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Not reliably on its own. CAPTCHA can slow basic automation and add friction, but modern vision-language models and paid solving services can get through many visual challenges. For a website owner, the practical answer is to use challenges as one risk signal within a layered defense—not as proof that a visitor is human or as the only barrier protecting a login, signup, or other sensitive action.
Can AI bots solve CAPTCHA?
Yes. Some AI systems can interpret images, follow challenge instructions, and submit answers; commercial solving services can also route challenges to automated systems or human workers. That does not mean every bot can solve every CAPTCHA, or that every challenge is equally weak. It means that passing one is not a dependable, universal test of human identity.
The strongest evidence is specific to the challenges and methods tested. At USENIX Security 2025, researchers reported that the Halligan agentic vision-language solver solved 60.7% of 2,600 challenges spanning 26 visual CAPTCHA types. In a separate 30-day evaluation on previously unseen, in-the-wild challenges, its success rate was 70.6%. Those results show that a capable system can generalize beyond a fixed set of examples; they do not establish the success rate for every CAPTCHA provider, configuration, attacker, or browser environment.
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For a defender, the useful interpretation is not “CAPTCHA is useless.” A challenge can still increase an attacker’s cost, interrupt simple scripts, and contribute information to a risk decision. The mistake is treating a correct answer as conclusive evidence that a request is benign.
What do the results say about CAPTCHA’s limits?
Challenge resistance depends on the task, the solver, and the conditions around the request. A benchmark result is evidence about the tested setup, not a permanent ranking of every CAPTCHA or a guarantee that an attack will succeed or fail on your site.
| Evidence | Reported result | What it supports |
|---|---|---|
| Halligan visual solver, USENIX Security 2025 | 60.7% across 2,600 challenges and 26 visual CAPTCHA types | A capable vision-language agent solved a broad set of tested visual tasks. |
| Halligan on previously unseen, in-the-wild challenges, over 30 days | 70.6% | Performance was not limited to the solver’s tested challenge set; the result remains specific to that evaluation. |
| Text-CAPTCHA evaluation, WWW 2024 | 80% success across several schemes with minimal training; 20% one-attempt success by a pretrained model without human intervention | Text-based tasks can also be vulnerable, including to models that do not rely on repeated attempts. |
| Commercial solving services, Broken Gates measurement study, 2026 | Near-perfect bypass was reported for the services measured; non-interactive reCAPTCHA v3 performed better in some settings | Results varied with execution-environment authenticity. The study does not establish a universal rate for all services or deployments. |
| COGNITION, USENIX Security 2026 | A hardened proof of concept reduced solver success from over 95% to 0% | Task design affected measured performance, but this proof of concept is not evidence that a deployed challenge is permanently AI-proof. |
COGNITION found a difference between kinds of visual work. Recognition-oriented, low-interaction tasks were easier for multimodal large language models; fine-grained localization, multi-step spatial reasoning, and consistency across video frames were harder in the tested setting. This is a design finding, not a reason to assume that adding complexity will keep a challenge secure indefinitely. Attackers can change models, use different tools, or outsource the task.
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Human solving services complicate the picture further: even a challenge that confuses a particular model may be passed by a person working at scale. OWASP cautions that visible challenges can be outsourced to human solver farms for very small amounts per solve. So a CAPTCHA that costs an attacker more time or money may still be economically viable when the target is valuable or the abuse volume is high.
Does reCAPTCHA stop AI agents?
It can make abuse harder in some circumstances, but it should not be treated as a reliable stand-alone stop against AI agents. Outcomes depend on which version and configuration is used, the action being protected, and whether the request environment looks authentic.
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The 2026 Broken Gates measurement study reported near-perfect bypass by the commercial solving services it measured. It also found that non-interactive reCAPTCHA v3 performed better in some settings, with results varying according to execution-environment authenticity. This matters because a challenge or risk score is not independent of the context in which a request is made. A real-looking browser session, an automated environment, and a service that delegates solving can produce different outcomes.
A risk score is better understood as an input to a policy than a verdict about a person. If the score or challenge result is favorable, a site may decide to allow a low-risk action, monitor it, or require another check for a sensitive action. If a request looks risky, the response can be a step-up challenge, a temporary limit, or stronger authentication. The exact decision should reflect what an attacker could do if the action succeeds.
CAPTCHA does not prove that the user is human, authorized, or acting in good faith. A person can abuse an account, and an automated agent can complete a challenge. Keep controls on the protected action itself rather than assuming the challenge has transferred all risk away.
What should I use instead of CAPTCHA?
Use controls that address different parts of the attack, and reserve a visible challenge for cases where other signals justify extra friction. OWASP’s Bot Management and Anti-Automation Cheat Sheet recommends considering managed challenges, invisible risk scoring, proof of work where suitable, rate limits, and WebAuthn or passkeys for high-value flows. It describes visible CAPTCHAs as an accessibility-hostile, machine-solvable last-resort step-up—not a primary defense.
For public, low-risk pages
Start with monitoring and proportionate limits rather than forcing every visitor through an image puzzle. A managed challenge or invisible risk score can be used to decide when additional friction is warranted. Cloudflare documents a bot-management approach that combines heuristic, JavaScript, behavioral, and machine-learning engines to produce a score from 1 to 99, with models retrained as attack patterns change. That is an example of a signal-based approach; a score is not a guarantee of correctness and still needs an application policy behind it.
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For signup and other abuse-prone forms
Apply rate limits to the action and the relevant account or network signals, then monitor outcomes after submission. A challenge may be one escalation for suspicious traffic, but avoid making a successful answer the only condition for accepting repeated signups or other costly actions. Set the policy according to the harm: the appropriate threshold for a public newsletter form is not necessarily appropriate for issuing credits, creating financial exposure, or provisioning expensive resources.
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Protect authentication with controls tied to the account and the consequences of a successful attempt. For high-value flows, OWASP points to WebAuthn and passkeys as stronger authentication options than relying on a visible puzzle. Rate limits and abuse monitoring should remain in place even when a challenge is passed; a CAPTCHA does not validate the credentials, establish account ownership, or make password attacks harmless.
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Consider a step-up requirement when risk is elevated, and decide what evidence is appropriate for that action. Proof of work may be suitable in some situations, but it is not a universal substitute: any added computational burden also affects legitimate users and devices. Where a flow has meaningful consequences, choose authentication and server-side policy controls that directly address those consequences instead of expecting a generic puzzle to do the job.
How do I stop AI bots from abusing a login or signup form?
Build defenses around the request and the protected action, not just the appearance of the challenge. A practical design is to assess risk, apply limits, challenge selectively, validate the result on the server, and keep observing behavior after access is granted.
- Identify the abuse you need to prevent. Separate password guessing, account creation at scale, credential stuffing, recovery abuse, and resource-intensive submissions. Their costs and likely signals differ, so one blanket CAPTCHA rule can be both too permissive and unnecessarily difficult for ordinary users.
- Choose controls for each flow. Use rate limits and monitoring for repeated attempts, risk scoring or managed challenges when traffic appears suspicious, and stronger authentication for valuable accounts or sensitive operations. Make the escalation proportionate to the action’s impact.
- Make the server the enforcement point. The application should validate the challenge or risk result before performing the protected action. Do not rely on a client-side display, a hidden field, or a browser’s claim that a challenge passed as sufficient authorization.
- Keep limits after a pass. A successful solve should not remove safeguards on subsequent login attempts, signups, or submissions. Continue monitoring and rate-limiting the action so a solver service cannot turn one pass into unlimited use.
- Review both missed attacks and blocked users. Watch for abuse that gets through as well as legitimate traffic that is challenged or denied. Adjust the policy when the signals show that it is too permissive or creating excessive friction.
- Provide a recovery path. If a user cannot complete a challenge or is incorrectly flagged, offer an accessible alternative appropriate to the flow. A security measure that blocks legitimate users without a way forward creates an operational problem, not just a usability one.
The available evidence does not prescribe a universal threshold, a specific rate limit, or a single configuration that fits every site. Those values depend on traffic, account value, abuse patterns, and the consequences of a false allow or false block. Treat the controls as an operating policy that needs review, rather than as a one-time CAPTCHA installation.
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How do security, accessibility, and false positives compare?
No single control is best on every dimension. CAPTCHA adds friction to users and may exclude people who cannot perceive or interact with a particular task; invisible scoring reduces routine interruptions but can misclassify requests; authentication strengthens proof of account control but may be excessive for anonymous, low-impact actions.
| Control | Useful role | Trade-off to evaluate |
|---|---|---|
| Visible CAPTCHA | Step-up friction for selected suspicious requests | Can be inaccessible, machine-solvable, or outsourced; a solve is not proof of benign intent. |
| Managed challenge or invisible risk scoring | Apply friction based on assessed request risk | Scoring can produce false positives and false negatives; assess what data is collected and how the site acts on the result. |
| Rate limits and abuse monitoring | Constrain repeated activity and reveal patterns over time | Limits need to fit the flow; overly broad rules can affect legitimate users, while weak rules may not contain abuse. |
| Proof of work | Add a computational cost where that trade-off fits | Imposes work on legitimate clients too and is not appropriate for every device or action. |
| WebAuthn or passkeys | Strengthen authentication for high-value flows | Best suited to proving control of an authenticator in an account flow, not to screening every anonymous page request. |
Privacy is part of the decision. Risk systems may use signals about the browser or request environment, and their data collection and retention should be reviewed against the site’s obligations and user expectations. The available materials establish that Cloudflare’s documented system uses multiple detection engines and a 1–99 score, but they do not provide a comparable privacy specification for every product or provider. Review each provider’s own documentation and your deployment configuration before selecting a service.
False positives and false negatives are not abstract metrics: the first can block a real person from using an account, while the second can let abuse continue. Monitor both outcomes where possible, and set a less disruptive response for uncertain traffic than a permanent denial. For example, an elevated risk signal may justify another verification step for a sensitive action without blocking access to ordinary public content.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How widespread is automated traffic?
Bot traffic is a significant operational concern, but the headline figures need their original scope. Cloudflare reported in 2024 that 31.2% of application traffic was bot traffic, and that 93% of the bot traffic it observed was outside its verified-bot list and potentially malicious.
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Those figures describe Cloudflare’s reported application-traffic measurements, not a universal estimate for every site or a claim that every unverified bot was confirmed malicious. They do help explain why a static challenge at one endpoint is an incomplete strategy: automated traffic is common, can serve different purposes, and changes as defenses change. A site needs controls that continue to work after a challenge is solved.
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When is CAPTCHA still worth using?
Use a visible CAPTCHA when added friction is justified for a specific risky request and other safeguards remain in place. It is a poor default for every visitor and a weak sole barrier for valuable accounts or actions.
- It may be useful as a selective step-up when a risk policy identifies suspicious activity and the action can tolerate extra user effort.
- It is a poor fit when accessibility, conversion, or account access would be harmed by presenting a challenge to everyone.
- It is not enough when the protected action has high value and the site has no meaningful rate limits, authentication, or post-challenge monitoring.
- It should be reevaluated when attack patterns change, legitimate users report difficulty, or monitoring shows that abuse continues despite successful solves.
Challenge design can change the effort required from a solver. COGNITION’s findings suggest that fine-grained localization, multi-step spatial reasoning, and cross-frame consistency were more difficult than simple recognition tasks in its evaluations. Its hardened proof of concept reduced success from over 95% to 0%, but that result belongs to that proof of concept and test setup—not to a general-purpose CAPTCHA guarantee. Any design advantage may erode as models, tools, and solving strategies evolve.
The evidence also cautions against equating a low score from one detector with an accurate account of all abuse. In a 2024 study by Christopher Rodriguez and Daniel M. Oppenheimer, hand-coded free-response screeners identified 18.9% of the study sample as potential bots, compared with 1.7% identified by Google reCAPTCHA v3. That comparison is specific to the study’s sample and methods; it does not establish which screening method is best for a website login or signup flow. It does illustrate that different detection approaches can classify the same general problem differently.
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What is the safest way to think about a successful challenge?
Think of it as one piece of evidence used by a configured risk policy. It can justify a decision to proceed with a limited action, but it cannot establish that a requester is human, trustworthy, or authorized. Keep server-side validation, rate limits, authentication appropriate to the action, and abuse monitoring in the design even when the challenge succeeds.
Frequently Asked Questions
Can AI solve image CAPTCHA without a human?
Yes. The WWW 2024 study reported that a pretrained model achieved 20% one-attempt success on the evaluated text-CAPTCHA schemes without human intervention. That figure is limited to the study’s models and challenges; it is not a universal rate for current services.
Is CAPTCHA accessible to people with disabilities?
Not consistently: visual puzzles can be inaccessible to people who cannot see or interpret the images, and interaction-based tasks can create other barriers. OWASP explicitly warns that visible CAPTCHAs are accessibility-hostile, so provide an accessible alternative rather than making a puzzle the only route through a critical flow.
Can CAPTCHA stop credential stuffing?
A challenge can add friction to some automated login attempts, but it does not validate the credentials or prove that a login attempt is legitimate. Combine it with controls specific to authentication, including appropriate rate limits, monitoring, and stronger account authentication for higher-risk use.
Why do CAPTCHA results vary between websites?
Challenge configuration, attacker tooling, solver availability, and the authenticity of the browser environment can all affect outcomes. The 2026 Broken Gates study specifically reported that measured performance varied with execution-environment authenticity, so results from one deployment should not be generalized to every site.
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