Key Takeaways: Using a Blacklist of Stolen Passwords [Webinar]

More than 90 billion passwords are being used across the web today, and it’s expected to be nearer 300 billion by 2020. With that in mind, the topics of password best practices and the threats around stolen credentials, remain top challenges for many global organizations.

Security Boulevard recently hosted a webinar with Shape and cyber security expert Justin Richer, co-author of the new NIST (National Institute of Standards and Technology) Digital Identity Guidelines. The webinar looks at how password protection and password attack prevention have evolved.

Watch the full webinar here

Key Takeaways

Traditional P@$$wOrd Guidelines Don’t Solve the Problem

Justin Richer discusses how passwords were originally invented as a way to gain entry. But today they have evolved into a way to authenticate who you are. Companies rely on a username-password combination to give them confidence you are who you say you are. So once passwords are stolen, companies have less and less confidence you are the person you claim to be.

To make it difficult for criminals to steal your identity companies have implemented complex password requirements. Unfortunately, this conventional wisdom around password management, such as enforced rotation every six months, using at least six characters, upper and lowercase characters, numbers and symbols, have made passwords hard to remember.

Additionally, for non-English languages, not all these rules can be applied regarding uppercase and lowercase. They also don’t always adapt to the world of mobile devices where it’s hard to type using touch screens, and the emerging technology of voice recognition personal assistants.

In the end, users reuse passwords that are easy to remember and pick bad passwords due to password fatigue. As a result, traditional password guidelines don’t help companies gain confidence—they are actually compounding the problem.

The Real Culprit – Password Reuse

In reality the problem companies are fighting is password reuse. Once one account has been compromised, the attackers have access to multiple accounts that use the same username and password. Fraudsters may use these accounts themselves, but often they bundle up the stolen credentials and sell the passwords on the dark web.

New NIST guidelines serve to help companies reduce password fatigue and reuse, while also providing suggestions for testing new passwords against a database of stolen credentials—a breach corpus. When the two are implemented together, fraudsters will have a much harder time taking advantage of stolen credentials through account takeover and automated fraud.

New Passwords and Using Blacklists

Revision 3 of the NIST password guidelines overview – Digital identity guidelines – has dramatically updated recommendations on how to use passwords properly:

The main tenets are:

    • Don’t rely on passwords alone. Use multi-factor authentication steps to verify the user is who they claim to be.
    • Drop the complexity requirements, they make passwords hard to remember and aren’t as effective as once thought.
    • Allow all different types of characters.
    • End the upper limit on size. Length can be an important key to avoid theft.
    • Rotate when something seems suspect. Don’t rotate because of an arbitrary timeout, like every six months.
    • Disallow common passwords.
    • Check new passwords against a blacklist of stolen passwords


The most important step is to check new passwords against a blacklist. These cover a range of passwords, including those known to have been already compromised, and those used in any major presentation. Checking against a blacklist is new territory—a lot of organizations don’t even know where to start.

Creating a Blacklist

An ideal blacklist should have all stolen passwords—not just the ones discovered on the dark web. Unfortunately creating a list of all stolen passwords is difficult. Recently companies have been relying on lists of stolen credentials from the dark web, but these are often too little, too late as it’s not possible to know how long these stolen passwords have been in circulation. For example, Yahoo was breached in 2013, but didn’t realize until 2016. Due to the economics of attackers, there is almost always a big lag between when data is breached and when it’s exploited.

Blackfish and the Breach Corpus

At Shape we created Blackfish to proactively invalidate user and employee credentials as soon as they are compromised from a data breach. It notifies organizations in near real-time, even before the breach is reported or discovered. How does it do this?

Blackfish technology is built upon the Shape Security global customer network which includes many of the largest companies in the industries most targeted by cybercriminals including banking, retail, airlines, hotels and government agencies. By protecting the highest profile target companies, the Blackfish network sees attacks using stolen credentials first, and is able to invalidate the credentials early in the fraud kill chain. This provides a breakthrough solution in solving the zero-day vulnerability gap between the time a breach occurs and its discovery.

Using machine learning, as soon as a credential is identified as compromised on one site, Blackfish instantly and autonomously protects all other customers in its collective defense network. As a result, Blackfish is the most comprehensive blacklist in the industry today.

Don’t Rely on Dark Web Research

Dark web research provides too little information, too late. Today major online organizations can take a much more proactive approach to credential stuffing. By using Blackfish businesses can immediately defend themselves from attack while reducing the operational risk to the organization. Over time these stolen credentials become less valuable to attackers because they just don’t work, and in turn credential stuffing attacks and fraud are reduced.

Watch the full webinar here

Introducing Blackfish, a system to help eliminate the use of stolen passwords

Today we’re releasing Blackfish, a system that proactively protects companies from credential stuffing before an attack takes place. Normally, credential stuffing starts with a data breach at one major company (“Initial Victim”), and continues when a criminal then uses the stolen data (usernames and passwords) against dozens or even hundreds of different companies (“Downstream Victims”). Usually, many months or years pass before the Initial Victim realizes and discloses the initial data breach, and in that time, criminals are able to successfully attack huge numbers of Downstream Victims. Later, once the Initial Victim does disclose the breach, the Downstream Victims start matching the username/password pairs from the Initial Victim against their own user databases, and resetting any passwords that match. The whole process can take years and results in hundreds of millions of dollars worth of fraud and brand damage.

Blackfish changes all that. From the very first moment a criminal attempts to use stolen usernames and passwords, Blackfish begins monitoring and protecting matching accounts at other companies. So, while under normal circumstances a criminal can get hundreds of chances to monetize the stolen usernames and passwords, with Blackfish in place, criminals get far fewer chances.

You may be wondering how Blackfish can accomplish all this. Explaining that requires a little background on Shape Security.

We founded Shape six years ago to answer a simple question: is a visitor to a web or mobile app an actual human being? This simple question proved to be an important one. As we perfected our ability to answer it, we started eliminating enormous amounts of fraudulent traffic from the largest web and mobile apps in the world — often 90% or more of the login traffic from a Fortune 100 web application.

Today, we are the primary line of defense for many of the largest organizations around the world. Our customers include: three of the top four banks, three of the top five airlines, two of the top three hotel chains, and numerous other leading companies and government agencies.

We secure all of those large organizations in a centralized way, directly delivering the security outcome of eliminating fraudulent traffic. That centralized security capability is also the heart of Blackfish, and allows Blackfish to see stolen usernames and passwords in use far before anyone else ever knows about them (including the Initial Victim).

Think about it: if you were a criminal and managed to steal all the usernames and passwords from a major corporation, where would you try them out? If you’re like most criminals, the answer is that you’d try them on the largest banks, airlines, hotels, and retail sites in the world. That’s what happens in practice, and when it does, that’s also when Blackfish sees the very first such attack, and sets about protecting all username/password pairs that happen to match on other large websites.

Blackfish does all this before the original data breach is reported or even detected by the Initial Victim company.

The problem with looking for credentials on the dark web

You can scour the dark web to find user credentials, but one of the greatest dangers companies face today is the long window of time between when breaches occur on third-party websites like Yahoo, and when those breaches are discovered and announced. Instead of hoping that stolen passwords will appear in the dark web in time to be useful, Blackfish autonomously detects credential stuffing attacks on the largest, most targeted websites in the world, identifies newly stolen credentials, and nullifies them globally. That stolen data becomes useless to cybercriminals.

How does it work?

Shape has grown into one of the largest processors of login traffic on the entire web. We have built machine learning and deep learning systems to autonomously identify credential stuffing attacks in real-time. These systems now generate an important byproduct: direct knowledge of stolen usernames and passwords when criminals are first starting to exploit them against major web and mobile apps. What this means is that we see the stolen assets months or years before they appear on the dark web.

Blackfish’s knowledge base of compromised credentials is built with maximum security in mind. To ensure that its knowledge base is secured, Blackfish does not store any credential information but instead leverages Bloom filters to create probabilistic data structures to perform its operations. As a result, the compromised credentials themselves are not stored anywhere and Blackfish can use the information about compromises to improve security while maintaining full data privacy.

What good is a stolen password if you can never use it?

For better or for worse, memorized secrets (a.k.a. “passwords”) are the most widely used authentication mechanism online. As such, having access to millions of stolen passwords (over 3.3 billion were reported stolen in 2016 alone) allows cybercriminals to easily take over users’ accounts on any major website. They do this with credential stuffing attacks, which take stolen passwords from website A and try them on website B to see which accounts the same email addresses and passwords will unlock. Cybercriminals can do this reliably with a typical 1-2% success rate, allowing them to seize the value in bank accounts, gift card accounts, airline loyalty programs, and other accounts, which they can then monetize for a predictable ROI.

Since credential stuffing attacks are responsible for more than 99.9% of account takeover attempts, if we identify the stolen credentials that are used in these attacks, and invalidate them across other websites, we change the economics for cybercriminals significantly. If their 1-2% success rate now drops by two orders of magnitude or more, their “business” no longer functions. At that point, the cybercriminal has no choice but to try to obtain new stolen passwords. If those new passwords are similarly detected and invalidated, it will become clear to the criminals that the economics of their scheme have been broken. We think that over time, Blackfish will end credential stuffing for everyone.

We are all very excited at Shape to announce this system and our vision to make credential stuffing attacks a thing of the past. You can learn more on our website and contact us when your company is ready to try Blackfish.

How Cybercriminals Bypass CAPTCHA

One thing the world can consistently agree on is that CAPTCHAs are annoying. The puzzle always appears in the most inconvenient of places. Online gift card purchases. Creating an account on an ecommerce webpage. Typing in those hard to memorize credentials one too many times.

But the ultimate frustration about CAPTCHA is that it serves absolutely no purpose. The CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart), was originally designed to prevent bots, malware, and artificial intelligence (AI) from interacting with a web page. In the 90s, this meant preventing spam bots. These days, organizations use CAPTCHA in an attempt to prevent more sinister automated attacks like credential stuffing.

Almost as soon as CAPTCHA was introduced, however, cybercriminals developed effective methods to bypass it. The good guys responded with “hardened” CAPTCHAs but the result remains the same: the test that attempts to stop automation is circumvented with automation.

There are multiple ways CAPTCHA can be defeated. A common method is to use a CAPTCHA solving service, which utilizes low-cost human labor in developing countries to solve CAPTCHA images. Cybercriminals subscribe to a service for CAPTCHA solutions, which streamline into their automation tools via APIs, populating the answers on the target website. These shady enterprises are so ubiquitous that many can be found with a quick Google search, including:

  • DeathbyCAPTCHA
  • 2Captcha
  • Kolotibablo
  • ProTypers
  • Antigate

This article will use 2Captcha to demonstrate how attackers integrate the solution to orchestrate credential stuffing attacks.


Upon accessing the site, the viewer is greeted with the image below, asking whether the visitor wants to 1) work for 2Captcha or 2) purchase 2Captcha as a service.


Option 1 – Work for 2Captcha

To work for 2Captcha, simply register for an account, providing an email address and PayPal account for payment deposits. During a test, an account was validated within minutes.

New workers must take a one-time training course that teaches them how to quickly solve CAPTCHAs. It also provides tips such as when case does and doesn’t matter. After completing the training with sufficient accuracy, the worker can start earning money.


After selecting “Start Work,” the worker is taken to the workspace screen, which is depicted above. The worker is then provided a CAPTCHA and prompted to submit a solution. Once solved correctly, money is deposited into an electronic “purse,” and the worker can request payout whenever they choose. There is seemingly no end to the number of CAPTCHAs that appear in the workspace, indicating a steady demand for the service.


2Captcha workers are incentivized to submit correct solutions much like an Uber driver is incentivized to provide excellent service—customer ratings. 2Captcha customers rate the accuracy of the CAPTCHA solutions they received. If a 2Captcha worker’s rating falls below a certain threshold, she will be kicked off the platform. Conversely, workers with the highest ratings will be rewarded during times of low demand by receiving priority in CAPTCHA distribution.

Option 2 – 2Captcha as a service

To use 2Captcha as a service, a customer (i.e., an attacker) integrates the 2Captcha API into her attack to create a digital supply chain, automatically feeding CAPTCHA puzzles from the target site and receiving solutions to input into the target site.

2Captcha helpfully provides example scripts to generate API calls in different programming languages, including C#, JavaScript, PHP, Python, and more. The example code written in Python has been reproduced below:


Integrating 2CAPTCHA into an Automated Attack

How would an attacker use 2Captcha in a credential stuffing attack? The diagram below shows how the different entities interact in a CAPTCHA bypass process:


Technical Process:

  1. Attacker requests the CAPTCHA iframe source and URL used to embed the CAPTCHA image from the target site and saves it locally
  2. Attacker requests API token from 2Captcha website
  3. Attacker sends the CAPTCHA to the 2Captcha service using HTTP POST and receives a Captcha ID, which is a numerical ID attributed with the CAPTCHA image that was submitted to 2Captcha. The ID is used in step 5 for an API GET request to 2Captcha to retrieve the solved CAPTCHA.
  4. 2Captcha assigns the CAPTCHA to a worker who then solves it and submits the solution to 2Captcha.
  5. Attacker programs script to ping 2Captcha using CAPTCHA ID (every 5 seconds until solved). 2Captcha then sends the solved CAPTCHA. If the solution is still being solved, the attacker receives a post from 2Captcha indicating “CAPTCHA_NOT_READY” and the program tries again 5 seconds later.
  6. Attacker sends a login request to the target site with the fields filled out (i.e. a set of credentials from a stolen list) along with the CAPTCHA solution.
  7. Attacker iterates over this process with each CAPTCHA image.

Combined with web testing frameworks like Selenium or PhantomJS, an attacker can appear to interact with the target website in a human-like fashion, effectively bypassing many existing security measures to launch a credential stuffing attack.

Monetization & Criminal Ecosystem

With such an elegant solution in place, what does the financial ecosystem look like, and how do the parties each make money?

Monetization: CAPTCHA solver

Working as a CAPTCHA solver is far from lucrative. Based on the metrics provided on 2Captcha’s website, it’s possible to calculate the following payout:

Assuming it takes 6 seconds per CAPTCHA, a worker can submit 10 CAPTCHAs per minute or 600 CAPTCHAs per hour. In an 8 hour day that’s 4800 CAPTCHAs. Based on what was earned during our trial as an employee for 2Captcha (roughly $0.0004 per solution), this equates to $1.92 per day.

This is a waste of time for individuals in developed countries, but for those who live in locales where a few dollars per day can go relatively far, CAPTCHA solving services are an easy way to make money.

Monetization: Attacker

The attacker pays the third party, 2Captcha, for CAPTCHA solutions in bundles of 1000. Attackers bid on the solutions, paying anywhere between $1 and $5 per bundle.

Many attackers use CAPTCHA-solving services as a component of a larger credential stuffing attack, which justifies the expense. For example, suppose an attacker is launching an attack to test one million credentials from Pastebin on a target site.  In this scenario, the attacker needs to bypass one CAPTCHA with each set of credentials, which would cost roughly $1000.  Assuming a 1.5% successful credential reuse rate, the attacker can take over 15,000 accounts, which can all be monetized.

Monetization: 2Captcha

2Captcha receives payment from the Attacker on a per 1000 CAPTCHA basis. As mentioned above, customers (i.e. attackers) pay between $1 and $5 per 1000 CAPTCHAs. Services like 2Captcha then take a cut of the bid price and dole out the rest to their human workforce. Since CAPTCHA solving services are used as a solution at scale, the profits add up nicely. Even if 2Captcha only receives $1 per 1000 CAPTCHAs solved, they net a minimum of 60 cents per bundle. The owners of these sites are often in developing countries themselves, so the seemingly low revenue is substantial.

What about Google’s Invisible reCAPTCHA?

In March of this year, Google released an upgraded version of its reCAPTCHA called “Invisible reCAPTCHA.” Unlike “no CAPTCHA reCAPTCHA,” which required all users to click the infamous “I’m not a Robot” button, Invisible reCAPTCHA allows known human users to pass through while only serving a reCAPTCHA image challenge to suspicious users.

You might think that this would stump attackers because they would not be able to see when they were being tested. Yet, just one day after Google introduced Invisible reCAPTCHA, 2CAPTCHA wrote a blog post on how to beat it.

The way Google knows a user is a human is if the user has previously visited the requested page, which Google determines by checking the browser’s cookies. If the same user started using a new device or recently cleared their cache, Google does not have that information and is forced to issue a reCAPTCHA challenge.

For an attacker to automate a credential stuffing attack using 2Captcha, he needs to guarantee a CAPTCHA challenge. Thus, one way to bypass Invisible reCAPTCHA is to add a line of code to the attack script that clears the browser with each request, guaranteeing a solvable reCAPTCHA challenge.

The slightly tricky thing about Invisible reCAPTCHA is that the CAPTCHA challenge is hidden, but there is a workaround. The CAPTCHA can be “found” by using the “inspect element” browser tool. So the attacker can send a POST to 2Captcha that includes a parameter detailing where the hidden CAPTCHA is located. Once the attacker receives the CAPTCHA solution from 2Captcha, Invisible reCAPTCHA can be defeated via automation in one of two ways:

  1. JavaScript action that calls a function to supply the solved token with the page form submit
  2. HTML code change directly in the webpage to substitute a snippet of normal CAPTCHA code with the solved token input.

The fact that Invisible reCAPTCHA can be bypassed isn’t because there was a fatal flaw in the design of the newer CAPTCHA. It’s that any reverse Turing test is inherently beatable when the pass conditions are known.

As long as there are CAPTCHAs, there will be services like 2Captcha because the economics play so well into the criminal’s hands. Taking advantage of low cost human labor minimizes the cost of doing business and allows cybercriminals to reap profits that can tick upwards of millions of dollars at scale. And there will always be regions of the world with cheap labor costs, so the constant demand ensures constant supply on 2Captcha’s side.

The world doesn’t need to develop a better CAPTCHA, since this entire approach has fundamental limitations. Instead, we should acknowledge those limitations and implement defenses where the pass conditions are unknown or are at least difficult for attackers to ascertain.


Holmes, Tamara E. “Prepaid Card and Gift Card Statistics.”, 01 Dec. 2015. Web.

Hunt, Troy. “Breaking CAPTCHA with Automated Humans.” Blog post. Troy Hunt. Troy Hunt, 22 Jan. 2012. Web.

Motoyama, Marti, Kirill Levchenko, Chris Kanich, and Stefan Savage. Re: CAPTCHAs–Understanding CAPTCHA-solving Services in an Economic Context. Proc. of 19th USENIX Security Symposium, Washington DC. Print.