
2024-10 36.56 61.59 1.85
2024-11 35.2 62.96 1.84
2024-12 35.07 63.07 1.86
2025-01 35.43 62.69 1.88
2025-02 35.91 62.23 1.85
2025-03 36.06 62.21 1.72
2025-04 36.3 62.01 1.7
2025-05 35.28 63.07 1.65
2025-06 35.24 63.13 1.63
2025-07 40.07 58.36 1.57

Notes: This post was edited/created using GenAI tools.

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by Asim BN via Digital Information World

Google Fixes Flaw That Let Hackers Control AI Assistant Through Calendar Invites

Google has patched a security vulnerability that allowed hackers to remotely hijack its Gemini AI assistant by sending malicious calendar invitations to victims.

The flaw enabled attackers to access emails, control smart home devices and track user locations without requiring any interaction from victims beyond normal use of the assistant.

Researchers at SafeBreach Labs discovered the vulnerability works by embedding harmful commands in Google Calendar event titles. When users ask Gemini about their schedule, the assistant processes these hidden instructions as legitimate requests.

The bug affected all versions of Gemini, including web browsers, mobile applications and Android voice assistants connected to Google Workspace. Hackers could exploit the AI's permissions to access Gmail, Calendar and connected home devices.

Google says no exploitation occurred before the company implemented fixes.

Attack required basic skills

The vulnerability bypassed existing security measures and required no advanced technical knowledge. Researchers demonstrated successful attacks using standard calendar features available to any Google user.

Attackers could send up to six calendar invitations to maintain stealth, hiding malicious commands in the final invitation. Google Calendar displays only five recent events directly, concealing additional entries behind a "Show more" button that Gemini still processes during queries.


The attack method exploited "context poisoning," where hidden commands become part of Gemini's conversation history. This causes the AI to follow hostile instructions while users remain unaware of any compromise.

SafeBreach researchers demonstrated multiple attack capabilities during their investigation. These included triggering spam campaigns, generating inappropriate content and remotely deleting victim calendar entries.

More serious capabilities involved controlling smart home devices through Google Home integration. Researchers successfully opened windows, adjusted heating systems and controlled lighting by exploiting the assistant's connections to internet-enabled devices.

Privacy and security risks

The vulnerability enabled location tracking through forced website visits that captured victim IP addresses. Attackers could also initiate unauthorized video calls, potentially enabling surveillance through device cameras and microphones.

Data theft represented a significant risk. The flaw allowed extraction of email content and calendar information through specially crafted web addresses that transmitted sensitive data to attacker-controlled servers.

Mobile versions faced additional exposure due to Gemini's integration with Android system functions. This connection allowed manipulation of phone features including application launches, screenshot capture and media controls.

Researchers bypassed URL security restrictions using redirect services that forced Chrome to open malicious websites. This worked because Gemini automatically followed redirects without displaying security warnings normally shown in browser sessions.

The attack also supported "delayed execution" where malicious instructions activated during future user interactions. This persistence allowed attackers to maintain access across multiple Gemini sessions.

High risk rating

The research team developed a threat analysis framework specifically for AI-powered applications. Their evaluation assessed attack feasibility, required expertise and potential damage across privacy, financial, safety and operational categories.

Results classified 73% of identified threats as high or critical risk, requiring immediate remediation. The assessment found these attacks require significantly less technical skill than traditional cyber threats while potentially causing greater harm.

The vulnerability demonstrated movement between different Gemini functions and extension beyond application boundaries to manipulate external systems outside Google's direct control.

Google addressed the reported vulnerabilities before any documented exploitation occurred. The company implemented enhanced user confirmation requirements for sensitive actions and improved web address handling with validation protocols.

Advanced detection systems now employ content analysis algorithms to identify malicious instructions. These protections underwent extensive internal testing before deployment to all Gemini users.

Andy Wen, senior director of security product management for Google Workspace, acknowledged the researchers' responsible disclosure approach and said it accelerated deployment of new protective measures.

Wider implications

The discovery highlights security challenges facing artificial intelligence integration across digital services. Traditional cybersecurity approaches targeting software bugs may prove insufficient for AI-integrated systems.

Security specialists anticipate shifts in application attack methods as AI adoption increases. These new threats target reasoning processes rather than code vulnerabilities, representing a fundamental change in attack methodology.

User trust in AI assistants compounds the security risk since people typically accept system recommendations without questioning potential external manipulation.

SafeBreach researchers Or Yair, Ben Nassi and Stav Cohen notified Google in February 2025 following responsible disclosure procedures. The team provided detailed technical documentation and collaborated during the remediation process.

The findings were presented at Black Hat USA and DEF CON 33 security conferences. Complete research documentation enables organizations to assess similar risks in their AI-powered systems.

Future threats may include attacks requiring no user interaction and methods targeting multiple users simultaneously through public platforms.

Read next: Man Develops Rare Bromide Poisoning After Following AI Diet Suggestion


by Asim BN via Digital Information World

Man Develops Rare Bromide Poisoning After Following AI Diet Suggestion

A 60-year-old man was hospitalized with a rare condition linked to bromide toxicity after acting on advice from an artificial intelligence chatbot. The case was described in the Annals of Internal Medicine: Clinical Cases and shows how online information can influence unusual health outcomes.

The patient, who had no prior psychiatric history, decided to remove sodium chloride from his diet after reading about its health effects. He found information about reducing salt intake but little about removing it entirely. Drawing on nutrition courses he had taken years earlier, he replaced table salt with sodium bromide bought online. The decision followed a chatbot exchange that suggested bromide as an alternative, a substance generally used in industrial or cleaning applications rather than food.

For three months, he maintained this substitution while following a highly restrictive vegetarian diet and distilling his own drinking water. Over time, he developed fatigue, insomnia, increased thirst, movement imbalance, and skin changes such as acne and small red growths. He also began to suspect that his neighbor was poisoning him.

When he arrived at the emergency department, his vital signs and neurological examination were normal. Blood tests, however, showed unusually high chloride levels, a negative anion gap, low phosphate, and signs of both respiratory acidosis and metabolic alkalosis. Poison Control staff considered bromism the most likely cause.

Bromism was common in the early 20th century when bromide salts were used in sedatives and other over-the-counter products. Its incidence declined after U.S. regulators banned such uses between the 1970s and late 1980s. In this case, the man’s bromide level measured more than 200 times the upper limit of normal.

Within the first day of his admission, he developed hallucinations and worsening paranoia, leading to an involuntary psychiatric hold. He was treated with antipsychotic medication, intravenous fluids, and electrolyte replacement. He also received vitamins to address deficiencies from his restrictive diet.

His condition improved gradually during a three-week hospital stay. Blood chemistry returned to normal and psychiatric symptoms resolved. He was discharged without medication and remained stable at follow-up.

The report’s authors noted that bromide-containing products remain available through online sellers. They advised that unusual electrolyte results, especially high chloride with a negative anion gap, should prompt clinicians to consider bromism in patients with unexplained psychiatric or neurological symptoms.

Notes: This post was edited/created using GenAI tools. Image: DIW-Aigen.

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• Office, Retail, and Support Jobs Projected to Shrink by 2030

• YouTube CPM Rates in 2025: How Location Shapes Earnings
by Irfan Ahmad via Digital Information World

Sunday, August 10, 2025

YouTube CPM Rates in 2025: How Location Shapes Earnings

YouTube advertising payouts in 2025 look very different depending on where viewers are watching. Median CPM (cost per mille, or cost per thousand impressions) figures from 50 countries put the global midpoint at $2.91 per thousand ad views. The United States comes in at $11.95, the highest in the dataset. Pakistan is at the other end of the scale with $0.42. Geography still plays a big part in deciding what a creator can make from their audience.

Where the Highest and Lowest Rates Fall

Top earnings are concentrated in North America, Western Europe, and parts of Oceania. The United States sits in first place. Australia follows at $8.93, then Norway at $8.19. Switzerland, the United Kingdom, and Denmark each post more than $7.00, suggesting strong advertiser competition in those markets.

Several Asian and African countries remain well below the global average. Pakistan, Bangladesh, and Egypt all report less than $0.55. India, Vietnam, and Indonesia stay under $1.00. These figures reflect smaller ad budgets, lower spending power, and frequent use of ad blockers.

Latin America and Eastern Europe are more mixed. Mexico and Brazil record between $1.30 and $1.64. Poland and Czechia are near the midpoint. Romania, Croatia, and Bulgaria fall in the $1.75 to $1.94 range, showing steady but limited advertiser activity.

YouTube Ad Rates in 2025 Show Wide Global Gaps Between Countries
Country Name CPM median (USD)
United States of America $11.95
Australia $8.93
Norway $8.19
Switzerland $8.02
United Kingdom $7.60
Denmark $7.43
New Zealand $6.72
Canada $6.65
Belgium $6.52
Netherlands $6.44
Germany $6.43
Sweden $6.30
Austria $4.95
Finland $4.77
France $4.54
Ireland $4.29
Singapore $3.62
Italy $3.59
Japan $3.41
Hong Kong $3.31
Czechia $3.20
Spain $3.14
United Arab Emirates $3.03
Poland $2.94
Israel $2.91
Republic of Korea $2.91
Portugal $2.78
Greece $2.33
Saudi Arabia $2.18
Hungary $2.10
NaN $2.08
Croatia $1.94
Romania $1.94
Bulgaria $1.75
South Africa $1.69
Mexico $1.64
Brazil $1.33
Malaysia $1.31
Serbia $1.28
Philippines $1.22
Thailand $1.22
India $0.96
Turkey $0.87
Indonesia $0.87
Vietnam $0.82
Sri Lanka $0.63
Nepal $0.58
Egypt $0.53
Bangladesh $0.52
Pakistan $0.42

How the Numbers Were Collected

The data, complied by IsThisChannelMonetized, comes from YouTube analytics on videos uploaded during 2024. Countries with fewer than 1,000 views were left out. Any entries showing no revenue or no views were removed. Median values were used to avoid skew from unusual spikes or drops, giving a more stable picture of the market.

CPM and RPM in Brief

CPM means the cost advertisers pay for every 1,000 ad impressions. RPM is what the creator actually earns for the same number of views after YouTube’s 45 percent cut. CPM will always be higher. Both figures depend on who’s watching, where they’re located, the topic of the content, and how many advertisers are bidding in that niche.

Rates by Content Type

The subject of a video can have as much impact on income as the viewer’s location. Based on USD estimates:
  • Shorts: $0.02 to $0.15 CPM
  • Entertainment and lifestyle: $1.36 to $1.82 CPM, RPM under $1.00
  • Gaming: $4.55 CPM, $2.50 RPM
  • Education: $9.09 CPM, $5.00 RPM
  • Finance and digital marketing: $14.55 to $36.36 CPM, with higher RPMs

Why Some Views Earn Nothing

Not all views lead to revenue. Ads might be blocked, rights holders could claim the income, or YouTube may run ads without sharing earnings if a channel is outside the Partner Program.

Ways to Lift RPM

Creators can’t control every factor, but they can:
  • Make videos longer than eight minutes to allow mid-roll ads
  • Target audiences in countries with stronger CPM rates
  • Focus on topics that attract high-value advertisers
  • Keep viewers watching for longer periods

The Takeaway

The 2025 CPM rankings show a widening gap between mature ad markets and emerging economies. Where an audience lives and the kind of content produced remain the biggest drivers of revenue. Growth depends on consistent quality and keeping viewers engaged over time.

Notes: This post was edited/created using GenAI tools.

Read next: Office, Retail, and Support Jobs Projected to Shrink by 2030

by Irfan Ahmad via Digital Information World