Stories with Tag CAPTCHA

• Solve the hCaptcha challenge with multimodal large language model

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  Posted: 2025-04-03 13:03:02

  Verbose tl;dr

  A Hacker News post describes a method for solving hCaptcha challenges using a multimodal large language model (MLLM). The approach involves feeding the challenge image and prompt text to the MLLM, which then selects the correct images based on its understanding of both the visual and textual information. This technique demonstrates the potential of MLLMs to bypass security measures designed to differentiate humans from bots, raising concerns about the future effectiveness of such CAPTCHA systems.

  A Hacker News post titled "Solve the hCaptcha challenge with multimodal large language model" describes a novel approach to bypassing hCaptcha, a popular CAPTCHA service used to distinguish humans from bots online. The author details their experiment utilizing a large multimodal language model (LLM) to solve the visual challenges presented by hCaptcha. The core of the experiment involves prompting the LLM with the image presented in the CAPTCHA, which typically contains a grid of images and a textual prompt asking the user to select all images that match a specific criteria (e.g., select all squares containing traffic lights). The LLM, being capable of both understanding image content and interpreting textual instructions, analyzes the provided CAPTCHA image and the accompanying prompt. It then processes this information to identify the images that satisfy the given criteria. The output of the LLM is a set of predicted selections which correspond to the images it believes match the prompt. The author implies a successful bypass, suggesting the LLM demonstrated an ability to correctly identify and select the correct images in the hCaptcha challenge with a reasonably high degree of accuracy. This approach leverages the advanced capabilities of multimodal LLMs to understand and interpret both visual and textual information, effectively mimicking human-like comprehension of the CAPTCHA challenge. The post highlights the potential implications of this technique, suggesting it could be used to automate the solving of hCaptchas, potentially posing a challenge to the effectiveness of this widely used bot detection mechanism. The author doesn't explicitly delve into the specific LLM used, its architecture, or provide detailed quantitative results regarding the success rate, but the post strongly suggests the feasibility of this method.

  • hCaptcha
  • CAPTCHA
  • multimodal
  • Large Language Model
  • LLM
  • AI
  • artificial intelligence
  • machine learning
  • computer vision
  • natural language processing
  • NLP
  • Challenge Solving
  • Automation
  • Web Security
  • accessibility

  Summary of Comments ( 1 )
  https://news.ycombinator.com/item?id=43569001

  Verbose tl;dr

  The Hacker News comments discuss the implications of using LLMs to solve CAPTCHAs, expressing concern about the escalating arms race between CAPTCHA developers and AI solvers. Several commenters highlight the potential for these models to bypass accessibility features intended for visually impaired users, making audio CAPTCHAs vulnerable. Others question the long-term viability of CAPTCHAs as a security measure, suggesting alternative approaches like behavioral biometrics or reputation systems might be necessary. The ethical implications of using powerful AI models for such tasks are also raised, with some worrying about the potential for misuse and the broader impact on online security. A few commenters express skepticism about the claimed accuracy rates, pointing to the difficulty of generalizing performance in real-world scenarios. There's also a discussion about the irony of using AI, a tool intended to enhance human capabilities, to defeat a system designed to distinguish humans from bots.

  The Hacker News post "Solve the hCaptcha challenge with multimodal large language model" has generated several comments discussing the implications of using LLMs to bypass CAPTCHAs.

  Several commenters express concern about the escalating arms race between CAPTCHA developers and those trying to circumvent them. One commenter highlights the increasing difficulty of CAPTCHAs for visually impaired users, suggesting this development further exacerbates that problem. They point out the irony that while these models are improving accessibility in some areas, they're making it worse in others.

  Another commenter questions the long-term viability of CAPTCHAs as a security measure, anticipating that LLMs will eventually render them obsolete. They predict a shift towards more robust authentication methods.

  Some users discuss the technical aspects of the LLM's approach, speculating about its ability to generalize to different CAPTCHA variations. One commenter questions the model's performance on more complex challenges, suggesting that current CAPTCHAs might be intentionally "dumbed down" due to the prevalence of simpler bypass methods. They anticipate an increase in CAPTCHA complexity as a response to these advancements in LLM-based solutions.

  There's also a discussion about the ethical implications of using LLMs to bypass security measures. One comment points out the duality of the situation, noting that while this technology can be used maliciously, it could also be valuable for accessibility purposes.

  Another thread explores the potential uses of this technology beyond just bypassing CAPTCHAs. Some suggest it could be helpful for automating tasks that involve image recognition, such as data entry or web scraping.

  Finally, a few commenters share anecdotes about their own experiences with CAPTCHAs, highlighting the frustration they often cause. One user mentions encountering CAPTCHAs that are seemingly impossible to solve, even for humans.

  In summary, the comments section reflects a mix of concern, curiosity, and cautious optimism about the implications of using LLMs to solve CAPTCHAs. The discussion touches on accessibility issues, the future of online security, the technical challenges of CAPTCHA design, and the ethical considerations surrounding the use of this technology.

• MapTCHA, the open-source CAPTCHA that improves OpenStreetMap [video]

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  Posted: 2025-02-13 19:44:34

  Verbose tl;dr

  MapTCHA is an open-source CAPTCHA that leverages user interaction to improve OpenStreetMap data. Instead of deciphering distorted text or identifying images, users solve challenges related to map features, like identifying missing house numbers or classifying road types. This process simultaneously verifies the user and contributes valuable data back to OpenStreetMap, making it a mutually beneficial system. The project aims to be a privacy-respecting alternative to commercial CAPTCHA services, keeping user contributions within the open-source ecosystem.

  The FOSDEM 2025 presentation, "MapTCHA, the open-source CAPTCHA that improves OpenStreetMap," introduces MapTCHA, a novel CAPTCHA system designed to simultaneously verify human users and contribute valuable data to the OpenStreetMap (OSM) project. Traditional CAPTCHAs, while effective at distinguishing humans from bots, often involve tasks that, while simple for humans, are computationally challenging for machines. These tasks, however, rarely contribute any useful output. MapTCHA diverges from this model by leveraging human input to enhance the accuracy and completeness of OSM's global map data.

  Specifically, MapTCHA presents users with challenges directly related to geospatial information, thereby directly improving the quality of OSM's data. This might involve identifying specific features on a map segment, confirming the location of businesses or landmarks, verifying street names, or categorizing different types of land use. By correctly solving these micro-tasks, users prove their human identity while simultaneously providing valuable information that can be integrated back into the OpenStreetMap database. This dual-purpose approach makes MapTCHA a compelling alternative to traditional CAPTCHAs, transforming what is typically a friction-laden verification process into a crowdsourced data contribution mechanism.

  Furthermore, the presentation emphasizes MapTCHA's open-source nature. This allows for community involvement in its development, fostering transparency and encouraging adaptation and implementation across various platforms and services. By being open-source, MapTCHA can be readily customized and integrated into different websites and applications, broadening its reach and maximizing its potential to contribute to the ongoing development and improvement of OpenStreetMap. The project actively encourages community contributions and aims to establish a collaborative environment for refining and expanding the functionalities of this innovative CAPTCHA system. The ultimate goal is to build a robust, versatile, and community-driven CAPTCHA solution that simultaneously combats bot activity and empowers individuals to contribute to the growth of a freely accessible and comprehensive global map.

  • CAPTCHA
  • OpenStreetMap
  • Open Source
  • Mapping
  • geospatial
  • Data validation
  • Human Verification
  • Crowdsourcing
  • FOSDEM
  • video

  Summary of Comments ( 41 )
  https://news.ycombinator.com/item?id=43040382

  Verbose tl;dr

  HN commenters generally express enthusiasm for MapTCHA, praising its dual purpose of verifying users and improving OpenStreetMap data. Several suggest potential improvements, such as adding house number verification and integrating with other OSM editing tools like iD and JOSM. Some raise concerns about the potential for automated attacks or manipulation of the CAPTCHA, and question whether the tasks are genuinely useful contributions to OSM. Others discuss alternative CAPTCHA methods and the general challenges of balancing usability and security. A few commenters share their experiences with existing OSM editing tools and processes, highlighting the existing challenges related to vandalism and data quality. One commenter points out the potential privacy implications of using street-level imagery.

  The Hacker News post about MapTCHA, an open-source CAPTCHA that improves OpenStreetMap, generated several comments discussing various aspects of the project.

  Several users expressed enthusiasm for the project, highlighting the dual benefit of verifying users and contributing to OpenStreetMap. One commenter appreciated the elegant solution of combining CAPTCHA with a useful task, seeing it as a win-win. Another user praised the project's cleverness and its potential to improve accessibility for OpenStreetMap contributions.

  The technical implementation of MapTCHA was also a subject of discussion. One user questioned how the system verifies the accuracy of user input, particularly considering the potential for malicious actors. Another commenter discussed the tradeoffs between using aerial imagery and map data, suggesting that map data might be more suitable for certain tasks like identifying street signs, while aerial imagery might be better for other tasks. This spurred further conversation about the challenges of relying solely on map data, which might not always be accurate or up-to-date.

  The discussion also touched upon the broader implications of using CAPTCHAs and their potential alternatives. One commenter suggested exploring alternatives to CAPTCHAs entirely, mentioning techniques like proof-of-work or analyzing user behavior. Another user expressed concern about the accessibility of CAPTCHAs for users with disabilities and inquired about the project's considerations for accessibility.

  The usability of MapTCHA was another point of interest. One user questioned whether the tasks presented in MapTCHA would be too complex or time-consuming for average users. This sparked a discussion about the balance between task complexity and the value of the contributions to OpenStreetMap.

  Finally, some comments focused on the potential applications and future development of MapTCHA. One commenter suggested the possibility of using MapTCHA for other crowdsourced mapping projects. Another user expressed interest in seeing the project expanded to include tasks beyond identifying features on a map, such as verifying the accuracy of existing data.