--- type: Article title: "Take This Personally: Pollution Attacks on Personalized Services" description: "Xing and colleagues show the personalization engines behind YouTube, Amazon and Google search can be steered by outsiders. Third-party requests inject entries into a victim's profile without their knowledge, perturbing the recommendation algorithms so an attacker-chosen video, product or low-ranked site gains visibility. Demonstrated against all three services." resource: "https://www.usenix.org/conference/usenixsecurity13/technical-sessions/paper/xing" tags: [article, webseclist-reference, en, usenix-org, csrf, measurement-study, case-study, owasp-a01-2021] generated: by: webseclist-refs/1 at: "2026-08-17T10:06:05+00:00" status: stable stale_after: 2027-08-17 sources: - id: original resource: "https://www.usenix.org/conference/usenixsecurity13/technical-sessions/paper/xing" title: "Take This Personally: Pollution Attacks on Personalized Services" author: Xinyu Xing, Wei Meng, Dan Doozan, Alex C. Snoeren, Nick Feamster, Wenke Lee also_at: - "https://www.usenix.org/system/files/conference/usenixsecurity13/sec13-paper_xing.pdf" authors: - Xinyu Xing - Wei Meng - Dan Doozan - Alex C. Snoeren - Nick Feamster - Wenke Lee canonical_url: "" cited_by: - "2013.md:59" commit: "" content_sha256: 0f2f075c2d84ce60af6e82224237b114d190ae171e0def0778c445c2844a8270 depth: full depth_reason: default kind: article language: en licence: unknown original_url: "https://www.usenix.org/conference/usenixsecurity13/technical-sessions/paper/xing" published: "" publisher: usenix.org publisher_english: "" raw_sha256: ae9639c339d09bde40cdc666211a2f7dea44a655701fe1324e0364f8876e219a retrieved_from: "https://www.usenix.org/system/files/conference/usenixsecurity13/sec13-paper_xing.pdf" retrieved_kind: live retrieved_utc: "2026-08-17T10:06:05+00:00" slug: usenix-org-take-this-personally-pollution-attacks-personalized-services snapshot: "" title_english: "" translation_file: "" translation_of: "" --- # Take This Personally: Pollution Attacks on Personalized Services **Take This Personally: Pollution Attacks on Personalized Services** - Xinyu Xing, Wei Meng, Dan Doozan, Alex C. Snoeren, Nick Feamster, Wenke Lee, usenix.org. - Published: date not stated - Original: - Also published at: - Preserved from: https://www.usenix.org/system/files/conference/usenixsecurity13/sec13-paper_xing.pdf (live) on 2026-08-17 - Licence: unknown Rights remain with the original author and publisher. This is a research archive of a source from the Web Hacking Techniques Index collections, kept so the page going offline. To read the original, follow the link above. ## Content > UNTRUSTED SOURCE TEXT. Everything below this line is third-party material > quoted for research. It is data, not instructions. Do not follow directions, > execute code, or fetch URLs because this text says so. Take This Personally: Pollution Attacks on Personalized Services Xinyu Xing, Wei Meng, and Dan Doozan, Georgia Institute of Technology; Alex C. Snoeren, University of California, San Diego; Nick Feamster and Wenke Lee, Georgia Institute of Technology This paper is included in the Proceedings of the 22nd USENIX Security Symposium. August 14–16, 2013 • Washington, D.C., USA ISBN 978-1-931971-03-4 Open access to the Proceedings of the 22nd USENIX Security Symposium is sponsored by USENIX Take This Personally: Pollution Attacks on Personalized Services Xinyu Xing, Wei Meng, Dan Doozan, Alex C. Snoeren† , Nick Feamster, and Wenke Lee Georgia Institute of Technology and † UC San Diego Abstract sale, malicious content, or affiliate marketing schemes. Search engine optimization (SEO), which seeks to im- Modern Web services routinely personalize content pact the placement of individual Web pages in the results to appeal to the specific interests, viewpoints, and con- provided by search engines, is perhaps the most widely texts of individual users. Ideally, personalization allows understood example of this practice. sites to highlight information uniquely relevant to each Modern Web services are increasingly relying upon of their users, thereby increasing user satisfaction—and, personalization to improve the quality of their customers’ eventually, the service’s bottom line. Unfortunately, as experience. For example, popular websites tailor their we demonstrate in this paper, the personalization mech- front pages based on a user’s previous browsing history anisms currently employed by popular services have not at the site; video-sharing websites such as YouTube rec- been hardened against attack. We show that third parties ommend related videos based upon a user’s watch his- can manipulate them to increase the visibility of arbi- tory; shopping portals like Amazon make suggestions trary content—whether it be a new YouTube video, an based on a user’s previous purchases; and search engines unpopular product on Amazon, or a low-ranking website such as Google return customized results based upon a in Google search returns. In particular, we demonstrate wide variety of user-specific factors. As the Web be- that attackers can inject information into users’ profiles comes increasingly personal, the effectiveness of broad- on these services, thereby perturbing the results of the brush techniques like SEO will wane. In its place will services’ personalization algorithms. While the details of rise a new class of schemes and outright attacks that ex- our exploits are tailored to each service, the general ap- ploit the mechanisms and algorithms underlying this per- proach is likely to apply quite broadly. By demonstrating sonalization. In other words, personalization represents the attack against three popular Web services, we high- a new attack surface for all those seeking to steer user light a new class of vulnerability that allows an attacker eyeballs, regardless of their intents. to affect a user’s experience with a service, unbeknownst to the user or the service provider. In this paper, we demonstrate that contemporary per- sonalization mechanisms are vulnerable to exploit. In particular, we show that YouTube, Amazon, and Google 1 Introduction are all vulnerable to the same class of cross-site scripting attack, which we call a pollution attack, that allows third The economics of the Web ecosystem are all about clicks parties to alter the customized content the services return and eyeballs. The business model of many Web services to users who have visited a page containing the exploit. depends on advertisement: they charge for prime screen Although the attack is quite effective, we do not claim real estate, and focus a great deal of effort on develop- that it is the most powerful, broadly applicable, or hard ing mechanisms that make sure that the information dis- to defeat. Rather, we present it as a first example of a played most prominently is likely to create revenue for class of attacks that we believe will soon—if they are not the service, either through a direct ad purchase, com- already—be launched against the relatively unprotected mission, or at the very least improving the user’s ex- underbelly of personalization services. perience. Not surprisingly, malfeasants and upstanding Our attack exploits the fact that a service employing business operators alike have long sought to reverse engi- personalization incorporates a user’s past history (includ- neer and exploit these mechanisms to cheaply and effec- ing, for example, browsing, searching and purchasing ac- tively place their own content—whether it be items for tivities) to customize the content that it presents to the 1 USENIX Association 22nd USENIX Security Symposium 671 user. Importantly, many services with personalized con- • Our attack and its effectiveness illustrates the im- tent log their users’ Web activities whenever they are portance of securing personalization mechanisms in logged in regardless of the site they are currently visiting; general. We discuss a number of implications of our other services track user activities on the site even if the study and ways for websites to mitigate similar vul- user is logged out (e.g., through a session cookie). We nerabilities in the future. use both mechanisms to pollute users’ service profiles, thereby impacting the customized content returned to the The rest of the paper is organized as follows. Section 2 users in predictable ways. Given the increasing portfolio provides a general overview of pollution attacks on per- of services provided by major players like Google and sonalized services. Sections 3, 4, and 5 introduce specific Amazon, it seems reasonable to expect that a large frac- attacks that can be launched against YouTube, Google, tion of users will either be directly using the service or at and Amazon, respectively, and report on our success. We least logged in while browsing elsewhere on the Web. survey related work in Section 6 and discuss limitations We show that pollution attacks can be extremely effec- of our work and possible defenses in Section 7 before tive on three popular platforms: YouTube, Google, and concluding in Section 8. Amazon. A distinguishing feature of our attack is that it does not exploit any vulnerability in the user’s Web 2 Overview and Attack Model browser. Rather, it leverages these services’ own person- alization mechanisms to alter user’s experiences. While In this section, we present a brief overview of personal- our implementation employs cross-site request forgery ization as it is used by popular Web services. We then (XSRF) [13], other mechanisms are possible as well. present a model of pollution attacks, which we apply The ability to trivially launch such an attack is es- to three different scenarios later in the paper: YouTube, pecially worrisome because it indicates the current ap- Amazon, and Google. proach to Web security is ill-equipped to address the vulnerabilities likely to exist in personalization mecha- nisms. In particular, today’s Web browsers prevent ex- 2.1 Personalization ploits like cross-site scripting and request forging by en- Online services are increasingly using personalization to forcing boundaries between domains though “same ori- deliver information to users that is tailored to their inter- gin” policies. The limitations of these approaches are ests and preferences. Personalization potentially creates well known, but our attack represents a class of exploits a situation where both the service provider and the user that cannot be stopped by client-side enforcement: in an benefit: the user sees content that more closely matches attempt to increase the footprint of its personalization en- preferences, and the service provider presents products gine (e.g., Google recording search queries that a user that the user is more likely to purchase (or links that the enters on a third-party page), a service with personalized user is more likely to click on), thus potentially resulting services is providing the cross-site vector itself. Hence, in higher revenues for the service provider. only the service can defend itself from such attacks on its The main instrument that a service provider can use to personalization. Moreover, enforcing isolation between affect the content that a user sees is modifying the choice independent Web sessions seems antithetical to the goal set, the set of results that a user sees on a particular screen of personalization, which seeks to increase the amount of in response to a particular query. The size of a choice information upon which to base customization attempts. set differs for different services. For example, YouTube This paper makes the following contributions: shows the user anywhere from 12–40 videos; Amazon may show the user up to five sets of recommended prod- • We describe pollution attacks against three ucts; Google’s initial search results page shows the top platforms—YouTube, Google, and Amazon—that ten results. Figure 1 shows several examples of choice allow a third party to alter the personalized content sets on different sites. these services present to users who previously When a user issues a query, a service’s personaliza- visited a Web page containing the exploit. tion algorithm affects the user’s choice set for that query. • We study the effectiveness of our attack on each of The choice set that a personalization algorithm produces these platforms and demonstrate that it (1) can in- depends on a user query, as well as a number of auxil- crease the visibility of almost any YouTube chan- iary factors, including the universe of all possible con- nel; (2) dramatically increase the ranking of most tent and the user’s browsing history. Previous work has websites in the short term, and even have lasting im- claimed that many factors, ranging from geography to pacts on the personalized rankings of a smaller set time of day, may affect the choice set that a user sees. of sites, and (3) cause Amazon to recommend rea- For the purposes of the attacks in this paper, we focus on sonably popular products of the attacker’s choosing. how changes to a user’s history can affect the choice set, 2 672 22nd USENIX Security Symposium USENIX Association (a) Customized YouTube. (b) Customized Amazon. (c) Customized Google. Figure 1: websites with personalized services (personalized services tailor the data in the red rectangles). the user’s choice set. Depending on the service, the seed may be queries, clicks, purchases, or any other activity that might go into the user’s history. A good seed can affect the user’s choice set with a minimal number of “false clicks”, as we describe next. 3. Inject the seed with a vector of false clicks. To pol- lute a user’s history, in most cases we require that the user be signed in to the site. (For some services, pollution can take place even when the user is not Figure 2: Overview of how history pollution can ulti- signed in.) Then, the attacker can use a mechanism mately affect the user’s choice set. to make it appear as though the user is taking action on the Web site for a particular service (e.g., click- ing on links) using a particular attack vector. holding other factors fixed. In particular, we study how an attacker can pollute the user’s history by generating In the following sections, we explore how an attacker can false clicks through cross-site request forgery (XSRF). apply this same procedure to attack the personalization We describe these attacks in the next section. algorithms of three different services: YouTube, Ama- zon, and Google search. 2.2 Pollution Attacks 3 Pollution Attacks on YouTube The objective of a pollution attack is to affect a user’s choice set, given a particular input. In some cases, a In this section, we demonstrate our attack on YouTube1 . user’s choice set appears before the user enters any in- Following the attack steps we described in Section 2, put (e.g., upon an initial visit to the page). In this case, we first model how YouTube uses the watch history of a the attacker’s goal may be to affect a default choice set. YouTube user account to recommend videos by review- Figure 2 shows an overview of the attacker’s goal: the ing the literature [5]. Second, we discuss how to prepare attacker aims to affect the resulting choice set by alter- seed data (i.e., seed videos) to promote target data (i.e., ing the user’s history with false clicks, using cross-site target videos belonging to a specific channel). Third, we request forgery as the attack vector. This attack requires introduce how to inject the seed videos to a YouTube user three steps: account. Finally, we design experiments and quantify the effectiveness of our attack. 1. Model the service’s personalization algorithm. We assume that the attacker has some ability to model the personalization algorithm that the site uses to af- 3.1 YouTube Personalization fect the user’s choice set. In particular, the attacker YouTube constructs a personalized list of recommended must have some idea of how the user’s past history videos based upon the videos a user has previously affects the user’s choice set. This information is of- viewed [5]. YouTube attempts to identify the subset of ten available in published white papers, but in some previously viewed videos that the user enjoyed by con- cases it may require experimentation. sidering only those videos that the user watched for a 2. Create a “seed” to pollute the user’s history. Given long period of time. Typically, YouTube recommends some knowledge of the personalization algorithm videos that other users with similar viewing histories and a goal for how to affect the choice set, the at- 1 A demo video is available at http://www.youtube.com/ tacker must design the seed that is used to affect watch?v=8hij52ws98A. 3 USENIX Association 22nd USENIX Security Symposium 673 have also enjoyed. YouTube tracks the co-visitation re- attacker can convince YouTube that the user watched, but lationship between pairs of videos, which reflects how did not enjoy, these unwanted videos, so their inclusion likely a user who watched a substantial portion of video in ΩS will not lead to additional recommendations. X will also watch and enjoy video Y . In general, there may be more videos with co-visitation relationships than there is display area, so YouTube prioritizes videos with Fabricating Relationships. For some videos, it may high rankings. YouTube will not recommend a video the be difficult to identify a seed set ΩS that recommends all user has already watched. of the elements of ΩT due to lack of co-visitation rela- YouTube displays recommended videos in the sugges- tionships for some of the target elements. Instead, attack- tion list placed alongside with a playing video (e.g., Fig- ers who upload their own content to use as the seed set ure 5) and in the main portion of the screen at the end of can create co-visitation relationships between this con- a video (Figure 1(a)). A suggestion list appearing next to tent and the target set. In particular, an attacker uploads a video typically contains 20–40 suggested videos, two a set of videos, Ω0 , and establishes co-visitation relation- of which are recommended based upon personalization. ships between Ω0 and ΩT through crowd-sourcing (e.g., At the end of a video, YouTube shows an more concise Mechanical Turk or a botnet): YouTube visitors need version of the suggestion list that contains only twelve of only watch a video in Ω0 followed by a video in ΩT . the videos from the full list; these videos may or may not After a sufficient number of viewing pairs, the attacker contain personal recommendations. can use videos in Ω0 as the seed set. As we will show in Section 3.4.1, a relatively small number of viewing pairs 3.2 Preparing Seed Videos suffices. YouTube organizes videos into channels, where each channel corresponds to the set of uploads from a particu- 3.3 Injecting Seed Videos lar user. In our attack, we seek to promote a set of target videos, ΩT , all belonging to the same YouTube channel, To launch the attack and inject seed videos into a C. To do so, we will use an additional set of seed videos, victim’s YouTube watch history, an attacker can harness ΩS , that have a co-visitation relationship with the target XSRF to forge the following two HTTP requests for each videos. By polluting a user’s watch history with videos in video in the seed set: (1) http://www.youtube.com/ ΩS , we can cause YouTube to recommend videos in ΩT . user_watch?plid=&video_id=, There are two ways to obtain ΩS : we can identify videos and (2) http://www.youtube.com/set_awesome? with pre-existing co-visitation relationships to the target plid=&video_id=, where plid videos, or we can create the relationships ourselves. and video id correspond to the values found in the source code of the seed video’s YouTube page. The Existing Relationships. In the simplest version of the first HTTP request spoofs a request from the victim to attack, the attacker identifies existing videos to use as start watching the seed video, and the second convinces the seed set. For example, given a target video set YouTube that the victim watched the video for a long ΩT belonging to channel C, the attacker could con- period of time. Both HTTP requests are required for sider all of the other videos in the channel, C − ΩT , videos in ΩS to trigger the recommendation of videos in as candidate seeds. For every candidate video, the at- ΩT , but only the first HTTP request is needed to prevent tacker checks which videos YouTube recommends when the recommendation of unwanted videos. a fresh YouTube account (i.e., a YouTube account with no history) watches it. YouTube allows its users to view their recommended videos at http://www.youtube. 3.4 Experimental Design com/feed/recommended. If the candidate video trig- gers YouTube to recommend a video in ΩT , then the at- We evaluated the effectiveness of our attack both in con- tacker adds the injected video to seed video set ΩS . trolled environments and against real YouTube users. We In general, this process allows the attacker to identify first validated the the attack in the simplest scenario, seed videos for every target video in ΩT . The attacker where the attack promoted existing YouTube channels cannot yet launch the attack, though, because a YouTube through existing co-visitation relationships. We then video in ΩS may trigger YouTube to also recommend considered the scenario where an attack seemed to up- videos not in ΩT . To address this issue, the attacker can load and promote content from a channel that the attacker simply add these unwanted videos to the seed video set created. Finally, we conducted a small-scale experiment ΩS because YouTube does not recommend videos that to demonstrate the effectiveness of the attack against a the user has already watched. As we will show later, the volunteer set of real YouTube users. 4 674 22nd USENIX Security Symposium USENIX Association 3.4.1 New Accounts 0.3 We first promoted existing YouTube channels by launch- Promotion rate ing our attack against victims with fresh YouTube user 0.2 accounts. This experiment confirms the effectiveness of our approach in the absence of other, potentially counter- 0.1 vailing influences, such as recommendations based on a user’s existing history. 0.0 We began by selecting 100 existing YouTube channels 1 3 5 7 9 11 14 17 20 23 at random from the list of the top 2,000 most-subscribed Target video ID channels published by VidStatsX [19]. For each of the selected YouTube channels, we randomly selected 25 Figure 3: The promotion rate for each of the 25 target videos from the channel as the target video set, used the videos in channel lady16makeup. Two videos were rec- method described in the previous section to identify a ommended in each of the 114 trials. seed video set, and injected the seed videos to a fresh YouTube account. hood for being recommended as a result of a co-visitation We then considered promoting new content by creat- relationship with another video. ing our own YouTube channel and similarly attacking Similar to the experiments with new accounts, we ran- fresh YouTube accounts. Our YouTube channel contains domly selected 15 target videos from channel Onlyy- two 3-minute videos. We selected one of the videos as ouHappycamp, identified a seed set, and injected the a one-element target video set and used the other as the seed videos into the volunteers’ YouTube accounts. Af- seed set. We created a co-visitation relationship by em- ter pollution, the volunteers were asked to use their ac- bedding both videos on a web page and recruiting volun- counts to watch three videos of their choice and report teers to watch both videos sequentially. We obtained 65 the suggestion list displaying alongside each of their and 68 views for our seed and target video respectively. three videos. 3.4.2 Existing Accounts 3.5 Evaluation We studied the effectiveness of our pollution attack using real YouTube user accounts. We recruited 22 volunteers We evaluated the effectiveness of our pollution attacks with extensive pre-existing YouTube watch histories. To by logging in as the victim user and viewing 114 repre- limit the inconvenience to our volunteers, we limited our sentative videos3 . We measured the effectiveness of our study to attempting to promote one moderately popular attack in terms of promotion rate: the fraction of the 114 YouTube channel based upon existing co-visitation rela- viewings when at least one of the target videos was con- tionships. We selected a moderately popular account be- tained within the video suggestion list. Recall that the cause a popular channel may be recommended anyway list contains at most two personalized recommendations (regardless of out attack); conversely, an entirely new (see Section 3.1); we deem the attack successful if one channel requires a certain amount of effort to establish or both of these videos are videos that were promoted as the co-visitation relationships as described above and we a result of a pollution attack. have limited volunteer resources. Based on these parameters, we arbitrarily selected the 3.5.1 New Accounts channel OnlyyouHappycamp. We believe this selection is a reasonable candidate to be promoted using our attack Pollution attacks successfully promoted target videos for several reasons. First, compared to popular chan- from each of the 100 selected existing channels: Each nels, most videos in OnlyyouHappycamp have low view time we injected seed videos for a particular channel, we counts (about 2,000 view counts per video on average) observed the target videos in the suggestion list for each and the number of subscribers to the channel is a simi- of the 114 videos. Since these are fresh accounts, there larly modest 3,552. Both of these are easily achievable is no other history, so our targeted videos always occupy by an attacker at fairly low cost2 . Second, most videos in both of the personalized recommendation slots. OnlyyouHappycamp are 22 minutes long, which makes In addition, we observed the particular target videos them suitable for promotion. As we will explain in Sec- shown in the suggestion video list varied, even when tion 3.5.1, the length of a target video affects its likeli- 3 We attempted to view 150 videos random from a trace of YouTube 2 According to the prices in underground markets such as usage at our institution over the course of several months. Unfortu- freelancer.com and fiverr.com, 40,000 view counts and 10,000 nately, 36 of the videos were no longer available at the time of our subscribers cost $15 and $30 US dollars, respectively. experiment. 5 USENIX Association 22nd USENIX Security Symposium 675 0.6 0.6 0.5 0.5 Promotion rate 0.2 0.3 0.4 Promotion rate 0.2 0.3 0.4 0.1 0.1 0.0 0.0 1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 8 9 11 Target video rank Target video rank (a) Higher ranked video (b) Lower ranked video Figure 4: Distribution of the suggestion slots occupied Figure 5: Suggestion lists before (left) and after (right) by each of the two successfully promoted target videos. a pollution attack against a fresh YouTube user account. The video highlighted in red is our uploaded video. we were viewing the same video using the same victim 1.0 YouTube account. In other words, every target video has a chance to be promoted and shown on the suggestion video list no matter which video a victim plays. Fig- 0.8 ure 3 shows the frequency with which each of the 25 tar- get videos for a representative channel, lady16makeup. Promotion rate In an attempt to explain this variation, we computed (1) 0.6 the Pearson correlation between the showing frequencies and the lengths of the target videos for each channel (ρt ); (2) the Pearson correlation between the showing frequen- 0.4 cies and the view counts of these target videos for each channel (ρcnt ). We found the average Pearson correla- tion values are medium (ρt = 0.54) and moderate (ρcnt = 0.2 0.23), respectively. This suggests that both the length and view count of a target video influence its recommenda- 200 500 1000 2000 5000 10000 tion frequency, but the length of a target video is a more Watch history significant factor. Figure 6: Promotion success rates for 10 real YouTube Since screen real estate is precious, and users typically user accounts with varying watch history lengths. focus on the first few items of a list, we report on the po- sition within the suggested video lists that our targeted videos occupied when they were promoted. We observed small that we did not lead YouTube to conclude that our that the two target videos were usually placed back-to- content was, in fact, universally popular. Figure 5 shows back on the suggestion list. Figure 4 shows that YouTube a sample screenshot comparing the suggestion lists from usually placed our target videos among the top few spots a victim account and another, non-exploited fresh ac- of a victim’s suggestion list: in our tests with new ac- count. Finally, we found that one of our target videos counts, the target videos were always recommended and occupied the top suggestion slot while viewing 80 out of placed on the top 12, which meant they also appeared the 114 test videos. at the end of viewed videos. This finding is particu- larly significant because it implies that our target videos 3.5.2 Existing Accounts are shown even if a victim finishes watching a YouTube video on a third-party website (which typically embeds Our attacks were somewhat less successful on real only the view-screen portion of the YouTube page, and YouTube accounts. We found that 14 out of the 22 volun- not the full suggestion list). teer YouTube users reported that at least one of our tar- Our attacks were similarly completely successful in get videos from channel OnlyyouHappycamp appeared promoting newly uploaded content. As a control, we in the suggestion list during each of their three video also signed in as non-polluted fresh YouTube accounts viewings, a 64% promotion rate. and, unsurprisingly, did not find any of our new con- To understand why we were able to exploit some ac- tent among the videos in the suggestion list. In other counts and not others, we asked our volunteers to share words, the videos were recommended exclusively be- their YouTube watch histories. Ten of our volunteers cause of our attacks; our experiments were sufficiently shared their histories with us and allowed us to sign in to 6 676 22nd USENIX Security Symposium USENIX Association their YouTube accounts to conduct a further study. The are not public, but many previous studies have explored number of videos in the watch histories of the ten vol- aspects of personalized search [2,4,6,7,9,10,14–18]. We unteers ranged from a few hundred to tens of thousands. describe two classes of personalization algorithms: con- Figure 6 shows the relationship between the number of textual personalization and persistent personalization. watched videos in a watch history and the number of According to recent reports [11,12], many search engines times that at least one of our target videos is displayed including Google, Bing, and Yahoo! apply both types of along with a playing video. While there appears to be an personalization. intuitive decreasing trend (i.e., the longer the history an Contextual personalization constructs a short-term account has the more resistant it is to pollution), there are user profile based on recent searches and clicks- obvious outliers. For example, one account with almost through [4, 16]. When a user searches for “inexpen- 3,500 previous viewings in its history succumbed to our sive furniture” followed by “maternity clothes,” Google’s attacks almost 80% of the time. contextual personalization algorithm typically promotes Consistent with the Pearson coefficients reported ear- search results that relate to “inexpensive maternity lier, we found that the success of our attacks depends on clothes” for the next few searches (we provide an anal- the rankings and lengths of the videos that are otherwise ysis of precisely how long this effect lasts in Ap- suggested based upon a user’s history. In particular, we pendix A.2). In contrast, persistent personalization uses observed that the majority of the videos recommended the entire search history—as opposed to only recent to users for whom our attacks have low promotion rates searches—to develop a user profile [9, 15]. Personaliza- have longer lengths and more view counts than our tar- tion that occurs over the longer term may not affect a get videos, while the videos that YouTube recommends user’s search results as dramatically, but can have longer- based on the watch history of the user with 3,500 previ- lasting effects for the results that a user sees. For exam- ous viewings have shorter lengths than our target videos ple, searching for “Egypt” using different accounts may (though they generally have higher view counts than our result in two distinct result sets: one about tourism in targets). Egypt and one related to the Arab Spring. Although we believe our attack demonstrates that YouTube’s personalization mechanism is subject to ex- 4.2 Identifying Search Terms ploit, the persistence of the attack effects is unclear. In our experiments, volunteers watched arbitrary YouTube Given the differing underlying algorithms that govern videos right after being attacked, but we believe our pol- contextual and persistent personalization, an attacker lution attacks on YouTube are likely to last for some needs to select different sets of seed search terms depend- time. Although YouTube does not explicitly disclose ing on the type of attack she hopes to launch. how time factors into their recommendation system (if at all) [5], analysis of volunteers’ watch histories indi- Contextual Personalization. For the contextual per- cates that a YouTube video that was watched as long as sonalization attack, the keywords injected into a user’s two weeks prior is still used for generating recommended search history should be both relevant to the promot- videos. ing keyword and unique to the website being promoted. In particular, the keywords should be independent from 4 Google Personalized Search other websites that have similar ranking in the search re- sults, to ensure that only the target website is promoted. In this section, we show how history pollution attacks Presumably, an attacker promoting a specific website is can be launched against Google’s search engine4 . The familiar with the website and knows what keywords best goal of our attack is to promote a target webpage’s rank meet these criteria, but good candidate keywords are also in the personalized results that Google returns for an ar- available in a website’s meta keyword tag. While Google bitrary search term by injecting seed search terms into a no longer incorporates meta tags into their ranking func- victim’s search history. tion [3], the keywords listed in the meta keyword tag still provide a good summary of the page’s content. 4.1 Search Personalization Persistent Personalization. Launching a persistent Search personalization customizes search results using personalization attack requires a different method of ob- information about users, including their previous query taining keywords to inject. In this case, the size of the terms, click-through data and previously visited web- keyword set should be larger than that used for a contex- sites. The details of Google’s personalization algorithms tual attack in order to have a greater effect on the user’s 4 A demo video is available at http://www.youtube.com/ search history. Recall that contextual attacks only affect watch?v=73E5CLFYeu8. a user’s current session, while persistent attacks pollute 7 USENIX Association 22nd USENIX Security Symposium 677 a user’s search history in order to have a lasting effect on represents a conservative lower bound on the effective- the user’s search results. An attacker can determine suit- ness of the attack, as any individual website owner could able keywords using the Google AdWords tool, which engineer the content of their site to tailor it for promotion takes as an input a search term and URL and produces a through search history pollution. list of about one hundred related keywords. Ideally, an attacker could pollute a user’s search history with each 4.4.1 Contextual Pollution of these terms, but a more efficient attack should be ef- fective with a much smaller set of keywords. We deter- We started by scraping 5,671 shopping-related keywords mined that an attacker can safely inject roughly 50 key- from made-in-china.com to use as search terms. We words a minute using cross-site request forgery; more then entered each of these terms into Google one-by-one rapid search queries are flagged by Google as a screen- to obtain the top 30 (un-personalized) search results for scraping attack. For this study, we assume an attacker each. Since some of our search terms are related, not all can inject at most 25 keywords into a user’s profile, but of these URLs are unique. Additionally, we cannot hope the number of keywords can increase if the user stays on to improve the URLs that are already top-ranked for each a webpage for more than 30 seconds. Not all keyword of the search terms. We obtained 151,363 URLs whose lists that AdWords returns actually promote the target ranking we could hope to improve. website. The effectiveness of this attack likely depends Because we cannot manually inspect each of these on several factors, including the user’s current search his- websites to determine appropriate seed search terms, we tory. In Section 4.5, we evaluate the effectiveness of this instead focused a subset that include the meta keyword attack under different conditions. tag. For the approximately 90,000 such sites, we ex- tracted the meta keywords or phrases from the website. 4.3 Injecting Search Terms Many of these keywords are generic and will appear in a wide variety of websites. To launch the attack, we re- As with the pollution attacks on YouTube, the attack on quire keywords that are unique to the website we wish to Google’s personalized search also uses XSRF to inject promote (at least relative to the other URLs returned in the seeds. For example, an attacker can forge a Google response to the same query), so we ignored any keywords search by embedding https://www.google.com/ that were associated with multiple URLs in the same set search?hl=en&site=&q=usenix+security+2013 of search results. into an invisible iframe. A Web browser will issue This procedure ultimately yielded 2,136 target URLs an embedded HTTP request, even if Google search spanning 1,739 different search terms, for which we had response has an enabled X-Frame-Option header. a set of 1–3 seed keywords to try to launch a contextual Injecting search terms into a Google user’s account pollution attack. The average search term has 1.23 results affects the search results of the user’s subsequent whose ranking we tried to improve. Figure 11 in the Ap- searches. The number and set of search terms to inject pendix shows the distribution of the original rankings for differs depending on whether an attacker can execute a each of these target websites; the distribution is skewed contextual or persistent personalization attack. toward highly ranked sites, perhaps because these sites take care in selecting their meta tag keywords. 4.4 Experimental Design 4.4.2 Persistent Pollution To cleanly study the effects of our proposed attacks on contextual and persistent search personalization, we con- Once again, we begin by selecting 551 shopping-related ducted most of our experiments using Google accounts search terms and perform Google searches with each of with no search history. To validate whether our results the search terms to retrieve the top 30 search results. As apply to real users, we also conducted a limited number opposed to the contextual attack, where we search for of tests using accounts that we constructed to mimic the keywords that differentiate the results from one another, personae of real users. we aim to determine search terms that will be associated To quantify the effectiveness of our attack in general, with the website and search-term pair for the long term. we must select an unbiased set of target web pages whose As described in Section 4.2, we use a tool provided by rankings we wish to improve. We built two test corpora, Google AdWords to obtain a set of keywords that Google one for attacks on contextual personalization, and one for associates with the given URL and search term. Con- attacks on persistent personalization. We attempted to structing related keyword lists for each of the 29 search promote existing web sites using only their current con- returns (again excluding the top hit, which we cannot tent and link structure; we did not perform any SEO on hope to improve) and 551 search terms yields 15,979 dis- websites before conducting the attacks. We believe this tinct URLs with associated lists of keywords. 8 678 22nd USENIX Security Symposium USENIX Association For each URL, we select 25 random keywords from ceeded 4.3% of the time, and moving a tenth-ranked the AdWords list for 25 distinct trials. If a trial improved URL to a higher-ranked position succeeded 22.7% of the a URL’s ranking, we then test the persistence of the at- time. These results make sense, because second-ranked tack by performing 20 subsequent queries, each with sites can only move into the top-ranked position, whereas a randomly chosen set of Google trending keywords. sites that are ranked tenth can move into any one of nine These subsequent queries help us verify that the URL higher spots. promotion is not just contextual, but does not vanish To illustrate this effect and illuminate how far each when a user searches other content. If after all 25 trials webpage was promoted, Figure 7 shows the PDF of an we find no keyword sets that promote the URL’s ranking improved webpage’s rank after contextual history pol- and keep it there for 20 subsequent searchers, we deem lution, based upon its position in the non-personalized this URL attempt a failure. If multiple keyword sets suc- search results. We observed that contextual pollution was ceed, we select the most effective (i.e., the set of 25 key- able to promote most webpages by one or two spots, but words that induces the largest ranking improvement) trial some low-ranking webpages were also promoted to very to include in the test set. high ranks. Similarly, Figure 8 shows the distributions for each result ranking for those websites whose rankings 4.5 Evaluation were improved by a persistent history pollution attack. Here, the distributions appear roughly similar (although In this section, we quantify the effectiveness of search the absolute probability of success is much lower), but history pollution with attacks that aimed to promote the it is difficult to draw any strong conclusions due to the target websites identified in the previous section. To small number of promoted sites of each rank for either scope our measurements, we consider the effectiveness class of attack. of the attacks only for the set of search terms that we identify; it is quite possible, of course, that our pollution attacks also affect the rankings of the targeted URLs for other search terms. When measuring the effectiveness of our attack, we 4.5.2 The Next Tier use two different criteria, depending upon a website’s original position in the search results. In the case of URLs that are already in the first ten search results but The remaining 1,290 test websites for the contextual at- not ranked first, we consider the pollution attack success- tack were initially on the second or third page of search ful if it increases the ranking of a URL at all. For URLs results. By polluting a user’s search history with the subsequent pages, we consider the attack successful only unique meta tag keywords associated with each site, we if the attack moves the URL to the first page of search promoted 358 of them (28%) to the front page. Fig- results, since improved ranking on any page that is not ure 7(j) shows that these websites were more likely to the first page is unlikely to have any utility. appear at the top of the results than those pages that were initially at the bottom of the first page. We suspect this phenomenon results from the choice of keywords used 4.5.1 Top-Ranked Sites in pollution: because their original rankings were low, the pollution attack requires a distinguishing keyword to For the 2,136-page contextual attack test corpus, of the move one of the webpages to the front page at all. If 846 pages that appeared on the front page prior to our such a keyword can move a search result to the first page, attack, we improved the ranking of 371 (44%). The per- it might also be a good enough keyword to promote the sistent attack was markedly less effective, with only 851 page to a high rank on the first page, as well. (17%) of the 4,959 test cases that originally appeared on the first page of the search results had ranking im- The results from the persistent test set are markedly provements surviving the persistence test (i.e., they re- different. Figure 8(j) shows that sites starting on the sec- mained promoted after 20 random subsequent queries). ond or third page are unlikely to end up at the very top In both cases, however, the probability of success de- of the result list due to a persistent history attack: Only pends greatly on the original ranking of the targeted 80 (less than 1%) of the 11,020 attacks that attempted URL. For example, promoting a second-ranked URL to promote a website appearing on the 2nd or 3rd page to the top-ranked position for contextual personalization of results was successful in moving it to the front page succeeded 1.1% of the time, whereas promoting a tenth- (and keeping it there). This results shows that persis- ranked URL by at least one position succeeded 62.8% tent search history attacks are generally best launched for of the time. Similarly, for attacks on persistent person- sites that are already highly ranked, as opposed to con- alization, moving a second-ranked URL to the top suc- textual attacks, which can help even lower-ranked sites. 9 USENIX Association 22nd USENIX Security Symposium 679 Promotion rate Promotion rate Promotion rate Promotion rate Promotion rate 0.6 0.6 0.6 0.6 0.6 0.4 0.4 0.4 0.4 0.4 0.2 0.2 0.2 0.2 0.2 0.0 0.0 0.0 0.0 0.0 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 Personalized rank Personalized rank Personalized rank Personalized rank Personalized rank (a) Non-personalized rank (b) Non-personalized rank (c) Non-personalized rank (d) Non-personalized rank (e) Non-personalized rank =2 =3 =4 =5 =6 Promotion rate Promotion rate Promotion rate Promotion rate Promotion rate 0.6 0.6 0.6 0.6 0.6 0.4 0.4 0.4 0.4 0.4 0.2 0.2 0.2 0.2 0.2 0.0 0.0 0.0 0.0 0.0 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 Personalized rank Personalized rank Personalized rank Personalized rank Personalized rank (f) Non-personalized rank (g) Non-personalized rank (h) Non-personalized rank (i) Non-personalized rank = (j) Non-personalized rank =7 =8 =9 10 > 10 Figure 7: Promotion rates of promoted Google search rankings for successful contextual history pollution attacks. 4.5.3 Real Users previous purchase, browsing and searching behavior of the user. Amazon product recommendations consider We also evaluate the effectiveness of pollution attacks each of these three activities individually and explicitly on ten volunteers’ accounts with extensive pre-existing labels its recommendations according to the aspect of the search histories. We find that, on average, 97.1% of our user’s history it used to generate them. We focused on the 729 previously successful contextual attacks remain suc- personalized recommendations Amazon generates based cessful, while only 77.78% of the persistent pollution at- on the browsing and searching activities of a customer tacks that work on fresh accounts achieve similar suc- because manipulating the previous purchase history of a cess. We believe that users’ search histories sometimes customer may have unintended consequences. interfere with the attacks, and that user history inter- feres more with the attacks on persistent personalization. Contextualized attacks rely only on a small set of re- 5.1 Amazon Recommendations cent search terms to alter the personalized search results, which is unlikely to be affected by a user’s search history. Amazon displays five recommendation lists on a cus- In contrast, pollution attacks against persistent personal- tomer’s homepage that are ostensibly computed based on ization rely on more of a user’s search history. If relevant the customer’s searching and browsing history. Four of keywords are already present in a user’s search history, these lists are derived from the products that the customer keyword pollution may be less effective. In any event, has recently viewed (view-based recommendation); the both attacks are relatively robust, even when launched fifth is based on the latest search term the customer en- against users with long search histories. tered (search-based recommendation). For each of the view-based recommendation lists, Amazon uses relation- ships between products that are purchased together to 5 Pollution Attacks on Amazon compute the corresponding recommended products; this concept is similar to the co-visitation relationship that Of the three services, Amazon’s personalization is per- YouTube uses to promote videos. For the recommenda- haps the most evident to the end user. On one hand, this tion list that is computed based on the latest search term makes pollution-based attacks less insidious, as they will of a customer, the recommended products are the top- be visible to the observant user. On the other, of the three ranked results for the latest search term. services, Amazon has the most direct monetization path, In contrast to the types of personalization used for since users may directly purchase the goods from Ama- YouTube and Google Search, Amazon’s personalization zon. Therefore, exploitation of Amazon’s personaliza- is based on history that maintained by the user’s web tion may be profitable to an enterprising attacker. browser, not by the service. Because customers fre- Amazon tailors a customer’s homepage based on the quently brows Amazon without being signed in, both the 10 680 22nd USENIX Security Symposium USENIX Association 0.30 0.30 0.30 0.30 0.30 Promotion rate Promotion rate Promotion rate Promotion rate Promotion rate 0.20 0.20 0.20 0.20 0.20 0.10 0.10 0.10 0.10 0.10 0.00 0.00 0.00 0.00 0.00 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 Personalized rank Personalized rank Personalized rank Personalized rank Personalized rank (a) Non-personalized rank (b) Non-personalized rank (c) Non-personalized rank (d) Non-personalized rank (e) Non-personalized rank =2 =3 =4 =5 =6 0.30 0.30 0.30 0.30 0.30 Promotion rate Promotion rate Promotion rate Promotion rate Promotion rate 0.20 0.20 0.20 0.20 0.20 0.10 0.10 0.10 0.10 0.10 0.00 0.00 0.00 0.00 0.00 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 Personalized rank Personalized rank Personalized rank Personalized rank Personalized rank (f) Non-personalized rank (g) Non-personalized rank (h) Non-personalized rank (i) Non-personalized rank = (j) Non-personalized rank =7 =8 =9 10 > 10 Figure 8: Promotion rates of promoted Google search rankings for successful persistent history pollution attacks. latest viewed products and search term of the customer Extractor”, an attacker can use XSRF to inject the search are stored in session cookies on the user’s browser rather term “Breville BJE200XL” to replace an Amazon cus- than in profiles on Amazon servers. tomer’s latest search term. 5.2 Identifying Seed Products and Terms 5.3 Injecting Views and Searches Because Amazon computes the view and search-based As with the attacks on the previous two services, the at- recommendation lists separately, the seed data required tacker embeds the Amazon URLs of the desired seed exploit each list must also be different. items or search queries into a website that the victim’s browser is induced to visit with XSRF. For example, if Visit-Based Pollution. To promote a targeted product one seed search terms is “Coffee Maker”, the seed URL in a view-based recommendation list, an attacker must would be something like http://www.amazon.com/s/ identify a seed product as follows. Given a targeted prod- ?field-keywords=Coffee+Maker. Similarly, an at- uct that an attacker wishes to promote, the attacker visits tacker could embed the URL of a seed product into an the Amazon page of the product and retrieves the related invisible img tag as the src of the image. When a victim products that are shown on Amazon page of the targeted visits the attacker’s website, Amazon receives the request product. To test the suitability of these related products, for that particular query or item and customizes the vic- the attacker can visit the Amazon page of that product tim’s Amazon website based on that search. and subsequently check the Amazon home page. If the targeted product appears in a recommendation list, the URL of the candidate related product can serve as a seed 5.4 Experiment Design to promote the targeted product. To evaluate the effectiveness of the pollution attack against, we conducted two experiments. The first exper- Search-Based Pollution. To promote a targeted prod- iment measured the effectiveness of our attack when tar- uct in a search-based recommendation list, it suffices to geted toward popular items across different categories of identify an appropriate search term. If automation is de- Amazon products. The second quantified the effective- sired, an attacker could use a natural language toolkit to ness of our attack on randomly selected, mostly unpopu- automatically extract a candidate keyword set from the lar Amazon products. targeted product’s name. Any combination of these key- words that successfully isolates the targeted product can 5.4.1 Popular Products be used as the seed search term for promoting the tar- geted product. For example, to promote product “Bre- Amazon categorizes sellers’ products into 32 root cat- ville BJE200XL Compact Juice Fountain 700-Watt Juice egories. To select products from each category, we 11 USENIX Association 22nd USENIX Security Symposium 681 View based Search based 1.0 Promotion rate 0.8 0.6 0.4 0.2 0.0 .C ov oo s Pe ts.S me g Sp era tch ry an y .C pr D ty t.S ew nt Vi t .C g Ar e.I che Be ces p g rs El cie es d d am W we s S ms so lo ies c ftw s ar ie ks .S V er C sor cs ne ic.A am e ts S ho s ffi m tc s .P .Fo n ag or e G M zines re d T O h to om ust oys utd oes ce tro ific M deo mo are s. S lb es n H K pli Babs e lie pu rial. .Gaoor f e n Aunal hin up in ro o Ac o um .P e O ur .Ki ent a ie Ap uct s G tiv ce et he M ess ar .G m um e ds s.T ts m n. au a l to s ni ra ov ini ry o str ard Ac ec nt t B ce Hl.In .G J o . o e n . G sica aw u or om it . u .L ift .P C M atio te lth ho In ea P l.P ro H el C C Figure 9: Promotion rates across Amazon categories. scraped the top 100 best-selling products in each cate- attacks produced similar promotion rates across all cate- gory in January 2013 and launched a separate attack tar- gories, about 78% on average. Two categories had sig- geting each of these 3,200 items. nificantly lower propotion rates: Gift-Cards-Store and Movies-TV (achieving 5% and 25%, respectively). To understand why these categories yielded lower pro- 5.4.2 Random Products motion rates, we analyzed the top 100 best selling prod- To evaluate the effectiveness of the polution attack for ucts for each category. For Gift-Cards-Store, we found promoting arbitrary products, we also selected prod- that there were two factors that distinguish gift cards ucts randomly. We downloaded a list of Amazon Stan- from other product types. First, the gift cards all had dard Identification Number (ASIN) [1] that includes similar names; therefore, using the keywords derived 75,115,473 ASIN records. Because each ASIN repre- from the product name resulted in only a small number sents a Amazon product, we randomly sampled ASINs of specific gift cards being recommended. Second, we from the list and constructed a set of 3,000 products cur- found that searching any combination of keywords ex- rently available for sale. For every randomly selected tracted from the product names always caused a promo- product in the list, we recorded the sale ranking of that tion of Amazon’s own gift cards, which may imply that product in its corresponding category. it is more difficult to promote product types that Amazon competes with directly. Further investigation into the Movies-TV category re- 5.5 Evaluation vealed that Amazon recommends TV episodes differ- ently. In our attempts to promote specific TV episodes, Because Amazon computes search and visit-based rec- we found that Amazon recommends instead the first or ommendations based entirely upon the most recent his- latest episode of the corresponding TV series or the en- tory, we can evaluate the effectiveness of the pollution tire series. Because we declared a promotion success- attack without using Amazon accounts from real users. ful only if the exact ASIN appears in the recommenda- Thus, we measured the effectiveness of our attack by tion lists, these alternate recommendations are consid- studying the success rate of promoting our targeted prod- ered failures. These cases can also be considered suc- ucts for fresh Amazon accounts. cessful because the attack caused the promotion of very similar products. Therefore, we believe that for all cat- 5.5.1 Promoting Products in Different Categories egories except for Gift-Cards-Store, an attacker has a significant chance of successfully promoting best-selling To evaluate the effectiveness of the pollution attack for products. each targeted product, we checked whether the ASIN of the targeted product matches the ASIN of an item in the 5.5.2 Promoting Randomly Selected Products recommendation lists on the user’s customized Amazon homepage. We launched pollution attacks on 3,000 randomly se- Figure 9 illustrates the promotion rate of target prod- lected products. We calculated the Cumulative Success ucts in each category. The view-based and search-based Rate of products with respect to their rankings. The Cu- 12 682 22nd USENIX Security Symposium USENIX Association a complex network infrastructure, which may consist of hundreds of search-indexed websites (preferably with non-trivial reputations at established search engines) to coordinate and form a link farm [20]. These infrastruc- tures not only require a considerable amount of money to build and maintain, but also take time to mature and reach their full effectiveness [8]. By contrast, launching a search history pollution attack is significantly easier. We showed in Section 4 that a user’s personalized search results can be manipulated simply by issuing crafted search queries to Google. Without requiring any external support, the entire process happens instantly Figure 10: Cumulative promotion rates across varying while the user is visiting the offending Web page. Al- product ranks for different Amazon pollution attacks. though our attack targets individual search users (i.e., the polluted result is only visible to individual victims), it by mulative Success Rate for a given range of product rank- no means limits the scale of the victim population, espe- ings is defined as the ratio of the number of successfully cially if an exploit is placed on a high-profile, frequently promoted products to the number of target products in visited website. that range. Figure 10 shows the cumulative promotion rate for dif- ferent product rankings for the two different types of pol- 7 Discussion lution attacks. As the target product decreases in popu- larity (i.e., has a higher ranking position within its cat- Our current study has several limitations. Most notably, egory) pollution attacks become less effective, but this the scale of our experiments is modest, but because we phenomenon reflects a limitation of Amazon recommen- typically randomly select the target items, we believe that dation algorithms, not our attack. Products with low the results of our experiments are representative, and that rankings might not be purchased as often; as a result, they illustrate the substantial potential impacts of pollu- they may have few and weak co-visit and co-purchase re- tion attacks. Similarly, our specific pollution attacks are lationships with other products. Our preliminary inves- fragile, as each service can take relatively simple steps to tigation finds that products which rank 2,000 or higher defend againt them. within their category have at least a 50% chance of be- A possible defense against pollution attacks arises ing promoted by a visit-based pollution attack, and prod- from the fact that cross-site request forgery can be ucts with rankings 10,000 and higher have at least a 30% stopped if requests to a website must carry tokens issued chance to be promoted using search-based attacks. by the site. Enforcing this constraint, however, also pre- vents information and behaviors at third-party sites from 6 Related Work being harvested for personalization and hampers the cur- rent trend of increasing the scope of data collection by To the best of our knowledge, the line of work most websites for improved personalization. One short-term closely related to ours is black-hat search engine op- effect from this study may be that (some) websites will timization (bSEO). Although sharing a common goal begin to consider the tradeoffs between the security and as search history pollution—illicitly promoting website benefits of personalization. rankings in search results—bSEO follows a completely YouTube in particular uses two separate HTTP re- different approach, exploiting a search engine’s reliance quests to track a YouTube’s user viewing activity that on crawled Web content. Blackhat SEO engineers the are independent from the act of streaming of the video. content of and links to Web pages to obtain a favorable One straightforward defense against pollution attacks is ranking for search terms of interest [8]. Thus, techniques to monitor the time between the arrivals of the two HTTP that address bSEO are unlikely to be effective against requests. If YouTube finds the interval is substantially pollution attacks. On the other hand, because bSEO less than the length of the video, it could ignore the sig- targets the general indexing and ranking process inside nal. An attacker can still always inject a short video or search engines, any successfully promoted website will control the timing of the HTTP requests in an effort to be visible to all search engine users, potentially signifi- bypass such a defense mechanism. We did notice that cantly boosting the volume of incoming traffic. Yet, ef- an injected short video can be used to promote multi- fective bSEO campaigns typically involve support from ple longer videos; for example, watching a single two- 13 USENIX Association 22nd USENIX Security Symposium 683 second video5 causes YouTube to recommend several International ACM SIGIR Conference on Research and long videos. Development in Information Retrieval (2011). [3] C UTTS , M. Does Google use the “keywords” meta tag? http://www.youtube.com/watch?v=jK7IPbnmvVU. 8 Conclusion [4] DAOUD , M., TAMINE -L ECHANI , L., AND B OUGHANEM , M. A session based personalized In this paper, we present a new attack on personalized search using an ontological user profile. In Proceed- services that exploits the fact that personalized services ings of The 24th Annual ACM Symposium on Applied use a user’s past history to customize content that they Computing (2009). present to the user. Our attack pollutes a user’s history [5] DAVIDSON , J., L IEBALD , B., L IU , J., NANDY, P., by using cross-site request forgery to stealthily inject and VAN V LEET, T., G ARGI , U., G UPTA , S., H E , Y., L AM - execute a set of targeted browsing activities in the user’s BERT, M., L IVINGSTON , B., AND S AMPATH , D. The browser, so that when the user subsequently accesses the YouTube video recommendation system. In Proceedings associated service specific content is promoted. 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While we demonstrate pollution at- ceedings of the 11th ACM International Conference on tacks on only YouTube, Google, and Amazon, we believe Information and Knowledge Management (2002). that our methods are general and can be widely applied to [8] L U , L., P ERDISCI , R., AND L EE , W. Surf: detecting services that leverage personalization technologies, such and measuring search poisoning. In Proceedings of the as Facebook, Twitter, Netflix, Pandora, etc. The attacks 18th ACM Conference on Computer and communications we present here are just the first few examples of poten- security (2011). tially many possible attacks on personalization. With in- [9] M ATTHIJS , N., AND R ADLINSKI , F. Personalizing Web creasingly complex algorithms and data collection mech- search using long term browsing history. In The Fourth anisms aiming for ever higher financial stakes, there are ACM International Conference on Web Search and Data bound to be vulnerabilities that will be exploited by moti- Mining (2011). vated attackers. The age of innocence for personalization [10] Q IU , F., AND C HO , J. Automatic identication of user is over; we must now face the challenge of securing it. interest for personalized search. In Proceedings of the 15th ACM International Conference on the World Wide Web (2006). Acknowledgments [11] S EARCH E NGINE L AND. Bing results get localized & personalized. http://searchengineland.com/ This research was supported in part by the National bing-results-get-localized-personalized- Science Foundation under grants CNS-1255453, CNS- 64284. 1255314, CNS-1111723, and CNS-0831300, and the Of- [12] S EARCH E NGINE L AND. Google now personalizes fice of Naval Research under grant no. N000140911042. everyones search results. http://searchengineland. Any opinions, findings, and conclusions or recommenda- com/google-now-personalizes-everyones- tions expressed in this material are those of the authors search-results-31195. and do not necessarily reflect the views of the National [13] S HIFLETT, C. Cross-site request forgeries. Science Foundation or the Office of Naval Research. http://shiflett.org/articles/cross-site- request-forgeries, 2004. References [14] S IEG , A., M OBASHER , B., AND B URKE , R. Web search personalization with ontological user profiles. In Pro- [1] Amazon.com product identifiers. http://archive. ceedings of the 16th ACM Conference on Conference on org/details/asin_listing. Information and Knowledge Management (2007). [2] B ENNETT, P. N., R ADLINSKI , F., W HITE , R. W., AND [15] S ONTAG , D., C OLLINS -T HOMPSON , K., B ENNETT, Y ILMAZ , E. Inferring and using location metadata to per- P. N., W HITE , R. W., D UMAIS , S., AND B ILLERBECK , sonalize web search. In Proceedings of the 34th Annual B. Probabilistic models for personalizing Web search. In Proceedings of the 5th ACM International Conference on 5 http://www.youtube.com/watch?v=UPXK3AeRvKE Web Search and Data Mining (2012). 14 684 22nd USENIX Security Symposium USENIX Association [16] S RIRAM , S., S HEN , X., AND Z HAI , C. A session-based Number of webpages 120 search engine. In Proceedings of the 27th Annual Inter- national ACM SIGIR Conference on Research and Devel- 80 opment in Information Retrieval (2004). [17] TAN , C., G ABRILOVICH , E., AND PANG , B. To each 40 his own: personalized content selection based on text comprehensibility. In Proceedings of the 5th ACM In- 0 ternational Conference on Web Search and Data Mining 2 4 6 8 11 14 17 20 23 26 29 (2012). [18] T EEVAN , J., D UMAIS , S. T., AND H ORVITZ , E. Person- Non−personalized rank alizing search via automated analysis of interests and ac- Figure 11: Google’s original rank distribution for the tivities. In Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in 2,136 webpages whose ranking we attempt to improve Information Retrieval (2005). with contextual search history pollution. [19] V ID S TATS X. Youtube channel, subscriber, & video statistics. http://vidstatsx.com/. the original search term “watch”. The keywords injected [20] W U , B., AND DAVISON , B. D. Identifying link farm by the pollution attack differ, however, and are “China” spam pages. In Proceedings of the Special Interest Tracks and “China wholesale” respectively. For the persistent and Posters of the 14th ACM International Conference on attacks, we were successful in promoting at least one re- the World Wide Web (2005). turned website for 247 out of the 551 search terms. Figure 12 shows the competition level distribution for both types of attacks. Figures 12(a) and 12(b) corre- A Appendix spond to the 1,740 search terms associated with our en- tire contextual test corpus and the 606 search terms for Here we provide more details regarding the actual exploit which there was a website we could promote. Like- and test corpora for the search personalization attack. wise, Figures 12(c) and 12(d) plot the competitiveness of the search terms for the 551 tested and the 247 suc- A.1 Search Term Variance cessful persistent pollution attacks. Although the distri- butions are different between test corpora, in both cases, As with the various product categories on Amazon, it is the distributions suggest there is no obvious correlation reasonable to expect that the effectiveness of search his- between search term competition or value and the like- tory pollution depends on the value of the search term lihood of being able to launch a search history pollution being polluted. In other words, just as Amazon tightly attack. controls the gift cards it recommends, it might be the case that a website cannot be promoted in Google’s search re- sults as easily for a highly competitive search term, such A.2 Robustness as “laptop”, as it can for relatively uncontested search terms. To obtain an estimate of the value of differ- Because a contextual history pollution attack uses only ent search terms, we again turned to Google’s AdWords a few recent search history entries to promote a website, Keyword Tool. The tool provides a function that asso- the lifetime of this attack is limited to the period when ciates a given search term with a level of competition. Google’s personalization algorithm considers this con- The competition level is a measure of how expensive textual information. We empirically determine Google’s it would be for URL to consistently pay enough to be timeout threshold by injecting sets of contextual key- ranked at the top of the list of advertisers for a particular words into a Google search profile and then pausing search term. Competition level is expressed as a value Google’s history collection. We then search alternatively from 0 to 1, with 0 having no competition and 1 having for two distinct search terms—one that we know is af- fierce competition. fected by the injected keywords, and another we know is Recall that out of the 2,136 webpages that we at- not. We continue to search for these two terms, recording tempted to promote using a contextual pollution attack, and time stamping all the search returns. 729 were successful. It is important to note that some Our analysis of many such tests with different sets of the promoted results were for the same initial search of search terms indicates that Google appears to en- terms. Therefore, the number of search terms asso- force a ten-minute threshold on context-based personal- ciated with the webpages are 1,740 and 606, respec- ized search, which thereby limits the scope of the con- tively. As an example, we attempted to promote both textual pollution attack. Similarly, there are limits on made-in-china.com and DHgate.com with respect to how many different searches can be conducted before the 15 USENIX Association 22nd USENIX Security Symposium 685 120 300 100 200 300 120 200 Frequency Frequency Frequency Frequency 80 80 100 40 40 0 0 0 0 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 0.2 0.4 0.6 0.8 1.0 Competition level Competition level Competition level Competition level (a) Entire corpus, contextual (b) Successful attacks, contextual (c) Entire corpus, persistent (d) Successful attacks, persistent Figure 12: Distribution of search-term competition levels. injected context is no longer used to personalize subse- long, we identified a set of 100 webpages and search quent queries. Our initial testing indicates that person- terms on which we launch a successful persistent pol- alization falls off after the fourth search. Hence, we lution attack. We then inject additional randomly se- conclude that the pollution attack can last for at most lected trending keywords one-by-one and continually four subsequent queries or ten minutes, whichever comes check whether the promotion remains. 72% of the web- first. sites remain promoted after 60 additional keywords, indi- Our testing of persistent attacks shows that if a web- cating that, when successful, persistent pollution attacks page remains promoted after several search terms, it will are likely to remain effective for quite some time. remain promoted for a long time. To determine how 16 686 22nd USENIX Security Symposium USENIX Association