Many internet users turn to adblockers hoping to improve their browsing experience and guard their privacy. Yet a recent study, titled "Sheep’s clothing, wolfish impact: Automated detection and
evaluation of problematic ‘allowed’ advertisements" suggests that one of the most widely used adblockers, Adblock Plus, might be undercutting those goals in ways most users don’t see coming.
Researchers from New York University examined how “Acceptable Ads,” a default feature in Adblock Plus, influences what users see online. The feature allows certain ads through, based on cosmetic standards designed to make them less annoying. But instead of improving user experience, the study found that these “allowed” ads often introduce more harmful content than ads seen without any adblocker at all.
The analysis pulled data from over 18,000 ads, collected using a simulated browsing environment that mimicked different countries and age groups. The findings weren’t subtle. Users running Adblock Plus with Acceptable Ads enabled were shown 13.6% more problematic ads compared to those browsing with no adblocker. That gap widened sharply for children and teens. For under-18 profiles, exposure to misleading or inappropriate ads rose by nearly 22%.
These ads weren’t just irritating. Many made exaggerated claims about health or finances, misused political messaging, or included manipulative buttons that tricked users into clicking. Some even bypassed age restrictions, exposing minors to ads about gambling or cannabis without any warnings.
But the issue runs deeper than misleading ads. The study revealed that the very structure of Acceptable Ads, which relies on curated allow lists of ad exchanges, may be creating blind spots. Certain ad exchanges that remain unblocked by the filter show a marked increase in questionable content when Acceptable Ads are enabled. This shift raises concerns that some exchanges may be tailoring ad delivery based on whether users have privacy tools installed.
To investigate further, the researchers tested how different ad networks behaved when Acceptable Ads were active. They found that while new exchanges added through the allowlist sometimes improved ad quality, long-standing ones were more likely to deliver problematic content under those same conditions. That difference hints at a troubling possibility: privacy-focused users may be singled out for lower-quality ads, and possibly targeted in ways they don’t expect.
To sort through the complexity, the team designed a new classification system for ad quality, then used a small, multimodal language model to automate part of the analysis. The model’s assessments were closely aligned with those of human reviewers, showing that smart automation can help flag suspicious content at scale. Still, researchers noted that human judgment remained vital in spotting nuance, especially when ads pushed the line without technically violating rules.
Even as automation improves, the researchers argue, the industry needs to rethink how ad filtering is done. Cosmetic rules (like ad placement or size) aren’t enough to protect users from misleading or harmful content. If adblockers rely on visuals while ignoring the messaging, users might be left with a cleaner-looking page that still carries deceptive or offensive material.
This concern becomes more urgent when considering how fingerprinting works. Ad content itself can leak clues about the user’s browser setup. By analyzing which ads are delivered and how, bad actors could infer whether someone uses an adblocker like ABP. That data could then feed into tracking systems, the very thing privacy tools are supposed to prevent.
There’s also a risk of backlash. If users feel betrayed by tools meant to protect them, they may abandon those tools altogether. That would further tilt the balance in favor of ad platforms and tracking systems already optimized for data extraction.
One potential path forward lies in smarter filtering, not just blocking ad formats, but evaluating ad content using modern tools like language models. These systems, if applied carefully, could detect patterns of manipulation, misinformation, or bias more effectively than cosmetic filters ever could. Still, any such solution must tread carefully to avoid new risks, like reinforcing profiling or creating opaque review systems.
The study leaves little doubt: making ads look cleaner isn’t enough. Unless privacy tools start paying attention to the actual content of the ads, not just where they appear or how big they are, users may end up with a false sense of safety. What’s said in an ad matters just as much as how it’s delivered, and ignoring that could leave people exposed despite their best efforts to stay protected.
For now, users hoping to stay safe online may need to think twice before assuming their adblocker has them covered.
Notes: This post was edited/created using GenAI tools. Image: DIW-Aigen.
Read next: Instagram Users Face Account Bans Over False Child Safety Flags
evaluation of problematic ‘allowed’ advertisements" suggests that one of the most widely used adblockers, Adblock Plus, might be undercutting those goals in ways most users don’t see coming.
Researchers from New York University examined how “Acceptable Ads,” a default feature in Adblock Plus, influences what users see online. The feature allows certain ads through, based on cosmetic standards designed to make them less annoying. But instead of improving user experience, the study found that these “allowed” ads often introduce more harmful content than ads seen without any adblocker at all.
The analysis pulled data from over 18,000 ads, collected using a simulated browsing environment that mimicked different countries and age groups. The findings weren’t subtle. Users running Adblock Plus with Acceptable Ads enabled were shown 13.6% more problematic ads compared to those browsing with no adblocker. That gap widened sharply for children and teens. For under-18 profiles, exposure to misleading or inappropriate ads rose by nearly 22%.
These ads weren’t just irritating. Many made exaggerated claims about health or finances, misused political messaging, or included manipulative buttons that tricked users into clicking. Some even bypassed age restrictions, exposing minors to ads about gambling or cannabis without any warnings.
But the issue runs deeper than misleading ads. The study revealed that the very structure of Acceptable Ads, which relies on curated allow lists of ad exchanges, may be creating blind spots. Certain ad exchanges that remain unblocked by the filter show a marked increase in questionable content when Acceptable Ads are enabled. This shift raises concerns that some exchanges may be tailoring ad delivery based on whether users have privacy tools installed.
To investigate further, the researchers tested how different ad networks behaved when Acceptable Ads were active. They found that while new exchanges added through the allowlist sometimes improved ad quality, long-standing ones were more likely to deliver problematic content under those same conditions. That difference hints at a troubling possibility: privacy-focused users may be singled out for lower-quality ads, and possibly targeted in ways they don’t expect.
To sort through the complexity, the team designed a new classification system for ad quality, then used a small, multimodal language model to automate part of the analysis. The model’s assessments were closely aligned with those of human reviewers, showing that smart automation can help flag suspicious content at scale. Still, researchers noted that human judgment remained vital in spotting nuance, especially when ads pushed the line without technically violating rules.
Even as automation improves, the researchers argue, the industry needs to rethink how ad filtering is done. Cosmetic rules (like ad placement or size) aren’t enough to protect users from misleading or harmful content. If adblockers rely on visuals while ignoring the messaging, users might be left with a cleaner-looking page that still carries deceptive or offensive material.
This concern becomes more urgent when considering how fingerprinting works. Ad content itself can leak clues about the user’s browser setup. By analyzing which ads are delivered and how, bad actors could infer whether someone uses an adblocker like ABP. That data could then feed into tracking systems, the very thing privacy tools are supposed to prevent.
What This Means for the Broader Ad Tech Landscape
The Adblock Plus findings land at a time when digital advertising continues to evolve, and not always for the better. As advertisers turn to artificial intelligence and automation to deliver targeted messages, the margin for abuse grows. Content filters based on layout or formatting simply can’t keep up with the subtleties of language, emotion, or manipulation.There’s also a risk of backlash. If users feel betrayed by tools meant to protect them, they may abandon those tools altogether. That would further tilt the balance in favor of ad platforms and tracking systems already optimized for data extraction.
One potential path forward lies in smarter filtering, not just blocking ad formats, but evaluating ad content using modern tools like language models. These systems, if applied carefully, could detect patterns of manipulation, misinformation, or bias more effectively than cosmetic filters ever could. Still, any such solution must tread carefully to avoid new risks, like reinforcing profiling or creating opaque review systems.
The study leaves little doubt: making ads look cleaner isn’t enough. Unless privacy tools start paying attention to the actual content of the ads, not just where they appear or how big they are, users may end up with a false sense of safety. What’s said in an ad matters just as much as how it’s delivered, and ignoring that could leave people exposed despite their best efforts to stay protected.
For now, users hoping to stay safe online may need to think twice before assuming their adblocker has them covered.
Notes: This post was edited/created using GenAI tools. Image: DIW-Aigen.
Read next: Instagram Users Face Account Bans Over False Child Safety Flags
