Mar 162013
 

Online polls and user voting contests are ubiquitous in the modern internet. Almost every major forum provider supports online polls, they are common on Facebook, and entire third party websites exist to provide polls and related services that can be embedded (such as SodaHead, who provides polling features for the LA times and ESPN SportsNet). And, since March Madness is upon us, almost everyone is running brackets—some for fun, but some carry (substantial) rewards for the winners. How then, does one ensure that a poll is not gamed by people who stand to profit from a specific result?

The fundamental challenge with securing polls and ensuring meaningful results lies in identifying anonymous users and restricting them to one vote per physical person. The nature of the internet complicates this problem: users can spoof their identities, multiple users can share an identity, and software can be written to automate any process that exploits a shortcoming in the poll code itself. At the same time, any anti-cheating strategy must not interfere with the voting process for the average user, and it must not involve a level of effort that a user considers unjustified given the significance of the poll in question. A user may be willing to spend 10 minutes validating his personal identity in order to cast a vote electronically for the Presidential election, but he probably is unwilling to spend more than 10 seconds casting a vote for his favorite brand of soda.

Passive Identification

The two most common methods of identifying a user without requiring him to take action are: (1) IP address tracking, and (2) tracking/persistent cookies. However, both have significant drawbacks that make them unsuitable for use as the primary method of preventing cheating in a polling application. The problem with IP address tracking is that it prevents users who are behind a NAT from differentiating themselves. In many situations, users who connect to the internet do so through either a NAT or a proxy: for instance, users of any wifi connection in a McDonald’s, an airport, or even public municipal services will all appear as a single IP address per physical location. In many situations, it is also possible for users to acquire new IP addresses and therefore new identities, by using cloud providers, open proxies, or other tactics. Therefore, anti-cheating strategies that limit each IP address to one vote are unsuitable because they will likely prevent a percentage of legitimate voters from taking part in the poll, but they will not eliminate illegitimate votes from users who wish to spam the poll (although they will not form a significant portion of votes, the loss in legitimate votes will make them more important to the poll’s outcome).

The other passive method worth discussing is the use of persistent or so-called “tracking” cookies, which would store an identifier to indicate that a user had already voted. However, this method is laughably easy to circumvent: cookies are not shared between browsers, cookies can easily be cleared by the end user, and most browsers can even be set to simply ignore cookies in the first place. Although some techniques exist for hardening cookies against removal (through questionable tactics), ultimately these can be defeated by the use of non-browser HTTP tools, such as curl. Against polls that use only cookies to prevent users from voting multiple times, it is trivial to configure scripts using curl that are capable of submitting thousands of votes per minute that appear as unique users to the server. Clearly, this is unacceptable from a security perspective

Active Identification

Realizing that passive protection does not offer much against scripted attacks, some sites make a requirement for active user identification in order to vote. This can take many forms, such as (1) requiring a user to register an account and log in to the website before he can vote, (2) requiring the user to authenticate through Facebook, Google, or an OpenID provider, or (3) for the sake of completeness, filling out a CAPTCHA in order to vote. All three of these methods are very intrusive to the user’s experience, which means they are unsuitable for some types of “quick” polls, even if they carry prizes for the winners, but they do offer much more robust protection against polling spam.

The first method, which requires the creation of a user account, works for sites that host polls along with forums; most users that would be interested in completing the poll have already registered an account, so there is no additional user burden for them to use the poll. However, this does prevent non-members from participating unless they create an account. For some sites this may function to bring in additional users, but for large aggregators it will often provoke an uncomfortable response from users—”why do they need me to create an account? I don’t want an account here; I just want to vote in this poll.” With appropriate protection on the account registration process (i.e. the use of CAPTCHAs, activation emails, unique email addresses, etc.), it is possible for a site which uses local accounts to restrict voters to offer a polling experience that will be free from attempted poll spam, because it becomes far too tedious on a per-vote basis and it is difficult to automate.

In the same vein of account-based identification, the use of OpenID, Facebook, or Google accounts for ID is a huge improvement in the user experience. This is a lot less intrusive as it does not require users to create a new account along with associated usernames and passwords on each site, but it does introduce privacy concerns. Giving a third party access to Facebook account information can leak a user’s real name and interests or other internet activity to a site that may not be trusted with this information. For this reason, although it was at one time very popular (and is still used or supported by many sites), the use of Facebook accounts for identification is unavailable on many large sites, even though it would effectively limit gaming the polls. General OpenIDs, however, can easily be abused, because it is possible to create a private OpenID endpoint that will authorize any number of unique accounts and script the entire process, which offers no more security than a cookie-based method (that is, none).

Finally, you can require users to complete a CAPTCHA before voting in the poll, which, although it does not actively identify users, will prevent automated programs from spamming the poll with votes. This may be sufficient to deter some malicious users, but not all. Although CAPTCHAs are not secure against all forms of automated attacks, high quality ones are available for free and are frequently updated to defeat the latest in OCR while maintaining human readability. The main downside to CAPTCHAs is the amount of effort for legitimate users, which is why they are generally not seen for something as trivial as an online poll. It takes about 2 seconds to select a polling option and click vote, but it will take 30-45 seconds for most users to fill out a CAPTCHA. For the same reasons that they are likely unwilling to register a throw-away account, most users would see a CAPTCHA attached to a simple poll and decide against voting rather than attempting to solve the puzzle.

Where does that leave us?

So far, it seems as though every method I’ve described either offers zero protection against automated attempts to poison the poll’s outcome or presents too much inconvenience to the user for practical use , either because it requires too much effort or raises privacy concerns. The truth is that there is no silver bullet that magically solves the problem, but my recommendation is the use of a tiered approach, along with a technique that I have not yet seen used. Users who wish to “cheat” at online polls come in roughly three flavors, the curious, the mildly knowledgeable, and the “hacker” or programmer type.

The first of these, the curious user, is basically an average user who knows how to use his web browser but doesn’t really understand much about web technology. A number of these users will see a poll, vote, and then upon seeing that their choice is losing, will wonder to themselves “can I vote again somehow?” Most will try refreshing the page or something, and then if they can, vote perhaps a half dozen times before getting bored and moving on. They are a very low priority threat, but they are also easily defeated with a tracking cookie, so, despite its inefficiency against sophisticated spam, the use of tracking cookies to prevent multiple votes is mandatory, both because of how easy it is and because of how many would-be illegitimate votes it can stop with zero impact on other users.

The second type of user, the mildly knowledgeable one, knows about the basics of how the internet works: he’s aware of tracking cookies, and he knows that that is how a lot of polls prevent people from voting multiple times. If he wants to try to spam a poll, he will be able to clear his cookies and re-vote multiple times, circumventing the tracking cookie mechanism. He could be stopped by a form of active identification, but the volume of votes that can be submitted by a user like this is limited in scope because he does not understand how to script. A friend of mine recently fell into this category while trying to win an online contest: he was able to vote 3-4 times per minute by recording a screen macro where he would load the page, vote, clear cookies, and then repeat the entire process. Most polling sites rightfully choose to ignore these users because their ability to impact a poll that reaches many thousands of people is minimal.

Finally, we have the “hacker” or programmer type of would-be assailant. He understands how to debug javascript, he knows how HTTP works, and he has a familiarity with scripting and other utilities (such as cURL) that may be available to automate the process. I myself would fall into this category, as would many readers. By spending 5-10 minutes examining the source code for a website that features polling, it is easy to extract the target URL, even if it is submitted with AJAX or uses javascript to rewrite form targets in an attempt to obfuscate the poll’s behavior—just as in cryptography, security through obscurity is ineffective here. Furthermore, tools such as the Web Developer Toolbar, Firebug, and jsbeautifier.org make it easy to undo any obfuscation, reducing it to a matter of reading and tracing (sometimes even with a built-in javascript debugger) the code until the poll’s submission behavior is captured. Then, it is easy to write a shell script that simply invokes cURL with the correct arguments, which can include form data, referrer data, user agent data, and even fake cookie data if necessary. Using this approach it is possible to vote as fast as your requests are handled by the server which easily works out to thousands per minute. Poll poisoning from power users like this represents the most meaningful threat to the integrity of the result and should be dealt with, but so far I haven’t found any online polls that offer meaningful protection against it.

The goal, then, is not to totally prevent sophisticated attacks (although it would be ideal), but instead to reduce them to the point where they cannot have a significant impact on the poll’s outcome. Many small things can be used to reduce the effectiveness of scripted attacks without affecting normal users. The first is the use of hidden fields with random values that must match on form submission. Although such fields are traditionally used to mitigate cross site request forgeries, they are of use in decreasing the magnitude of a scripted attack in this case. The attacker can readily load the page before the form, read off the correct value of the field, and include it with his scripted submission, but this process takes time. Because vote requests are normally made without waiting for a response for each request (as the response is irrelevant), a spammer can submit them as fast as the server will accept his requests. However, if he must first load another page completely in order to read off a semi-secret value, the maximum rate at which he can request new pages is significantly reduced.

For example, if they were to implement the token-passing strategy and I wanted to script an attack against Buzzfocus’s TV show tournament, I would need to request the page for each vote I wanted to submit, which takes an average of 300 ms for my computer to download from the servers, limiting me to ~200 votes per minute, a significant reduction compared to the thousands (or tens of thousands? I’ve never tried, but I can’t imagine this taking many resources on my end as a large number of votes can be submitted in parallel) of votes I could have submitted without any need for state transfer. This does not eliminate the vulnerability but it does reduce it significantly in scope, which is the best we can hope for. Note that it takes 5-6 seconds for the page to load, but all of the necessary data is actually contained in the original html document—the rest is third party javascript, advertisements, etc. which are all included in “loading time,” but which would be irrelevant to a cURL-based attack.

Even 200 votes per minute is still a serious problem. Multiple machines could be involved, cloud providers could be used, and ultimately it’d be well within reach to get back to thousands of votes per minute, although the resource usage is already much higher, limiting the number of people who are willing to put in the effort to cheat at the poll. The next, and most effective step, then, is to identify the behavior of cheaters and target them specifically, which is surprisingly easy to do. Anyone who wants to skew the results of the poll must do so within a specific time frame, which generally means that he will try to vote as fast as possible for a length of time to ensure that his candidate is ahead. The best strategy in this case, is simply to apply a rate-limiting policy to vote submission, which can easily be implemented in software or even within the the webserver itself (nginx, for instance, supports request rate limiting by IP address and target URL, which would form a rudimentary solution to this problem with only a few lines of configuration).

Because users may share IP addresses, rate limiting cannot be totally unintelligent, but there is almost no reason for an IP address to vote more than once a minute. What are the odds of everyone in a single McDonald’s coming across a poll at exactly the same time and attempting to submit their choice? There are only a handful of people in McDonald’s even using the free wireless at any given part of the day, making this kind of a collision extremely unlikely. However, it can be further improved to reduce this problem anyway. Identify some amount of vote spam that is tolerable (for instance, five votes within a minute, initially) and set it as a threshold for activating the rate limiting procedure—this would allow multiple users, or friends who are all together in one location to vote at the same time. Because the way in which spammers operate is fundamentally different from how normal users operate, simply by tracking the rate of requests and limiting it as it becomes less and likely that it represents a legitimate series of votes, it is possible to reduce the power of a sophisticated spammer to nearly nothing.

Personally, I would outline a basic approach to rate limiting polls that looks something like this: Start by tracking the time at which a vote was submitted for a specific poll for each IP address. If the number of votes submitted in the last minute, ten minutes, or hour, etc. exceeds the threshold, flag the IP address and assign a timeout to the address. When the timeout expires, set a grace period timer of equal length to the timeout and begin accepting votes from the address again. If the number of votes received during this grace period exceeds the threshold again, assign a timeout for twice as long. After this timeout, set another grace period of equal length to the timeout and allow votes again. Repeat this process, doubling the timeout period and the grace period each time. If the grace period expires without exceeding the threshold again, reset the timeouts to the default. The timeout does not need to match the time period used to determine if spamming is occurring, but matching between the two will allow for the best detection, because a “slow” spammer might trigger a threshold of 100 votes in 15 minutes but might not trigger a per-minute detector at the same time unless they have equal values (making one redundant).

For a naïve approach, simply blasting the server with votes, this method will significantly limit the number of spam votes, providing significant defense against a power user’s attack. If someone attempts to submit votes constantly, we can determine how many he can get in assuming he starts as soon as the poll goes live and spams continuously for its entire duration of N minutes. Assuming an initial timeout and grace period of t_0=g_0 and a doubling of both each time the threshold, \theta is reached, we can find the total number of time periods (the initial pre-detection time plus all subsequent grace periods) during which the spammer may submit additional votes as the smallest integer x for which

t_0 + \sum\limits_{k=0}^x t_k+g_k \ge N

The recursive nature of the definition of t_k and the equality between t_0 and g_0 allows us to reduce the expression significantly:

t_0 + \sum\limits_{k=0}^x (t_k +g_k) = t_0 (2^{x+2}-1)

The maximum number of time periods during which we will receive votes, then, is the smallest integer x which satisfies

x \ge log_2 (\frac{N}{t_0} -1) - 2

This gives us a logarithmic increase in votes as a function of the poll’s duration, which, for a reasonable choice of \theta = 10 and t_0 = 1 minute for a poll lasting five days results in at most 110 illegitimate votes due to constant spam from one IP address. However, in the spirit of robustness, if we assume that the attacker knows how the exponential reduction strategy works, we need to analyze what might happen. This enables him to wait out each time period without triggering the threshold and achieve a maximum of \theta – 1 votes per t_0 minutes, totaling (\theta-1)\frac{N}{t_0} votes. In the quick spam scenario presented before with one minute timeouts, an attacker could still submit 64,800 votes per IP address, more than enough to alter the outcome of most online polls. Even with a longer timeout (such as 60 minutes) and a similar threshold (say, 30 votes), it is still possible to submit a lot of illegitimate votes, but the overall impact is significantly lower, as the maximal number of spam votes per minute per IP address has been reduced from potentially thousands to less than one.

In retrospect, rate limiting is a rather obvious solution, yet it is not one that I have seen implemented by any poll provider. It offers no additional burden to the normal end user, but it cleanly prevents the power user and even the mid-tier spammers from having significant impact on the poll. I’m surprised that nobody does this yet, given the prevalence of rate limiting in other applications to prevent messaging spam. The method I’ve outlined for rate limiting is rather basic (and I already have a better one in mind, but this post has gone on far longer than I intended), but the impact of even a basic method such as this can reduce spam votes by several orders of magnitude, making the poll overall more fun for users (people seem to become outraged if they think they were cheated out of something meaningless, for reasons that are unclear to me).

About Greg Malysa

I am a EE PhD student whose interests include computer architecture, analog circuit design, digital signal processing, and programming in a wide variety of languages. I do a lot of hands-on implementation work, such as doing PCB layout, assembling prototypes, and writing software for both embedded and general purpose systems. I also enjoy research and do many academic or proof-of-concept projects just to see if something can be done. If it involves electricity, I probably think it is interesting.

  11 Responses to “Preventing Spam Votes in Online Polls”

  1. Thanks for the interesting article. Maybe the psychological factor is as important as the technical one. What kind of audience will vote and will hackers ruin polls for fun?

    • I think you’re right, the psych factor is very important. People running polls may want to consider that in deciding how they want to prevent poisoning. At the same time though, lots of big polls (like Time’s Reader’s Person of the Year) have been completely altered by groups like 4chan, and it generated a lot of publicity for Time magazine as a result, which may be an incentive for them to allow it.

  2. I want to run a contest by asking the user to invite his/her friends to like the content of the user, the maximum likes by the first 10 users will be eligible for a prize money….i want your thoughts on

    1. Can I restrict the visitor to only one like ( 1 vote) by asking to fill details using facebook /linkedin/ twitter?

    2. Will this restrict the spammers? / multiple entries from the same person?

    Cos i need only interested individual to like the content and also serious visitors. Kindly advice. Thanks.

    • I think that social login (facebook, twitter, etc.) is a good way to restrict the number of votes that people make. It’s easy to track whether one facebook account has already been used or not, and creating additional facebook accounts takes a lot of time. Therefore, that should work great for limiting people to one vote.

      You will, however, need to try to do some cross-identificiation if you allow multiple social logins. You may want to pull identifying information (full name, email address, and anything else that seems useful), to attempt to cross-check the different websites. That way, I can’t vote once with my facebook account, then again with my twitter account, etc., because they all have the same full name and email address. Of course, you need to be careful, as full name may not be enough to uniquely identify people–there are many people with the name John Smith, for instance.

      It should have a strong deterring effect on spammers, because most sources of social login work pretty hard to prevent people from creating many accounts easily. If someone really wanted to try bump the votes, they could maybe create half a dozen facebook accounts to use, but it would take a significant amount of time. Personally, I don’t think that is a huge concern for you. You can also work to identify spam accounts by how many friends they have on facebook, for example, as well as how new the account is.

  3. This is a great approach to a very complex yet extremely relevant problem. Any plans to add actual code implementation examples of how you’d implement this in PHP/MySQL?

    • I’m really busy with my research right now (haven’t even had time to write new posts in two months!), but during the break between classes coming up at the end of the winter quarter, it’s a possibility! I hadn’t thought about it much, but setting up an open source implementation could be very useful for people. I’ll add it to my todo list.

  4. Hi Greg – quite an interesting read. I’m currently working on a Symfony bundle for polls (open source, of course), allowing voting for either registered or anonymous users or both (depending on configuration), and I intend to implement the basic rate limiting approach you’ve outlined here.

    That said, you also wrote “The method I’ve outlined for rate limiting is rather basic (and I already have a better one in mind, but this post has gone on far longer than I intended)” – I would be keen to hear what you have in mind there, so I’m hoping for a follow-up post. :) Cheers!

    • It’s been a (long) while so I’ve actually forgotten what I had in mind, but it most likely had to do with counting the number of timeouts and invalidating votes for abusive IPs (or allowing an admin to view this kind of stuff and select whether to allow or deny them). If I get a chance I’ll try to do a follow up on this with something on that topic. Regardless, I am glad to hear you are putting together an open source library that handles this!

  5. How about using browser fingerprint. This approach can make sure an user can at most add votes equal to the number of browsers he/she has. You can also include other steps before checking browser, ex: If IP address has a registered vote already then use browser fingerprint, if this is also same then rate limit votes.

    You can also restrict vote submissions by IP address – Meaning you can configure your server to accept votes from the web application, if web application is different then you can just configure server to accept requests from the web url()

    I think it is better to use combinations of steps rather than just rate limiting by ip.

  6. has anyone written a poll plugin for wordpress that works like this yet?

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