--- type: Article title: "Spider-Scents: Grey-box Database-aware Web Scanning for Stored XSS" resource: "https://www.usenix.org/conference/usenixsecurity24/presentation/olsson" tags: [article, webseclist-reference, en, usenix-org] generated: by: webseclist-refs/1 at: "2026-08-19T16:21:26+00:00" status: stable stale_after: 2027-08-19 sources: - id: original resource: "https://www.usenix.org/conference/usenixsecurity24/presentation/olsson" title: "Spider-Scents: Grey-box Database-aware Web Scanning for Stored XSS" author: Eric Olsson, Benjamin Eriksson, Adam Doupé, Andrei Sabelfeld also_at: - "https://www.usenix.org/system/files/usenixsecurity24-olsson.pdf" - "https://www.usenix.org/system/files/usenixsecurity24-appendix-olsson.pdf" - "https://www.usenix.org/system/files/sec24summer-prepub-286-olsson.pdf" - "https://www.usenix.org/system/files/usenixsecurity24_slides-olsson.pdf" authors: - Eric Olsson - Benjamin Eriksson - Adam Doupé - Andrei Sabelfeld canonical_url: "" cited_by: - "2024.md:150" commit: "" content_sha256: 4af5f0bf1cd08a99071d42fe3f0d08d9defc15d92a62e53d07d7cbcd21210889 depth: full depth_reason: default kind: article language: en licence: unknown original_url: "https://www.usenix.org/conference/usenixsecurity24/presentation/olsson" published: "" publisher: usenix.org publisher_english: "" raw_sha256: 7e0b79a9b7a21cc8ab092a2e56341284eb7996d0fd70ed1874e3e85e31ad9406 retrieved_from: "https://www.usenix.org/system/files/usenixsecurity24-olsson.pdf" retrieved_kind: live retrieved_utc: "2026-08-19T16:21:26+00:00" slug: usenix-org-spider-scents-grey-box-database-aware-web-scanning-stored-xss snapshot: "" title_english: "" translation_file: "" translation_of: "" --- # Spider-Scents: Grey-box Database-aware Web Scanning for Stored XSS **Spider-Scents: Grey-box Database-aware Web Scanning for Stored XSS** - Eric Olsson, Benjamin Eriksson, Adam Doupé, Andrei Sabelfeld, usenix.org. - Published: date not stated - Original: - Also published at: - Also published at: - Also published at: - Also published at: - Preserved from: https://www.usenix.org/system/files/usenixsecurity24-olsson.pdf (live) on 2026-08-19 - 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. # Spider-Scents: Grey-box Database-aware Web Scanning for Stored XSS --- page 1 --- Spider-Scents: Grey-box Database-aware Web Scanning for Stored XSSEric Olsson and Benjamin Eriksson, Chalmers University of Technology; Adam Doupé, Arizona State University; Andrei Sabelfeld, Chalmers University of Technologyhttps://www.usenix.org/conference/usenixsecurity24/presentation/olsson --- page 2 --- This paper is included in the Proceedings of the 33rd USENIX Security Symposium.August 14–16, 2024 • Philadelphia, PA, USA978-1-939133-44-1Open access to the Proceedings of the 33rd USENIX Security Symposium is sponsored by USENIX. --- page 3 --- Spider-Scents: Grey-box Database-aware Web Scanning for Stored XSSEric OlssonChalmers University of TechnologyBenjamin ErikssonChalmers University of TechnologyAdam DoupŽArizona State UniversityAndrei SabelfeldChalmers University of TechnologyAbstractAs web applications play an ever more important role in so-ciety, so does ensuring their security. A large threat to webapplication security is XSS vulnerabilities, and in particular,stored XSS. Due to the complexity of web applications andthe difÞculty of properly injecting XSS payloads into a webapplication, many of these vulnerabilities still evade currentstate-of-the-art scanners. We approach this problem from anew directionÑby injecting XSS payloads directly into thedatabase we can completely bypass the difÞculty of inject-ing XSS payloads into a web application. We thus proposeSpider-Scents, a novel method for grey-box database-awarescanning for stored XSS, that maps database values to theweb application and automatically Þnds unprotected outputs.Spider-Scents revealscode smellsthat expose stored XSSvulnerabilities. We evaluate our approach on a set of 12 webapplications and compare with three state-of-the-art black-boxscanners. We demonstrate improvement of database cover-age, ranging from 79% to 100% database coverage acrossthe applications compared to the range of 2% to 60% forthe other scanners. We systematize the relationship betweenunprotected outputs, vulnerabilities, and exploits in the con-text of stored XSS. We manually analyze unprotected outputsreported by Spider-Scents to determine their vulnerabilityand exploitability. In total, this method Þnds 85 stored XSSvulnerabilities, outperforming the union of state-of-the-artÕs32.1IntroductionThe web is a key enabler for todayÕs ever-more digital world.Our society increasingly relies on web applications to supportthe Þnancial, governmental, and military infrastructure. Thedynamic functionality of web applications, coupled with themyriad of implementation technologies, makes developinga bug-free application challenging. Furthermore, these bugscan often manifest as security vulnerabilities. The complexityof modern systems and ever-powerful adversaries make se-curing web applications agrand challenge. Even the biggestweb players such as Google and Meta still release vulnerableapplications and services, which reßects in $12 and $2 millionin bug bounties in 2022 respectively [15,26].Challenge of XSS.A particularly common class of webapplication security vulnerability isCross-Site Scripting(XSS)[28], allowing attackers to inject JavaScript code intoweb pages. Astonishingly, XSS has persisted in the OWASPTop 10Õs list of most critical security risks to web appli-cations for the past 20 years [48]. This remarkable persis-tence is reßected in bug bounties, with HackerOne reportedpaying over $4.7 million for XSS vulnerabilities in 2022alone [17]. Securing web applications against XSS is difÞ-cult because there is no single general solution that preventsall XSS vulnerabilities [20]. Indeed, XSS vulnerabilities arecontext-dependent [42,44], requiring that the correct outputsanitization be used depending on the output context.Stored XSS.Stored XSS, where the injection is stored andonly later executed [28], is particularly challenging due tothe disconnect that storage brings between thesource ßow,where the payload is input and stored, and thesink ßow, wherethe retrieved payload is executed. Current vulnerability detec-tion approaches [14,32] have fundamental difÞculties Þndingstored XSS.InsufÞciency of!-box approaches.The difÞculty of secur-ing a web application against XSS motivates the developmentof vulnerability detection tools [2,7,11,33,49,54]. Web appli-cation vulnerability detection approaches can be classiÞed aswhite-box, black-box, or grey-box based on what informationis available (cf. Section4):White-box approach:White-box approaches [12,19,22,24]usually statically analyze source code artifacts. Such staticanalysis is necessarily speciÞc to the structure of the ana-lyzed artifact, such as the server-side language or framework.Unfortunately, white-box vulnerability detection is fundamen-tally limited in its applicability to web applications becauseit is hard for white-box static analysis to precisely modelthe combined interplay of increasingly complex and dynamicclient-side, database, and server-side behavior [43]. In addi-tion, white-box analyses depend on the availability of artifacts, --- page 4 --- USENIX Association 33rd USENIX Security Symposium 6741 --- page 5 --- further limiting their usage.Black-box approach:More advantageously from a usabilityperspective, black-box vulnerability detection for web appli-cations does not typically require access to source code andinstead analyzes a running web application from the perspec-tive of a user. Black-box scanners have been developed withvarious methods to better cover the increased attack surfaceof modern web applications [8], such as modeling server-sidestate [7], tracking data ßows and fuzzing payloads [9,10],modeling client-side state [25,33], and combining multipleapproaches [11]. However, while coverage of the attack sur-face has improved for some XSS, black-box scanners areoften still unable to Þnd even simpler stored XSS [32].Grey-box approach:A common solution [1,14,18,49,50]is to combine black-box dynamic interactions of a running ap-plication, with white-box access. How these two informationsources are combined varies. Sometimes, artifacts or non-standard interfaces only available with white-box access canbe used to guide the otherwise black-box scan of a web appli-cation [47] . More commonly, a fully white-box static analysisof the application source code is combined with a black-boxscan for dynamic runtime information [1,14]. Combiningthese sources of information can mitigate some challengesinherent to otherwise using one approach in isolation. How-ever, this combination still has a white-box component, fromwhich its usability suffers.While the previously described approaches constitute anexciting and active area of research, we identify a key con-sideration in the design space of!-box approaches. A coreproblem with Þnding stored XSS is that ablack-box scannermust Þnd both the source and sink ßows, and also understandthe relationship between the two, without any access to theweb applicationÕs source code.Approach.Inspired by recent work in improving binaryfuzzing [34], our insight is to make stored XSS easier toÞnd by relaxing the requirement that the scanner must Þndthe source of a stored XSS. To do so, we supplement anotherwise black-box scanner with access to the database andallow the scanner to inject payloads directly into the databasewhile scanning the running web application for sensitive sinksthat output the inserted payload. This yields the beneÞt thata scanner no longer needs to Þnd both source and sink norunderstand their relation. The elegance of our approach isthat it requires no knowledge of the web application source,only the database. Figure1illustrates the unique point ourapproach occupies in the design space of!-box approaches.This unique position represents a paradigm shift from thedomain ofapplication inputto that ofapplication state, rep-resented in the database. Challenges (presented in Section3)stemming from fundamental problems with *-box approachesdo not have to be solved by our method.Based on these insights, we develop Spider-Scents, an ap-proach to grey-box database-aware web scanning for storedDatabaseCodeInfrastructureBackendResetFrontendClientCodeNetworkBBBWB*WFigure 1: Access to components involved in a web applica-tion that different approaches need. Black-box approaches (B)have access to the front-end, and sometimes (B*) need infras-tructure access to perform resets in the face of irreversiblestate changes. White-box approaches (W) have access to bothfront- and back-end code. Our approach, shaded in grey, ac-cesses the front-end and database.XSS. Spider-Scents injects payloads directly into the databaseand reports where database content is used in the HTML out-put without proper sanitization, ßagging what we callunpro-tected outputs. This does not mean that all reports are storedXSS vulnerabilities, as the web application might be sanitiz-ing the data on input. However, relying on input sanitization isagainst best practices for XSS prevention, as it is impossibleto sanitize user input for every possible HTML output context.Indeed, the OWASP guidelines [31] postulate: ÒApply InputValidation (using "allow list" approach) combined with Out-put Sanitizing+Escaping on user input/output,Ó conÞrmingthat input validation alone is not enough. We term this resultacode smell, an indication that something is wrong deeperwithin the application [13]. Even in the best case, where thereis neither bug nor vulnerability, the application is fragile. Anynew functionality added, such as creating a REST API, risksfailing to properly sanitize user input. Therefore, even theunprotected outputs that are not currently exploitable storedXSS should be addressed by the web developer. Light manualanalysis is required to verify a complete stored XSS vulnera-bility (see Section6.7)Non-vulnerable unprotected outputs constitute what wecall adormant XSSÑthey would be vulnerable, except thatthecurrentweb application does not allow an exploit payload.The web applicationÕs evolution risks elevating a dormantXSS to acomplete XSS vulnerability, even for security-waryapplications. Our empirical study indeed conÞrms a dormantXSS vulnerability on WordPress, elevated to a complete vul-nerability by arealpublished plugin (see Section4.2).Evaluation.We evaluate our approach across 12 web ap-plications and compare our results with three state-of-the-art --- page 6 --- 6742 33rd USENIX Security Symposium USENIX Association --- page 7 --- black-box scanners. The applications range from referenceapplications used in prior work to latest versions of modernapplications. Our results show that we cover (measured ashow much of the database the scanner can change, describedin Section5.2.1) between 79% to 100% across all applica-tions. In comparison, the other scanners cover 2% to 60% onaverage. We also Þnd vulnerabilities that the other scannersare unable to detect. In total, we Þnd 85 XSS vulnerabilitiescompared to 32 unique XSS for the other scanners. To fur-ther classify the impact of our Þndings we manually analyzethe input protections and permission models of the Spider-Scents discovered vulnerabilities and determine that 59 areexploitable (and not self-XSS).Contributions.We offer the following contributions:¥We present a novel approach for Þnding stored XSS vulnera-bilities by injecting XSS payloads directly into the database,thus simplifying the detection of stored XSS. We presentthis in Section4.¥We implement our approach into a prototype Spider-Scents,a semi-automated grey-box database-aware stored XSSscanner.¥We evaluate Spider-Scents and three state-of-the-art black-box scanners on 12 web applications. We present the resultsin Section5and analyze these in Section6. Spider-ScentsÞnds 85 XSS vulnerabilities across 7 applications.¥We systematize the relationship betweenunprotected out-puts,stored XSS vulnerabilities, andexploitsbased on in-put protections and permissions models. Following thissystematization, we manually analyze unprotected outputsreported by Spider-Scents to determine their vulnerabilityand exploitability. We also present this in Section6.¥For the beneÞt of future research in this area, we share thesource code of Spider-Scents1.Ethical considerations and coordinated disclosure.Byactively scanning only our local clones of web applications ina controlled environment, we strictly avoid any harm causedby scanners on the web. We handle the discovered securityvulnerabilities in accordance with the best practices of ethicsin security [41]. We are in the process of reporting our Þnd-ings to the affected vendors, following coordinated vulnera-bility disclosure for all discovered vulnerabilities. We reportresponses from vendors in Section6.9.2TerminologyHere we attempt to systematize the terminology around XSSvulnerability analysis. Spider-Scents Þnds places in the webapplication where database content is used in the HTML out-put without proper sanitization. We term theseunprotectedoutputsÑoutput sinks where the output is not protected suf-Þciently against XSS. In contrast, there are alsoprotectedoutputs: output sinks that are properly sanitized against XSS.1Our implementation is available online athttps://www.cse.chalmers.se/research/group/security/spider-scents/We call acomplete XSS vulnerabilitywhere user input ßowsto the unprotected output. Furthermore, that input must itselfbe anunprotected inputÑan input source lacking sufÞcientXSS protection. In contrast, there are alsoprotected inputs:inputs protected with some combination ofsanitizationsuchas validation, stripping, or escaping. An unprotected outputcan also fail to be a complete XSS due to havingno input.If an unprotected input ßows to an unprotected output,the web application has anXSS vulnerability. However, anXSS vulnerability is not necessarilyexploitable, as this de-pends on the access control policy of the web application.The core question is if the user (or role) that injects the XSSpayload can get the output on either another user or a rolewith greater permissions. We call XSS vulnerabilities that arenot exploitableself-XSS, which have signiÞcantly less severitythan exploitable XSS vulnerabilities.3Roadblocks for current XSS scannersAutomatically Þnding vulnerabilities in web applications re-mains a challenge despite active research in improving vul-nerability detection. Stored XSS is especially difÞcult to Þnd,as this type of vulnerability involves correctly injecting inputinto the application, where it will be stored by the database,and subsequently used in the web applicationÕs output incor-rectly sanitized.Black-box approaches can explore entire web applicationswithout access or reliance on the underlying web applicationsource code. It is possible for black-box scanners to track anentire stored XSS vulnerability from initial payload injectionto vulnerable output. However, Þnding all such vulnerabilitiesfor a black-box scanner is difÞcult, due to several challengesinherent to stored XSS that they must solve:Vulnerable input validation.Web applications perform inputvalidation (in client-side JavaScript and also server-side code)to ensure that the input data conforms to certain requirements.Web applications can check vulnerable inputs for validationthat the scanner must pass and also inject an XSS payloadinto. As most black-box scanners use a pre-conÞgured list ofXSS payloads, it is difÞcult for them to create a custom XSSpayload that also bypasses the vulnerable input validation.Interdependent vulnerable input validation.Web applicationsvalidate all types of inputs. Often, a web application requiresthe user to Þll out a set of inputs together. Consider a user reg-istration form that might require the desired username, emailaddress, zip code, and biography. All forms are required, theserver-side input validation requires that the username beunique, the email address has a speciÞc form, the zip codeis Þve digits, and the biography has no validation and is vul-nerable to stored XSS. Due to theinterdependenceof thefour inputs, to Þnd the stored XSS a black-box scanner mustbe able to provide a unique username (which is difÞcult forrepeated injection attempts), correctly-formatted email ad-dress and zip code. As the number of inputs interdependentto vulnerable input increases, and as the input validation is --- page 8 --- USENIX Association 33rd USENIX Security Symposium 6743 --- page 9 --- application-speciÞc, it is more difÞcult for black-box scannersto generate the proper interdependent input to inject storedXSS payloads.Vulnerable input modiÞcation.Web applications can also es-cape or modify user input. Consider a blog that accepts blogposts in markdown format that is transformed to HTML be-fore storing them in the database. Because the black-boxscanner has no knowledge of the server-side source code, itcannot know about this modiÞcation. If a vulnerable input ismodiÞed before being stored into the database, it is difÞcultfor a scanner to create a custom XSS payload that can survivethe modiÞcation, and, therefore, it is difÞcult to detect.Multi-step vulnerable input.Web applications are statefulapplications that can require a multi-step process before per-sisting user data. A classic example of this is the multi-stepstored XSS in WackoPicko [8], where commenting on a pic-ture requires Þrst previewing the comment (which is outputwith sanitization) and then approving the comment (whereit is output without sanitization). To our knowledge, the Þrstblack-box scanner that was able to automatically detect thisvulnerability was Black Widow [11], published 11 years af-ter WackoPicko was released. Therefore, vulnerable inputsthat require multi-step interactions are difÞcult for black-boxscanners to detect.Vulnerable input identiÞcation.The core of stored XSS is thatthe XSS payload is stored in the database before being usedas output. Even if a black-box scanner can correctly injectan XSS payload, it must be able to Þnd where that input isoutputÑotherwise, it will never detect the XSS.4ApproachOur goal is to overcome the challenges black-box scannersface (mentioned in Section3) in detecting stored XSS vulner-abilities. Rather than take a completely black-box approach,we use a novel grey-box approach that includesknowledgeof the databaseto help an otherwise black-box scanner Þndstored XSS vulnerabilities. Our idea is that by injecting theXSS payload directly into the database, and then scanningthe web application for the payloadÕs output, we cancom-pletely bypassseveral of the roadblocks black-box scannersface in detecting stored XSS: vulnerable input validation, in-terdependent vulnerable input validation, vulnerable inputmodiÞcation, and multi-step vulnerable input.ÔGrey-boxÕ.In web security, black-box has been synony-mous with dynamic testing, as white-box is to static analysis.With this view, grey-box can appear to be deÞned as the com-bination of these two: dynamic testing and static analysis.Recent papers [14,49] in grey-box web testing have suppliedtwo deÞnitions: the previous, based onmethod, or based onac-cessorvisibilityto the application. Our approach is grey-boxonly by the second deÞnition: we do not use static analysis,but we have access to the database.Besides the application data contained, metadata such as Spider-ScentsDatabase1DatabaseModuleBreakageCheckerReßectionScannerWebApplication2345Figure 2: Overview of Spider-ScentsÕ different componentsand their interactions with both the database and web applica-tion.table structure and associated relations are also assumed to beavailable, through the same database connection. In practicalterms, SQL databases provide programmatic access to suchmetadata in theINFORMATION_SCHEMAtables [30].4.1OverviewIn contrast to existing state-of-the-art scanners, our database-aware method requires a paradigm shift from applicationinputtostate. Instead of solving problems associated with the inputdomain (such as crawling, modelling, or payload selection),we address new challenges of preparing the application state,selecting which part to modify, and analyzing its impact onthe application.Spider-Scents is our implementation of our novel grey-boxscanning approach. Figure2shows a diagram of our approach.First1, we prepare the web application for scanning. Next2, we choose a database cell to modify, and3insert an XSSpayload into the cell. We then validate4that the modiÞeddatabase did not break the application. Finally5, we crawlthe web application looking for reßections of the injected pay-load. This approach iterates as long as there remain untesteddatabase cells to modify.4.1.1ReportsDue to Spider-Scents injecting XSS payloads directly into thedatabase, what it reportsis not XSS vulnerabilities, but ratherunprotected outputs(deÞned in Section2). Therefore, theÞnal step is to manually analyze the results of Spider-Scents.As our evaluation in Section5shows, many unprotected out-puts are also XSS vulnerabilitiesÑSpider-Scents Þnds 133unprotected outputs in evaluated applications and 85 XSSvulnerabilities.An interesting side-effect of Þnding so many vulnerabili-ties with Spider-Scents is that we realized that theimpactofthe discovered XSS vulnerabilities is critically important tocontextualize the results. SpeciÞcally, we found that some ofthe XSS vulnerabilities wereself-XSS, deÞned in Section2 --- page 10 --- 6744 33rd USENIX Security Symposium USENIX Association --- page 11 --- wherein the privilege required to store the XSS payload isthe same as the user that views it. Of the 85 XSS vulnerabil-ities found by Spider-Scents, 26 are self-XSS while 59 arefully-exploitable.4.2Motivating ExamplesSpider-ScentsÕ database-aware scanning is particularly wellsuited to Þnding stored XSS vulnerabilities due to circum-venting common challenges for black-box scanners. Here, wepresent examples of the types of issues that Spider-Scents isbetter at Þnding than previous work:1.Fully-exploitable stored XSS that other scanners do notÞnd.2.Dormant vulnerabilities that become exploitable.3.Self-XSS that other scanners do not Þnd.Fully-exploitable stored XSS.Even though they are capa-ble of Þnding a fully-exploitable stored XSS vulnerability,other scanners often fail due to their inability to extensivelyexplore the input surface of the web application, including sat-isfying vulnerable input and interdependent input validation.In Section5, we further analyze the precise reasons why otherscanners miss fully-exploitable stored XSS that Spider-ScentsÞnds.In addition, beyond needing a full XSS from input to out-put, the XSS vulnerability also must be exploitable. Deter-mining the exploitability of an XSS currently requires manualanalysis, as the exploitability of a vulnerability depends onapplication context such as the levels of user permissionswithin the application. Other presentations of scanners havegenerally not provided such analysis of their XSS results.An example of a fully-exploitable stored XSS that otherscanners fail to Þnd is in the CMS Made Simple bookmarksfunctionality, which is shown in Figure3. In this case, itis harder to Þnd the input form, but easy to Þnd the outputfrom the database. Armed with this vulnerability, a user canperform XSS on an admin of CMSMS. Spider-Scents reportsthis as an unprotected output, and we manually conÞrm itsexploitability.Dormant XSS.Spider-Scents reports what it Þnds as un-protected outputs rather than stored XSS vulnerabilities, asit only Þnds the second half of a complete XSS workßow inunprotected outputs. Unprotected outputs do not always havean unprotected input ßowing to them. However, we believethere is signiÞcant value in reporting unprotected outputsbecause they are acode smellÑthe web application has notfollowed the best practice of Òalways escape lateÓ emphasizedby both WordPress and WordPress VIP [51,52]. While not abug or vulnerability, an unprotected output is something thata developer should look at and Þx.In fact, we believe that an unprotected output in isolationcan be considered adormant XSSÑit would be vulnerable,except that the current inputs for the web application do notecho"bookmark_id."\">".$onemark->title."\n";echo"".$onemark->url."\n";echo"bookmark_id."\">";echo$themeObject->DisplayImage(Õicons/system/edit.gifÕ,lang(ÕeditÕ),ÕÕ,ÕÕ,ÕsystemiconÕ);echo"\n";echo"bookmark_id."\"onclick=\"returnconfirm(Õ".cms_html_entity_decode(lang(ÕdeleteconfirmÕ,$onemark->title))."Õ);\">";echo$themeObject->DisplayImage(Õicons/system/delete.gifÕ,lang(ÕdeleteÕ),ÕÕ,ÕÕ,ÕsystemiconÕ);echo"\n";Figure 3: CMS Made Simple bookmarks functionality. Abookmark contains a title and a URL. Both the title and URLare correctly escaped in their respective Þrst two cells in thissnippet from CMSMS code, but the title is not protected inits inclusion in the delete button.allow an exploit payload due to either escaping, stripping,validation, no input possible, or some combination of these.The web applicationÕs evolution, either through future devel-opment or integration with other code (plugins, for example),can elevate a dormant XSS to a full XSS.For example, Spider-Scents found that WordPress has un-protected output of bothdisplay_nameanduser_nicenamefrom theuserstable. In the base WordPress application, thereare no unprotected inputs to these columnsÑin fact, these arenot modiÞable after the creation of a user. However,bothareexposed in a vulnerable version of theusername-changerplugin [5] (code shown in Listing4), and, therefore, whenthis vulnerable plugin is installed this unprotected output be-comes a fully-exploitable XSS.2If WordPress followed theirown best practices of Òalways escape lateÓ, this dormant XSSwould not be possible (and Spider-Scents would not report itas an unprotected output).It is also possible that some inputs to the database are keptconstant. For example, in Hostel Management System, anapplication we evaluate in Section5, a list of US states is hard-coded in the database. Spider-Scents Þnds unprotected outputsin this list. While not directly exploitable, these unprotectedoutputs could become a problem through extension of code,either in future versions of the application or with plugins. Inour manual analysis, we quantify how many reports fall intothis category and present them in theNIcolumn in Table2.Self-XSS.Finally, an XSS vulnerability is not necessarilyexploitable. An XSS can be unexploitable due to user permis-sions, such that the admin can only perform an XSS on them-self. This self-XSS is the counterpoint to fully-exploitableXSS. An example of this is templates in MyBB. A template2Vulnerable version of WordPressusername-changerplu-ginhttps://github.com/evertiro/Username-Changer/blob/dd1976b05213d9895886da7f9a91515c52188344/includes/functions.php#L84 --- page 12 --- USENIX Association 33rd USENIX Security Symposium 6745 --- page 13 --- $qnn=$wpdb->prepare("UPDATE$wpdb->usersSETuser_nicename=%sWHEREuser_login=%sANDuser_nicename=%s",$new_username,$new_username,$old_username);$wpdb->query($qnn);$qdn=$wpdb->prepare("UPDATE$wpdb->usersSETdisplay_name=%sWHEREuser_login=%sANDdisplay_name=%s",$new_username,$new_username,$old_username);$wpdb->query($qdn);Figure 4: Username-Changer WordPress plugin vulnerablecode:display_nameanduser_nicenameare not sanitized.Algorithm 1Synthesizing data in the database.rows"3i"1whilei#rowsdorow"[]j"0whilej#columnsdoappend(row,increment(i))j"j+1end whileinsert(row)error"breakage()iferrorthendelete(row)end ifi"i+1end whilefor the calendar functionality in this application can be modi-Þed to include executable JavaScript. However, only an adminuser has the necessary permissions to add or modify this tem-plate. Spider-Scents Þnds this vulnerability, and we manuallyconÞrm that it is a self-XSS.4.3Preparing the web applicationThe initial step in our approach is to prepare the web applica-tionÕs database for scanning, as shown in step1$in Figure2.4.3.1Database synthesisIdeally, our method should scan an application with afulldatabase. Steinhauser and Tûuma note the importance of thisas well [47]. However, they do not attempt to solve this andinstead rely on somewhat complete conÞgurations providedby the applications themselves, or other publicly-availablemanually-assembled data. For an e-commerce website, thismeans that the database already contains products, customers,and other data. To be able to discover an XSS-vulnerableworkßow that spans multiple tables, data must exist in eachtable.To address cases where data is lacking, more speciÞcallyempty database tables, we insert a constant number of rowsof benign data matching the schema of the empty tables. Weperform this simple algorithm shown in Section4.3.1on eachempty table in the database.columnsis the number of columns in the schema for theempty table andincrement(i)modiÞes a base value by theincrementi. For example, integers have a base value of 0,dates have Ô1970-01-01 00:00:00Õ, and strings havea. Bymodifying a constant value, we ensure that there are recordscorrelated between tables by these deterministic values, tosatisfy constraints common in web applications using normal-ized databases. We always insert 3 initial rows due to differ-ent auto-increments in the applicationsÕ database setupsÑwehave observed empirically that these often start at 1, but notalways.While this naive solution is implemented and works wellfor our evaluation, the more general case of database synthe-sis is orthogonal to this work. Related work in this speciÞcdirection can be found in Section7.5.4.3.2Reverting changesWe also periodically revert modiÞcations done by Spider-Scents to the database, the rules for which are described inAppendixA.1. This is to add independence between our pay-load insertions, and also reduces our reliance on detectingapplication breakage, if we automatically revert based onother rules. Some rules we implement enforce independenceacross boundaries in the database; such as tables and columns.To revert a database edit, fromnewDatatooldData, per-form:error"updateRow(oldData)iferrorthenremoveRow(newData)insertRow(oldData)end ifremoveRowandupdateRowcan identify a row based on ei-ther keys or values. It is necessary to handle reverting changesin Spider-Scents, as built-in database functionality such astransactions cannot be open-ended, which is necessary to al-low the simultaneous manipulation of both Spider-Scents andthe web application backend of the database.Reverting changes also happens when breakage is detected,conditions for which are covered in Section4.6.4.3.3Logging inFinally, we also must make sure that the web applicationitself is in a proper state to be scanned. Among other things,this means making sure that Spider-Scents is logged in. Weautomatically grab relevant details such as cookies, user agent,and the user URL (dashboard) at the start of each scan. Spider-Scents uses Selenium to interact with a headless Chromeinstance with a custom extension to record this information,after having been supplied with the necessary credentials(username, password, and login page). These client details,which web applications often use to identify users, are then --- page 14 --- 6746 33rd USENIX Security Symposium USENIX Association --- page 15 --- Algorithm 2Discovering sensitive rows.i"1whilei#rowsdorow"retrieve(i)error"delete(i)iferrorthensensitive(row)end ifcrawl()error"insert(row)iferrorthensensitive(row)end ifi"i+1end whilere-used throughout the rest of the scan.4.4Choosing a database cellFor each cell in the database, Spider-Scents checks if the cellis suitable for our XSS payload. We ensure that the schema forthe cellÕs column speciÞes that it is a text Þeld with a lengththat can accommodate our payload, described in Section4.5.4.4.1Avoiding sensitive rowsWe also must consider if the web application is particularlysensitiveto changing a speciÞc cellÕs value. If the applicationtries to revert an injected database value, reset a table or row,or even insert a conßicting row, various problems can occur.This can either be due to the web application not havinga robust recovery method that can handle Spider-ScentsÕsadmittedly unexpected meddling in their database tables, ordue to Spider-Scents losing track of the state the database isin. In either case, we avoid such sensitive cells by Þrst probingthe database for their existence.For each table in the database, we discover sensitive rowsas shown in Algorithm2, whererowsis the number of rowsin the table,retrieve(i)anddelete(i)perform appropriateactions on theithrow in the table,insert(row)inserts the rowinto the table,sensitive(row)marks that this row in the tableis sensitive to changes, andcrawl()crawls the application toinduce reverts, conßicts, or resets in the database.4.4.2Choosing a cellHow a table in a database is used by an application alsomatters, and, unfortunately, this usage is not always fullyspeciÞed in the database schema.If a table has uniform types of rows, then it might be sufÞ-cient to modify cells in a single row. However, this assumptiononly holds if, for instance, the access control policies of theapplication specify that all users can see the data in all rows.This also assumes that each entry in the table is handledidentically by the web application code, which might not bea correct assumption. Indeed, not all tables have only suchuniformly typed entries; a counterpoint is tables that store key-value data, where the interpretation of a value cell depends ona key cell identifying it. Given the existence of such key-valuetables and the fact that reßection code for entries can changebased on the contents of a particular entry, we must iterateacross all rows in a table.In addition, the order of cell changes does impact whichXSS-vulnerable workßows are discovered. We implementdifferent traversal orders, primarily based on iterating tablesin alphabetical order. From there, the scanner either choosescells (that satisfy payload requirements and are not sensitive)based on the iteration of rows or columns.4.5Payload insertionIn contrast to traditional black-box approaches, Spider-Scentsdoes not interact with the web application in an attempt to in-sert data from user input to a particular database cell. Instead,Spider-Scents inserts the payload directly into a database cell(step3$).Once a suitable cell has been found, Spider-Scents gener-ates a unique ID to use for the payload. The payload we useis inspired by payloads used in prior work [11]. This struc-ture helps reduce false positives with dynamic XSS detection.We use the following template, whereIDis replaced with aunique generated ID:"Õ>While other payloads, such as a general-purposeimgpay-load, can be more or less suitable depending on the context ofthe reßection, we consider the problem of creating smaller orcontext-speciÞc XSS payloads to be orthogonal to this work.Using this payload can lead to false negatives, for example, ifthe reßection happens in atextareaortitletag.4.5.1Structured dataThis is with the caveat that wedo considerthe presence ofstructured datain a cell. Spider-ScentsÕ iterative modiÞcationof database cells, and searching for these individual reßec-tions in the web page, has the implicit assumption that it isindividualdata stored in database cells in a one-to-one corre-spondence from web input to database cell. This might not betrueÑdata might be split up into multiple cells or combinedwithin a single cell. A payload would need to be either splitacross cells in the Þrst case or multiple payloads combined inthe second. Correlating changes across cells is considered outof scope for this work.However, when data is combined within a single cell, weconsider this as structured data. Spider-Scents modiÞes pay-loads to Þt some common types of structured data. If a knownstructure is detected by parsing cell data, the payload will bedelivered to each valid location for it. Currently, Spider-Scentsimplements a customized payload for PHP serialized objectsand image paths. The support for these formats is chosen todemonstrate this approach and can lead to false negatives dueto not handling more widely-used structured data formats, --- page 16 --- USENIX Association 33rd USENIX Security Symposium 6747 --- page 17 --- Algorithm 3Determining breakage.i"1broken"0whilei#length(urls)docurrent"request(urls[i])broken_url"request(urls[i])ifbroken_urlthenbroken"broken+1end ifi"i+1end whileifbroken/length(urls)%thresholdthen returnbrokenend ifsuch as JSON.4.6Application breakageAfter inserting a payload, the changing database values canhave catastrophic effects on the web applicationÕs functional-ity, which we callbreakage.While breakage can happen when using an applicationthrough its standard functionality, it is even more likely whenSpider-Scents directly modiÞes the database and possiblyinserts values that the application has not defensively codedagainst.From a black-box scannerÕs perspective, such internalapplication-speciÞcation breaking changes are not available.However, similar behavior, when reachable on standard inter-faces, is regarded asirrecoverable state changes[7]. Whileit may be intended functionality, such an irrecoverable statechange overlaps with our notion of application breakage.Guarding against breakage is inherently a tradeoff. On theone hand, changing the websiteÕs domain to an XSS payloadin a CMS such as WordPress will rewrite all links, includingadmin ones, making the application unusable. On the otherhand, new vulnerable behavior of the application might havebeen discovered instead.Entirely black-box approaches will not be able to recoverfrom such a breaking, irrecoverable state change. Some scan-ners add infrastructure access (shown in Figure1) to be ableto reset the application when this happens [7,10]. With ourposition in the database, while we are more prone to causebreakage, we can alsoidentify and Þx it, without any addi-tional access to the application.Therefore, as noted by step4$in Figure2, Spider-Scentswill dynamically scan the application looking for signs ofbreakage. If any are found we can reset the exact cell wechanged that caused the breakage, even in the case when theweb application is unusable.Web application breakage across a web application is in-ferred by Algorithm3, whereurlsis a list of URLs for distinctweb pages in the application,compare(baseline,current)compares the current status of a web page to a baseline mea-surement, andthresholdis a threshold speciÞed for how manypages can be acceptably broken in a web application.compare(baseline,current)can be implemented to com-pare measurements of a web page response based on HTTPstatus codes, linked content, length, or other heuristics. Theapproach we take is a combination of status codes and linkedcontent.4.7Reßection scanningFinally, we dynamically exercise the application with a reßec-tion scanner (step5$). This scanner will crawl the applicationand report back on all the IDs that it Þnds. Here we differenti-ate between reßected JavaScript payloads that are executed,i.e. unescaped, and cases where we Þnd the IDs in text. Wedo not try to Þnd mangled or encoded payloads.Spider-Scents usesBlack Widow[11], the source code ofwhich is available3, with minor modiÞcations to facilitatecommunication between modules, as its reßection scanner.4.8Manual analysisFrom the Spider-Scents reports we manually analyze the un-protected outputs to determine if the payloads could be addedfrom the web application. Once an input element is found wesupply valid data and ensure the database is updated accord-ingly. Next, we add our payload and record if it is (1) rejecteddue to validation, (2) escaped, or (3) sanitized. We then repeatthis for all inputs relating to the column.5EvaluationWe evaluate our approach by analyzing 12 different web appli-cations and report on the number of stored XSS vulnerabilitiesfound.We compare Spider-Scents with a combination of up-to-date academic and open-source scanners that Þnd stored XSSin Arachni [2], Black Widow [11], and OWASP ZAP [54].5.1Web applicationsSimilar to previous works [7,11,33,49] we test both oldapplications and new modern ones. We divide the targetapplications into two sets. The Þve reference applications(that have known CVEs) and are used in prior work are:SCARF [45], Hospital Management System [35], User Regis-tration & Login and User Management System [36], DoctorAppointment Management System [37], and Hostel Manage-ment System [38].For modern, complex, applications we use these seven:CMS Made Simple [46], Joomla [21], MyBB [27], Open-Cart [29], Piwigo [39], PrestaShop [40], and WordPress [53],Statistics that describe the applications chosen, their versionnumbers, and their usage in evaluation by prior work is pro-vided in the Appendix Table4.3https://github.com/SecuringWeb/BlackWidow --- page 18 --- 6748 33rd USENIX Security Symposium USENIX Association --- page 19 --- Applications are largely chosen based on those evaluated bythe authors of jŠk [33], Black Widow [11], and Witcher [49]4,as these represent the current state-of-the-art in academicblack-box web scanners. We restrict the evaluation to thosethat are database-backed.Note that while we have selected applications based on theprior criteria, we choose thelatest versionof each application.Therefore, the unique vulnerabilities found by Spider-Scentsare alsonew.5.2Experimental setupIn this section, we present the experimental setup used forthe evaluation of Spider-Scents and comparison with otherscanners.5.2.1Performance metricsWe focus our evaluation on three metrics: database coverage,vulnerabilities, and exploitability.Database coverage.To successfully execute a stored XSSpayload the scanner must Þrst write the payload to thedatabase5. By comparing a snapshot of the database beforeand after each scan we can approximately measure what effecteach scanner has on the database.These snapshots allow us to more precisely determinewhere the scanner fails in storing a payload, and where Spider-Scents can beneÞt from directly adding the payload to thedatabase. In addition, we also classify the changes as eitherbenign or XSS payloads. If an XSS payload is added, weinvestigate if the scanner can Þnd it.Vulnerabilities.To evaluate our methodÕs capability to Þndvulnerabilities we also record the number of vulnerabilitiesreported by each scanner.There is no clear method, neither in literature nor suggestedby the scannersÕ implementations, of how to differentiatebetween two different XSS vulnerabilities. This means that fora given web application functionality, different scanners cangenerate different amounts of reported XSS vulnerabilities.For example, a vulnerable search bar included on every URLcould generate a reported XSS vulnerability for each URL.To level the playing Þeld and allow for a fair comparison wemanually inspect each vulnerability and cluster them basedon their related functionality. This clustering is justiÞed asan application ofroot cause analysis, a process already well-established and valued in software bug reports [16].Furthermore, we only compare stored XSS results fromother scanners. Reports from compared scanners are man-ually conÞrmed to either be stored XSS or non-stored XSS(reßected or DOM). During evaluation, Arachni Þnds 4, Black4Witcher only supports detecting SQL Injection and Command Injection,therefore we do not compare against it.5Assuming the payload is stored in the database; see Section6.3for anexample of a stored XSS in the Þlesystem.Widow Þnds 2, and ZAP Þnds 46non-stored XSS. Ourmethod is unable to Þnd these, as it is speciÞc to stored XSS.Exploitability.Scanners search for XSS vulnerabilities byinjecting data with XSS payloads into the application. After-wards, they search for this data, either statically, or dynami-cally. However, due to permissions, this does not guaranteethat an attacker can abuse the discovered XSS. For example,if only the super user can inject the payload, the vulnerabilityis not exploitable.As modern applications can have complex user and grouppermissions with different associated application views, itis difÞcult for a scanner to automatically reason about theexploitabilityof these possible injections. In this paper, wemanually verify and report on the exploitability of each re-ported vulnerability. We divide this step into two parts, i)input protections and ii) permissions.For input protection, we ensure that it is possible to addthe XSS payload from the application to the database. If itis not possible (protected), we further categorize the inputprotection for the reported vulnerability.No Input, when there is no usable input Þeld allowing forwriting to the database. For example, input Þelds that are onlyavailable during installation or constants, such as US states.Escaping, when the application changes the user input,to prevent it from being interpreted in some context, beforeadding it to the database. E.g. transformingfrom the user input.Validation, when the application refuses to add the userinput to the database if it does not satisfy some format, suchas containing illegal characters.For permissions, we consider the vulnerability exploitableif a less privileged user can add a payload that is executedon a page that a user with more privileges can access. Forexample, if a normal user can book an appointment whoseXSS payload is executed in the admin dashboard, then wewould consider this exploitable. However, if only the admincould add the payload to such an appointment then it wouldbe equivalent to self-XSS.5.2.2Scanner conÞgurationWe conÞgure other scanners to make as fair a comparison aspossible. While we focus on stored XSS, web scanners canscan for a plethora of other vulnerabilities, including SQLinjection, command injection, and local Þle inclusion.For this evaluation, we conÞgure each scanner to only focuson Þnding XSS vulnerabilities. Furthermore, to allow scan-ners a better chance to authenticate and stay authenticated,we make slight modiÞcations to theweb application. First,6In general, non-stored-XSS found by scanners is the difference betweencolumnsRandSin Table1. However, due to the false positive reports byZAP in the Hospital Management System (see Section6.2), ZAP only Þnds4 non-stored-XSS in the Doctor Apt. and Hostel applications. --- page 20 --- USENIX Association 33rd USENIX Security Symposium 6749 --- page 21 --- we ensure the index page has a link to the admin login. Sec-ondly, we rewrite the POST parameters server-side to matchthe correct user. This will level the playing Þeld, as scannersprefer different authentication methods. Other scanners areconÞgured to limit their runtime to 8 hours, similar to priorwork [11,49]ConÞguration of parameters speciÞc to Spider-Scents canbe found in AppendixA.1.5.3Comparison resultsIn addition to the most direct comparison statisticÑvulnerabilities foundÑwe also collect a new statistic for thisproblem:database coverage. This is motivated by the differ-ent approach taken by Spider-Scents.Database coverage.Modifying data in the database is re-quired to detect stored XSS in database-backed applications.As such, we record the number of unique columns in thedatabase each scanner modiÞes. In the case where an entirerow is added, we give the scanner credit for all columns inthe table. In our analysis in Section6.4, we look more closelyat the data inserted by the scanners.In Appendix Table5, we present the database coverage ofeach scanner and compare them to Spider-Scents. We furthervisualize this in Figure5. As is evident, Spider-Scents canaffect a much greater portion of the database compared to theother scanners. We cover between 79% to 100% while theother scanners cover between 2% and 60% on average. Thisshows that black-box scanners are still limited in how muchthey can affect the database, and subsequently, how well theycan detect stored XSS.There are cases where other scanners affect columns thatSpider-Scents does not modify: For example, on CMSMS,both Arachni and ZAP affect columns that Spider-Scents doesnot. In this particular case, it is thecms_adminlog.usernameand thecms_users.usernamecolumns. Both these have amax length of 25 while our payload is 30 characters. Wediscuss these cases in more detail in Section6.1.Database application mappings.Our approach generates amapping from database tables and columns where a payload isinserted, to the URLs where the payload is found. In Figure6we show such a mapping for the application Piwigo, wherethe red lines indicate unprotected output and the black linesindicate protected output.XSS results.In this section, we compare the reported XSSvulnerabilities by each scanner. In Table1, we present reportsby each scanner in columnR, manually conÞrmed stored XSSin columnS, and manually veriÞed and de-duplicated reportedvulnerabilities in columnV. Note that anything we Þnd incolumnV, all black-box scanners should report as well.Reports by other scanners are of XSS vulnerabilities. How-ever, the Spider-Scents scanner reports unprotected reßec-tions, not XSS vulnerabilities (see Section4.1.1). Therefore,columnSis inapplicable, and undeÞned for Spider-Scents.Table 1: XSS vulnerabilities reported (and manually veriÞed)by each scanner.R- All XSS or unprotected outputs reportedby the scanner,S- ConÞrmed stored XSS,V- VeriÞed andde-duplicated with our unique Þnds in parentheses.ScannerArachni Black Widow ZAP Spider-ScentsRSVRSVRSVRS VCMSMS00000000 018 - 8 (8)Doctor Apt.52210041 18 - 4 (2)Hospital54444426 1 133 - 30 (22)Hostel13 13 1333300 023 - 19 (6)Joomla00000010 09- 0MyBB00000000 06 - 6 (6)OpenCart00000000 06- 0Piwigo00011100 05 - 1 (1)PrestaShop00000000 03- 0SCARF00010 9 900 012 - 11 (2)User Login00000000 03 - 3 (3)WordPress00000000 07 - 3 (3)Total23 19 1919 17 1731 2 2133 - 85 (53)Overall, our approach Þnds 85 stored XSS vulnerabilitiesthat other scanners should be able to Þnd, compared to the15, on average, that they do Þnd. 53 of these vulnerabilitiesfound by our approach are unique and new.With the exception of the Piwigo vulnerability BlackWidow Þnds, which we discuss in Section6.3, we Þnd allstored XSS the other scanners Þnd.Notably, classic black-box scanners still struggle to Þndstored XSS, as indicated by the relatively low numbers inTable1. There are some notable outliers, such as ZAP report-ing 26 XSS on the Hospital Management System. However,as later clariÞed by manual analysis, this is a single storedpayload being mislabeled as multiple XSS vulnerabilities.Exploitability.While scanners report on user input beingexecuted as JavaScript, they fall short of understanding theexploitabilityof the vulnerability. In this section, we breakdown the vulnerabilities we Þnd with Spider-Scents into un-protected output, unprotected inputs, and unprotected permis-sions, deÞned in Section2. We deÞne the unprotected input asan input Þeld where it is possible to add a payload without itbeing escaped, stripped, or subject to validation, as describedin Section5.2.1.In Table2we present the results from our approach andexploitability analysis. Interestingly, we note that there is adiverse mix of input protection methods, even within oneapplication. For example, in CMS Made Simple, escapingis used for the userÕs Þrst and last name, while stripping isused for the email, and validation is used for the content alias.Nevertheless, the application still failed to properly sanitizeits output.Moreover, complex and dynamic user roles in modern ap-plications make it difÞcult to automatically reason about theimpact of XSS. For example, in CMSMS there is a binary --- page 22 --- 6750 33rd USENIX Security Symposium USENIX Association --- page 23 --- User LoginZAPOpenCartArachniOpenCartZAPJoomlaZAPJoomlaArachniOpenCartBWJoomlaBWPrestaShopZAPMyBBArachniDoctor Apt.BWCMSMSArachniPrestaShopBWPrestaShopArachniCMSMSZAPMyBBZAPCMSMSBWDoctor Apt.ZAPWordPressArachniSCARFArachniPiwigoBWPiwigoArachniSCARFZAPMyBBBWPiwigoZAPHostelArachniHostelBWHospitalZAPHospitalBWDoctor Apt.ArachniWordPressBWHospitalArachniHostelZAPWordPressZAPUser LoginArachniUser LoginBWSCARFBW0%20%40%60%80%100%Our ScannerCommonOther scannerFigure 5: For every bar we present the fraction of database columns affected. First, on top, the fraction of columns onlySpider-Scents Þnds, the middle shows the fraction of columns both scanners Þnd, and Þnally, on the bottom, the fraction ofcolumns only the other scanner Þnds.Figure 6: Subset of results from scanning the Piwigo webapplication. Black lines indicate protected outputs while redlines indicate unprotected outputs.option for permission to modify bookmarks, that can be as-signed to any user group. Any user with this permission canabuse an XSS to gain privileges. In contrast, MyBB has astrict separation of admin conÞgurations and forum modera-tion conÞgurations. This means that any XSS a scanner Þnds,including ours, while authenticated as an admin in MyBB,could be regarded as self-XSS as only trusted parties controlthe input. Similarly in Piwigo, the page title is vulnerable toXSS, however, only the admin can change it. As we see inTable2, while both MyBB and CMSMS fail to escape all out-puts, MyBB is less exploitable due to its stricter permissions.In the Doctor Appointment Management System, neitheroutput protection nor input protection is used. Despite this,two vulnerabilities are not exploitable because of permissions.SpeciÞcally, while users can change their own email address,only doctors (super users in this context) can change a doctorÕsname. Still, these are not fully protected, as the applicationwith its default settings is also vulnerable to CSRF attacks.These vulnerabilities can be combined to exploit the XSS.Therefore, we believe it is useful for developers to learn whereunprotected outputs are so that they can be Þxed, even if theyare protected by permissions.6AnalysisIn this section, we investigate our results and highlight limita-tions of both black-box scanners and our approach.6.1Database coverageWhile our method generally achieves higher database cover-age, there are some interesting cases where other scannersstill perform better in this metric.In our evaluation, the portion of the database we miss andother scanners can reach is a result of our payloadÕs length.Our payload length is always at least 30 characters long,making it too big for some cells. For example, on PiwigoBlack Widow can affect theoc_customer_ip.countrycol-umn, which only holds two characters. ZAP also modiÞesmybb_templates.version, with a size of 20 characters.This could be enough for some XSS payloads.In theory, we can miss Þnding possible vulnerabilities ifchanging a non-text (numeric or date) value is necessary totrigger a vulnerability. Foreign key constraints can also causeproblems but are less common in the text Þelds we focus on. --- page 24 --- USENIX Association 33rd USENIX Security Symposium 6751 --- page 25 --- Table 2: Exploitability of reported vulnerabilities.T- Totalreßections,NI- No Input,VA- Validation,ST- Stripping,ESC- Escaping,P- Permission,EXP- Exploitable. * ACSRF vulnerability could be abused to exploit it. ** Poorauthentication validation allows privilege escalation.TNIVASTESCPEXPCMSMS18313308Doctor Apt.840002*2Hospital3330006*24Hostel2340004**15Joomla9504000MyBB6000060OpenCart6000600Piwigo5130010PrestaShop3030000SCARF1210007*4User Login3000003WordPress70013036.2False positivesBlack-box scanners use a variety of methods to detect injectedXSS payloads, which can result in false positives. ZAP, forexample, incorrectly identiÞed XSS in WordPress. It staticallyfound the injected token;alert(1);in a JavaScript context.However, the token was inside a string, which in this case itis not possible to break out of.Confusing multiple payloads is another problem many scan-ners face. In the Hospital Management System, ZAP can suc-cessfully inject an XSS payload into the database. However,it does not detect this as a stored XSS, and is confused whenit later scans for DOM-based XSS with the same payload,alert(5397), which ZAP does Þnd. This is caused in partby the number 5397 not being random but a constant, deÞnedin the code asUNLIKELY_INT. Therefore, for a single storedXSS vulnerability, ZAP instead reports 26 DOM-based XSS.In this case, mistaking DOM XSS for stored XSS can impactdevelopers who are unable to reproduce the results when thedatabase is reset.Our approach can avoid many of these problems by usingunique IDs for each cell in the database and dynamicallytesting that each payload is executed. As such, similarly toBlack Widow, we have a low rate of this type of false positive.In contrast to black-box scanners, our approach does not au-tomatically verify that an unprotected input exists. Therefore,we might report a database cell that cannot be changed bythe web application. For example, in the Hostel ManagementSystem, the list of US states were not escaped on output, butwere all hard-coded. We argue that when more functionalityis added, either through software updates or third-party codesuch as plugins, it can introduce a vulnerability, and as suchthese reports are important.6.3What we missIn the complex setting of web applications, there might alwaysbe more unknown vulnerabilities. In the absence of groundtruth, in line with previous work [9Ð11,33,47,49], our falsenegative comparison baseline is the stored XSS results ofother scanners.In Piwigo our method can Þnd a reßected value, but notXSS, for a value in the conÞguration table. However, uponfurther manual analysis, we note that the reason we did notÞnd the XSS vulnerability was because of the payload chosen.In this case, the value was reßected inside a textarea, meaningatag was needed to break out and executeJavaScript.Black Widow found one XSS on Piwigo that we missed.While we were able to add the payload and Þnd the correctURL, the URL was too late in the reßection scannerÕs queueand was therefore never visited. Increasing the reßection scan-nerÕs timeout would solve this, at the cost of runtime perfor-mance.We have only implemented a prototype that demonstratesthe utility of our approach. As with all scanners, the choicestaken in that implementation can lead to false negatives. Falsenegatives can stem from missing SQL analysis that can limitour interaction with the database, such as foreign keys andother constraints, and triggers. Better authentication, and re-authentication, mechanisms would also improve our approach.Replaceable components to our method, such as the reßection-crawling and payload-selection modules, are shown to be thecause for some of the false negatives in this section.In addition to our evaluated comparison with the other scan-ners, we also survey vulnerabilities from CVEs and previousacademic papers to construct a dataset of known vulnerabili-ties in our choice of evaluated web applications. In this dataset,33 previous reports covered 29 unique stored XSS vulnerabil-ities, of which we Þnd 26 (details in Table6). In WordPress,we miss a vulnerability that relies on an attacker-controlledserver that returns a crafted message to a link embedded in apost. For all evaluated methods, including ours, this type ofattack is out-of-scope. However, it should be noted that wedo correctly mark the database column as unprotected output.In CMSMS, we miss a stored XSS vulnerability in the Þle-name of uploaded Þles. Here the payload is not stored in thedatabase but rather in the Þlesystem. Extending our methodfrom databases to other storage mechanisms could be an av-enue for future work. Finally, in OpenCart, we miss a storedXSS in the category description because we do not spendenough time crawling the application after database modiÞ-cations, to scan for reßections of the inserted payload. Afterextending the reßection scannerÕs timeout, Spider-Scents wasable to Þnd this vulnerability as well. --- page 26 --- 6752 33rd USENIX Security Symposium USENIX Association --- page 27 --- 6.4What others missFrom the scanning results of the Doctor Appointment Man-agement System, we can see that ZAP fails to detect multipleXSS vulnerabilities. By analyzing database snapshots beforeand after execution we note that ZAP was only able to insertdata into thetbldoctortable. While it was able to add thestring ÒZAPÓ to theFullNamecolumn, it could not add anXSS payload. Furthermore, ZAP misses other tables, suchastblpageandtblappointment, that our method modiÞesand detects as unprotected output.We also note cases where the compared scanners failto Þnd XSS due to a lack of database coverage. For ex-ample, in MyBB, no other scanner affects the vulnerablemybb_usergroups.namestylecolumn.6.5ExploitabilityBoth black-box scanners and our method will report on in-jected JavaScript being reßected and executed. However, aswe see in Table2, not all these executions could, in theircurrent form, be exploitable. Interestingly, we note that webapplications are relatively equally split on using sanitizationand escaping on user input. Validation, on the other hand, isless common. Moreover, permissions also play an importantrole in protecting theseXSS vulnerabilitiesfrom becomingexploitable.6.6Drop-in testing with Spider-ScentsIn our evaluation, we assisted other scanners by modifying theweb applications under test, so they could evade typical loginchecks. While beneÞcial for increasing their performance forthe sake of comparison, such modiÞcations are not ideal.To demonstrate the applicability of Spider-Scents, wedonot modifyweb applications when we run Spider-Scents.Therefore, we rely entirely on our breakage heuristics, auto-matic reverting, and automatic use of captured log-in details.6.7Manual analysis with Spider-ScentsSpider-Scents requires more manual effort to verify a vul-nerability compared to a black-box solution. However, asSpider-Scents reports the corresponding database table andcolumn, e.g.users.email, it is usually relatively easy tomanually Þnd relevant input Þelds and test for a working XSSpayload, as described in Section4.8. In our evaluation, it tookan author approximately 15 minutes per report, on average.Preparing Spider-Scents to scan takes a similar time to otherscanners, with the small addition of database credentials.Further automation to ease analysis is possible, such asmapping input Þelds to the database, although this will requireaddressing general challenges of crawling, such as explorationand input validation [11].6.8Runtime performance of Spider-ScentsIn contrast to other black-box scanners, which can run indef-initely [11], our method runs for a time proportional to theTable 3: Runtime performance of Spider-Scents. Runtime isproportional to the size of the database of the application,reported in both the raw number of database cells and thosethat Spider-Scents can scan (satisfy payload requirements).Scan timeDatabase cellsScannableCMSMS7:14118444339Doctor Apt.0:0819587Hospital0:22282106Hostel0:1320590Joomla12:59115844813MyBB4:21157015321OpenCart1:393149011553Piwigo1:071826520PrestaShop32:294452910745SCARF0:063615User Login0:01106WordPress1:55385868database of the application. In our evaluation, other black-box scanners are limited to a runtime of 8 hours. Spider-Scents almost always completed its scans within this timewindow, with the exception of modern applications Joomlaand PrestaShop. Reference applications are scanned withinminutes, while modern applications are scanned in hours.We report the scan time of Spider-Scents in evaluation inTable3. These times are collected on a laptop from 2021with 8 cores and 16 gigabytes of RAM, running both theapplicationÕs web server and the Spider-Scents scanner.6.9Coordinated disclosureWe have reported all new vulnerabilities to the affected ven-dors and will summarize their responses here. MyBB is plan-ning to Þx the vulnerabilities we reported in the upcoming 1.9version. The CMSMS developers argue that any authenticatedXSS (regardless of the speciÞc user/group permissions) isnot considered a vulnerability. Instead, they will revise theirdocumentation to no longer motivate their permission modelas a Òsecurity mechanismÓ. WordPress, on the other hand,does consider some authenticated XSS as vulnerabilities, de-pending on permission. However, their security model differsfrom that evaluated. In our model, we considered any privi-lege escalation as a vulnerability, while WordPress developersconsidereditorandadminto be equivalent. As such, theredoes not seem to be a consensus among web developers as tohow application permissions should be modelled. We are stillwaiting for a response from PHPGurukul for vulnerabilitiesin their multiple applications. However, these vulnerabilitieshave a clearer precedence with similar vulnerabilities to ours,e.g.CVE-2023-27225. --- page 28 --- USENIX Association 33rd USENIX Security Symposium 6753 --- page 29 --- 6.10SummaryAs the results show, Spider-Scents performs both better indatabase coverage and stored XSS vulnerability detectionwhen compared to state-of-the-art scanners. Based on whatvulnerabilities the other scanners miss and what we uniquelyÞnd, we believe the reason for this improved performance isbecause we bypass the majority of the roadblocks that cur-rent XSS scanners face (as deÞned in Section3). Solvingthese challenges directly is a substantially harder problem [8],and will require solving fundamental challenges with crawl-ing [11]. In many instances, the other scanners fail to get anydata into the vulnerable database column for vulnerabilitiesonly Spider-Scents Þnds, and in other cases when they do,only benign data is added. In general, the main problem cur-rent scanners face, which we bypass, is getting the payloadinto the database.7Related Work7.1Black-box scannersEnemy of the State [7] models server-side state in differentlinks and requests are identiÞed that drive such state changes.Notably, this work recognizes the necessity of a solution toresetting a web application. In this case, the application is runin a VM, and the machine is reset to counteract irreversiblestate changes. Spider-Scents does not need a VM, and resetscan be done in a granular and inexpensive fashion. Further-more, Enemy of the StateÕs access to the VM subsumes thispaperÕs access to the database.LigRE [9] and KameleonFuzz [10] also focus on server-side state. LigRE improves XSS detection with taint ßowinference, and KameleonFuzz adds genetic algorithms forpayload generation and modiÞcation. Similar to Enemy ofthe State, these approaches require the ability to reset the webapplication.jŠk [33] instead focuses on modelling client-side state.JavaScript APIs are hooked to be able to model dynamic be-haviour. The crawler generates a navigation graph includingthis information.CrawlJax [25] also models client-side state. Interactablecandidate elements, such as clickable ones, are interacted withto extend the crawlerÕs reach. A state-ßow graph models theuser interface.Black Widow [11] identiÞes key fundamental challengesfor black-box scanning. They mitigate them by combiningnavigation graphs, workßows, and inter-state dependenciesin one XSS scanner. In contrast to prior work, Black Widowdoes not assume the ability to reset the web application.7.2White-box scannersSaner [4] focuses on identifying improper sanitization to Þndvulnerabilities such as XSS and SQLi. Saner is limited toanalyzing PHP, and even more to custom sanitization routines.To reduce complexities with application state Ð such as thedatabase Ð Saner does not interact with a live instance of theweb application, instead choosing to build a model of thesanitization process from static analysis results.Restler [3] does not use the entire applicationÕs codebase,but instead only the REST API speciÞcation. Static analy-sis of this speciÞcation identiÞes inter-request dependenciesto generate tests, which generate dynamic feedback execu-tion to guide further testing. Similar to Spider-Scents in bothanalyzing a different artifact/interface than typical static orÔgrey-boxÕ analyses, Restler also focuses on bugs. Indeed, theynote that vulnerabilities in a REST speciÞcation are unclear.Sentinel [24] seeks to limit access to sensitive data in thedatabase to SQLi attacks. The authors model web applicationsto identify invariants for the ÔnormalÕ functionality, which theyuse to examine queries and responses to block malicious SQLusage.7.3Grey-box scannersMost prior grey-box approaches inform a white-box scan withsome runtime information from a black-box scan, to reducethe false positive rate and generate a full exploit proof. Weargue that only needing database access is more general thansource code7. It is easier to apply our approach to a differentweb application. Being almost black-box, we are agnosticto the coding language and framework for the applicationÕsimplementation. White-box approaches might not be able tohandle obfuscated code. Obfuscated code can also be presentdue to extensions, such as plugins. We also do not replace thedatabase or insert some proxy between the database and theapplication. This makes it easy to adapt our approach to otherstorage mechanisms.webfuzz [50] instruments code for coverage, and uses thisfeedback to fuzz requests for detecting reßected and storedXSS. This approach is expensive - WordPress reaches 27%coverage in 2000 minutes.Witcher [49] identiÞes issues with using grey-box coverageto guide a web application fuzzer for vulnerability discovery.A Fault Escalator is deÞned to detect when the application isin some vulnerable state, and guide the fuzzer to escalate thatto a vulnerability. Together with a reÞned notion of coverage,Witcher can fuzz URLs to Þnd command and SQL injection.This approach is limited to Þrst-order reßected vulnerabilitiesand does not model application state.Gelato [18] detects reßected and DOM-based XSS. Taintanalysis is used to target exploration of the large state spaceof modern JavaScript.Backrest [14] statically infers a model REST API, thenuses coverage and taint feedback to drive fuzzing of requestsfor detecting SQLi, XSS, and command injection. While themotivations are to both improve coverage and runtime, theruntime improvements are more evident. Notably, XSS detec-7Static analysis can also be performed on compiled binaries or intermedi-ate representations. However, the same arguments against generality apply tothose other artifacts. --- page 30 --- 6754 33rd USENIX Security Symposium USENIX Association --- page 31 --- tion, especially stored XSS, is reduced when provided withfeedback. The authors point out the problem of followingtaint across the interface with storage in a database.Chainsaw [1] implements automated exploit generation,where potential vulnerabilities derived from static analysisare dynamically tested, with successful executions being con-crete exploits. Symbolic execution of PHP is used to Þndsources and sinks, as well as sanitizations/transformationsalong paths. The database is regarded as an additional in-put to the application, with the database schema consumed.Workßow-based vulnerabilities, such as stored XSS, are foundwithin a comparable 600 minutes.7.4Database-aware grey-box web scanningSimilarly to Spider-Scents, Steinhauser and Tûuma utilizea grey-box approach for detecting context-sensitive XSSusing the database alongside a normal black-box scanner[47]. They deal with context-sensitivity in line with Context-Auditor [23].Steinhauser and TûumaÕ s grey-box approach interceptsdatabase and web application communication, injecting non-XSS payloads into the application by replacing data comingfrom the database.XSS ßaws are detected by black-box parsing of HTMLresponses from the application matching portions of payloadsin responses, to detect the payload if the application appliessome common encodings. The parser continues with the pos-sible XSS ßaw, and payloads are iteratively modiÞed to avoidcontext encoding. Some automatic reports of XSS ßaws mustthen be manually analyzed to identify vulnerabilitiesThis approach is substantially different from that of Spider-Scents. We achieve a different, more complete form of cover-age, by iterating through the contents of the database, insteadof only modifying values as they are retrieved by the requestsfrom a black-box scanner. This approach1skips missingdatabase entries, with the database only populated by pre-provided conÞgs or manually sampled data from public demoinstances. We also scale differently; with the database mod-elled asadditional inputs, this approach of extending HTTPrequest Þelds can become2severalorders of magnitudeslowerthan the base black-box scanner. For efÞciency,alldatabase injectionsare combined per request,3which au-thors note increases breakage, without proposing a solution.The applications under test are also4substantially modiÞedto aid the scanner. Finally, Steinhauser and TûumaÕs approachis implemented by5extending MariaDB, which replacesthe database in the tested web application.In contrast, Spider-Scents1augments the database,2islightweight in our evaluation,3identiÞes and Þxes applica-tion breakage,4works without modifying web applications,and5is implemented without a heavy-weight database re-placement or proxy.In terms of results, we share Joomla and PrestaShop. Stein-hauser and Tûuma veriÞed 5 and 12 vulnerabilities in Joomlaand PrestaShop, with their reports resulting in all Joomla andsome PrestaShop ßaws being Þxed. As all Joomla ßaws re-ported were Þxed, the 9 we identify either are missed by theirapproach or come from further development of Joomla. Unfor-tunately, source code artifacts for this paper are unavailable,so we cannot do a direct comparison.7.5Database synthesisSynthDB [6] is a recent work addressing the tangential prob-lem of preparing database-backed web applications for secu-rity testing, such as vulnerability scanning, by synthesizinga database. In contrast to our simplistic approach, with thesingular goal of having some data in every table while cor-relating inserted Þelds across tables, SynthDB uses concolicexecution of PHP source to collect database constraints. Theseconstraints are solved to uncover more program paths, whilenot violating Ôdatabase integrityÕ. Similar to Spider-Scents,the performance of scanners such as Burp is improved withthis white-box preprocessing step.Our approach also uncovers a separate problem - Þndingaminimaldatabase. In the osCommerce application, thereare over 3 million cells in the base application.Our approachmust have its parameters tuned to handle this volume of cells.However, we have found this applicationÕs scale abnormal byseveral orders of magnitude; other web applications typicallyonly have hundreds to tens of thousands of cells.8ConclusionBlack-box vulnerability scanners are the best tools currentlyavailable for democratizing security testingÑallowing webdevelopers with no security background or knowledge toproactively Þnd vulnerabilities in their web applications. How-ever, the twisted designs and logic of web applications makeit difÞcult for black-box vulnerability scanners to even in-ject XSS payloads into the web application. Our approachcuts this Gordian Knot of properly supplying inputs to a webapplicationÑby injecting the XSS payloads directly into thedatabase. We believe that this approach represents a step for-ward in automatic stored XSS detection, and the evaluationresults show that our Spider-Scents prototype surpasses state-of-the-art black-box vulnerability scanners, while our manualsystematization provides the necessary contextualization ofvulnerability and exploitability to these results.AcknowledgementsThis work was partially supported bythe Wallenberg AI, Autonomous Systems and Software Pro-gram (WASP) funded by the Knut and Alice Wallenberg Foun-dation, the Swedish Foundation for Strategic Research (SSF),and the Swedish Research Council (VR).References[1]Abeer Alhuzali, Birhanu Eshete, Rigel Gjomemo, andVN Venkatakrishnan. Chainsaw: Chained automatedworkßow-based exploit generation. InCCS, 2016. --- page 32 --- USENIX Association 33rd USENIX Security Symposium 6755 --- page 33 --- [2]Arachni.https://www.arachni-scanner.com.[3]Vaggelis Atlidakis, Patrice Godefroid, and Marina Pol-ishchuk. Restler: Stateful rest api fuzzing. 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InICITST, 2015. --- page 34 --- 6756 33rd USENIX Security Symposium USENIX Association --- page 35 --- [33]Giancarlo Pellegrino, Constantin TschŸrtz, Eric Bodden,and Christian Rossow. jŠk: Using dynamic analysis tocrawl and test modern web applications. InRAID, 2015.[34]Hui Peng, Yan Shoshitaishvili, and Mathias Payer. T-Fuzz: fuzzing by program transformation. InS&P, 2018.[35]PHPGurukul.https://phpgurukul.com/hospital-management-system-in-php/.[36]PHPGurukul.https://phpgurukul.com/sdm_downloads/login-system/.[37]PHPGurukul.https://phpgurukul.com/doctor-appointment-management-system-using-php-and-mysql/.[38]PHPGurukul.https://phpgurukul.com/hostel-management-system/.[39]Piwigo.https://piwigo.org.[40]Prestashop.https://prestashop.com.[41]The Menlo Report.https://www.dhs.gov/sites/default/files/publications/CSD-MenloPrinciplesCORE-20120803_1.pdf.[42]Mike Samuel, Prateek Saxena, and Dawn Song. Context-sensitive auto-sanitization in web templating languagesusing type qualiÞers. InCCS, 2011.[43]Prateek Saxena, Steve Hanna, Pongsin Poosankam, andDawn Song. Flax: Systematic discovery of client-sidevalidation vulnerabilities in rich web applications. InNDSS, 2010.[44]Prateek Saxena, David Molnar, and Benjamin Livshits.Scriptgard: Automatic context-sensitive sanitization forlarge-scale legacy web applications. InCCS, 2011.[45]SCARF.https://scarf.sourceforge.net.[46]CMS Made Simple.http://www.cmsmadesimple.org.[47]Anton’ n Steinhauser and Petr Tûuma. Database trafÞcinterception for graybox detection of stored and context-sensitive XSS.Digital Threats: Research and Practice,2020.[48]OWASP Top Ten.https://owasp.org/www-project-top-ten/.[49]Erik Trickel, Fabio Pagani, Chang Zhu, Lukas Dresel,Giovanni Vigna, Christopher Kruegel, Ruoyu Wang,Tiffany Bao, Yan Shoshitaishvili, and Adam DoupŽ.Toss a fault to your witcher: Applying grey-boxcoverage-guided mutational fuzzing to detect sql andcommand injection vulnerabilities. InS&P, 2022.Table 4: Web applications used in the evaluationApplication Date Version GitHub Stars Lines of Code Prior ResearchCMSMS 2022 2.2.16&144944Doctor Apt. 2023 2023/1/11&65603 [49]Hospital 2022 2022/11/8&67667 [49]Hostel 2021 2021/9/30&9377Joomla 2023 4.2.84.5k747197 [11,33,47,50]MyBB 2023 1.8.33932153055 [33]OpenCart 2023 4.0.1.16.8k186101Piwigo 2023 13.6.02.6k280906 [33]PrestaShop 2022 1.7.8.87.3k1175530 [11,47]SCARF 2007 2007/2/27&1318 [7,11,24]User Login 2021 V3&7036 [49]WordPress 2023 6.1.117.6k651599 [9Ð11,33,49,50][50]Orpheas van Rooij, Marcos Antonios Charalambous,Demetris Kaizer, Michalis Papaevripides, and EliasAthanasopoulos. webfuzz: Grey-box fuzzing for webapplications. InESORICS, 2021.[51]WordPress VIP.https://docs.wpvip.com/technical-references/security/validating-sanitizing-and-escaping/.[52]WordPress.https://developer.wordpress.org/apis/security/escaping/.[53]WordPress.https://wordpress.com.[54]OWASP ZAP.https://www.zaproxy.org.AAppendixA.1Spider-Scents conÞgurationWe have implemented a variety of tunable parameters for con-Þguring Spider-ScentsÕ choice of heuristics while scanning.Some notable parameters are:¥Avoid sensitive rows or not¥Insert rows into empty tables or not¥ConÞgure the traversal through the database (order by table,row, column, random, reverse)¥Breakage threshold and detection type (based on statuscodes, response length, link content)¥Enforce independence across boundaries (across table, row,column)We evaluate our approach avoiding sensitive rows, insertinginto empty tables, traversing the database by tables and thencolumns, with breakage sensitive to status codes and allowingup to 50% of link content to be missing, and independenceenforced across bounds. --- page 36 --- USENIX Association 33rd USENIX Security Symposium 6757 --- page 37 --- Table 5: The number of unique database columns affected by each scanner. For each column in the table, we present: databasecolumns only covered by Spider-Scents (A\B), columns covered by both scanners (A'B), and columns covered by the otherscanner (B\A). The very last column presents the maximum number of columns that allow arbitrary text values.CrawlerArachni Black Widow ZAPMAXA\BA'BB\AA\BA'BB\AA\BA'BB\ACMSMS85 13 182 16 385 13 2111Doctor Apt.6 10 014 2 012 4 016Hospital14 28 220 22 221 21 244Hostel15 19 015 19 07 27 036Joomla283 12 1281 14 1287 8 0325MyBB194 15 5133 76 15184 25 7264OpenCart282 3 0272 13 1278 7 0326Piwigo37 21 141 17 232 26 263PrestaShop306 55 3313 48 4329 32 2410SCARF10 5 02 13 087015User Login2412416007WordPress35 9 222 22 516 28 753Table 6: Known stored XSS vulnerabilities from CVEs and other publications.Application SourceDescription We FindCMSMS CVE-2023-36970File upload stored XSS!Hospitalhttps://github.com/Ko-kn3t/CVE-2020-25271username"Hospitalhttps://sisl.lab.uic.edu/projects/chess/cross-site-scripting-in-hms3weight"Hospitalhttps://sisl.lab.uic.edu/projects/chess/cross-site-scripting-in-hms3temperature"Hospitalhttps://sisl.lab.uic.edu/projects/chess/cross-site-scripting-in-hms3medicalpres"Hospitalhttps://sisl.lab.uic.edu/projects/chess/cross-site-scripting-in-hms3BloodPressure"Hospitalhttps://sisl.lab.uic.edu/projects/chess/cross-site-scripting-in-hms3BloodSugar"Hospitalhttps://sisl.lab.uic.edu/projects/chess/cross-site-scripting-in-hms2PatientName"Hospitalhttps://sisl.lab.uic.edu/projects/chess/cross-site-scripting-in-hms2PatientEmail"Hospitalhttps://sisl.lab.uic.edu/projects/chess/cross-site-scripting-in-hms2PatientGender"Hospitalhttps://sisl.lab.uic.edu/projects/chess/cross-site-scripting-in-hms2PatientAdd"Hospitalhttps://sisl.lab.uic.edu/projects/chess/cross-site-scripting-in-hms2PatientMedhis"Hospitalhttps://www.exploit-db.com/exploits/47841doctorspecilization"Hostel CVE-2020-25270guardianName"Hostel CVE-2020-25270guardianRelation"Hostel CVE-2020-25270corresAddress"Hostel CVE-2020-25270corresCIty"OpenCarthttps://github.com/nipunsomani/Opencart-3.x.x-Authenticated-Stored-XSS/blob/master/README.mdCategory description!SCARF [11]Add session"SCARF [11]Comment"SCARF [11]Conference name"SCARF [11]Edit paper"SCARF [11]Edit session"SCARF [11]Delete comment"SCARF [11]General options"SCARF [11]User options"User Login CVE-2022-43097, CVE-2020-23051, CVE-2020-24723fname"User Login CVE-2022-43097, CVE-2020-23051, CVE-2020-24723lname"WordPresshttps://research.securitum.com/xss-in-wordpress-via-open-embed-auto-discoveryEmbed in post content! --- page 38 --- 6758 33rd USENIX Security Symposium USENIX Association --- page 39 --- !"#$%"$&'(#")&"*)+#(,-,.,"$&/%'&01!+'2,03!&(,-,'+2&'($"!!&0+&'1%0!"*)+#(,-,.,"$&/'+2&1%,*"2&4'+2&/52-&)&'(#")& --- page 40 --- oúz‡-9òt ½FnϾ‘D-Ûbïo­äF¬…œ�l²ÝŽ