---
type: Article
title: Toward Black-Box Detection of Logic Flaws in Web Applications
description: "A black-box method that infers a behavioural model of a web application from recorded HTTP traces, abstracts requests into resources, then generates test cases that replay, reorder or skip steps to break the intended workflow. It exposes logic flaws in e-commerce software that allow shopping for free and hijacking another user's session."
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title: Toward Black-Box Detection of Logic Flaws in Web Applications
author: Giancarlo Pellegrino, Davide Balzarotti
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- Giancarlo Pellegrino
- Davide Balzarotti
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# Toward Black-Box Detection of Logic Flaws in Web Applications
**Toward Black-Box Detection of Logic Flaws in Web Applications** - Giancarlo Pellegrino, Davide Balzarotti, Publisher not stated.
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# Toward Black-Box Detection of Logic Flaws in Web Applications
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Toward Black-Box Detection of Logic Flaws in WebApplicationsGiancarlo PellegrinoEURECOM, FranceSAP Product Security Research, Francegiancarlo.pellegrino@eurecom.frDavide BalzarottiEURECOM, Francedavide.balzarotti@eurecom.frAbstractÑWeb applications play a very important role inmany critical areas, including online banking, health care, andpersonal communication. This, combined with the limited securitytraining of many web developers, makes web applications one ofthe most common targets for attackers.In the past, researchers have proposed a large number ofwhite- and black-box techniques to test web applications for thepresence of several classes of vulnerabilities. However, traditionalapproaches focus mostly on the detection of input validation ßaws,such as SQL injection and cross-site scripting. Unfortunately,logic vulnerabilities speciÞc to particular applications remainoutside the scope of most of the existing tools and still needto be discovered by manual inspection.In this paper we propose a novel black-box technique to detectlogic vulnerabilities in web applications. Our approach is basedon the automatic identiÞcation of a number of behavioral patternsstarting from few network traces in which users interact witha certain application. Based on the extracted model, we thengenerate targeted test cases following a number of common attackscenarios.We applied our prototype to seven real world E-commerceweb applications, discovering ten very severe and previously-unknown logic vulnerabilities.I. INTRODUCTIONWeb applications play a very important role in manycritical areas, and are currently trusted by billions of users toperform Þnancial transactions, store personal information, andcommunicate with their friends. Unfortunately, this makes webapplications one of the primary targets for attackers interestedin a wide range of malicious activities.To mitigate the existing threats, researchers have proposeda large number of techniques to automatically test web appli-cations for the presence of several classes of vulnerabilities.Existing solutions span from black-box fuzzers and pentestingtools to static analysis systems that parse the source code ofan application looking for well-deÞned vulnerability patterns.However, traditional approaches focus mostly on the detectionof input validation ßaws, such as SQL injection and cross-sitescripting. To date, more subtle vulnerabilities speciÞc to thelogic of a particular application are still discovered by manualinspection [33].Logic vulnerabilities still lack a formal deÞnition, but,in general, they are often the consequence of an insufÞcientvalidation of the business process of a web application. Theresulting violations may involve both the control plane (i.e., thenavigation between different pages) and the data plane (i.e., thedata ßow that links together parameters of different pages). Inthe Þrst case, the root cause is the fact that the applicationfails to properly enforce the sequence of actions performedby the user. For example, an application may not require auser to log in as administrator to change the database settings(authentication bypass), or it may not check that all the stepsin the checkout process of a shopping cart are executed inthe right order. Logic errors involving the data ßow of theapplication are caused instead by failing to enforce that theuser cannot tamper with certain values that propagate betweendifferent HTTP requests. As a result, an attacker can try toreplay expired authentication tokens, or mix together the valuesobtained by running several parallel sessions of the same webapplication.Formal speciÞcations describing the evolution of the inter-nal state and of the expected user behavior are almost neveravailable for web applications. This lack of documentationmakes it very hard to Þnd logic vulnerabilities. For example,while being able to add several times the same product to ashopping cart is a common feature, being able to add severaltimes the same discount code is likely a logic vulnerability.A human can easily understand the difference between thesetwo scenarios, but for an automated scanner without the properapplication model it is very hard to tell the two behaviors apart.Only recently the research community has started investi-gating automated approaches to detect logic vulnerabilities [9,18, 21]. Unfortunately, the existing solutions have seriousscalability problems that limit their applicability to smallapplications. Moreover, the source code of the application isoften required in order to extract a proper model to guide thetest case generation. As a result, to date the impact of availableautomated tools has been quite limited.As an alternative approach, researchers have recently re-PermissiontofreelyreproduceallorpartofthispaperfornoncommercialpurposesisgrantedprovidedthatcopiesbearthisnoticeandthefullcitationontheÞrstpage.ReproductionforcommercialpurposesisstrictlyprohibitedwithoutthepriorwrittenconsentoftheInternetSociety,theÞrst-namedauthor(forreproductionofanentirepaperonly),andtheauthorÕsemployerifthepaperwaspreparedwithinthescopeofemployment.NDSSÕ14,23-26February2014,SanDiego,CA,USACopyright2014InternetSociety,ISBN1-891562-35-5http://dx.doi.org/
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sorted to manual analysis to expose several severe logic ßawsin real world commercial applications [34, 35] resulting, forinstance, in the ability to shop online for free. Following thestep of these previous works, in this paper we show thatit is possible to automatically infer an approximate modelof a web application starting from a few network traces inwhich a user ÒstimulatesÓ a certain functionality. Our goalis not to automatically reconstruct an accurate model of theapplication or of its protocol (several works already exist in thisdirection [14, 15]) but instead to empirically show that evena simple representation of the application logic is sufÞcient toperform automated reasoning and to generate test cases thatare likely to expose the presence of logic vulnerabilities.In this paper we propose a technique that analyzes networktraces in which users interact with a certain applicationÕsfunctionality (e.g., a shopping cart). We then apply a set ofheuristics to identify behavioral patterns that are likely relatedto the underlying application logic. For example, sequences ofoperations always performed in the same order, values that aregenerated by the server and then re-used in the following userrequests, or actions that are never performed more than once inthe same session. These candidate behaviors are then veriÞedby executing very speciÞc test cases generated according toa number of attack patterns. It is important to note that ourapproach is not a fuzzer, and both the trace analysis and thetest case generation steps are performed ofßine. In other words,they do not require to probe the application or generate anyadditional interaction and network trafÞc.While our approach is application-agnostic, the choice ofthe attack patterns reßects a particular class of logic ßawsand application domain Ñ and in our case were customizedfor E-commerce applications. In particular, we applied ourprototype to seven large shopping cart applications adoptedby millions of online stores. The prototype discovered tenpreviously-unknown logic ßaws among which Þve of themallow an attacker to pay less or even shop for free.In summary, this paper makes the following contributions:1)We introduce a new black-box technique to testapplications for logic vulnerabilities;2)We present the implementation of a tool based on ourtechnique and we show how the tool can be used totest several real web applications, even with a verylimited knowledge and a small number of networktraces;3)We discover ten previously-unknown vulnerabilitiesin well-known and largely deployed web applications.Most of these vulnerabilities have a very high impactand would allow an attacker to buy online for freefrom hundreds of thousands of online stores.Structure of the paper. Section II presents the black-boxapproach. Section III describes the experiments that we per-formed and Section IV shows the results. Section V discussesthe limitations of our approach and Section VI presents relatedwork on detecting logic vulnerabilities. Finally, Section VIIconcludes the paper.II.APPROACHThe OWASP Testing Guide 3.0 [33] suggests a four-stepapproach to test for logic ßaws in a black-box setting. First, thetester studies and understands the web application by playingwith it and reading all the available documentation. Second,she prepares the information required to design the tests,including theintended workßowand thedata ßow. Then sheproceeds with the design of the test cases, e.g., by reorderingsteps or skip important operations. Finally, she sets up thetesting environment by creating test accounts, runs the tests,and veriÞes the results.Our approach aims at automating the previous steps in asingle black-box tool. First, starting from a list of networktraces containing HTTP conversations, our system infers anapplication model and clusters resources related to the sameworkßow ÒstepÓ (Section II-A). Second, our technique an-alyzes the model and extracts a set ofbehavioral patterns(Section II-B) modeling both the workßow and data ßow ofthe application. Third, we apply a set ofattack patternstoautomatically generate test cases (Section II-C). Finally, weexecute them against the web application (Section II-D), andwe use anoracleto verify whether the logic of the applicationhas been violated (Section II-E).In the rest of the section we describe each phase in detailsusing E-commerce web applications as a running example.A.Model InferenceThe technique we present ispassiveandblack-box. We donot require any access to the application source code (both onthe client- and server-side), and we do not actively crawl theapplication pages nor generate any trafÞc to probe its internalstate. Instead, we take as input a list of HTTP conversations.These traces can be manually generated by the tester, orcollected by logging real user activity.For simplicity, we consider only traces that exercise aspeciÞc functionality of the web application. For example, ifthe web application is a shopping cart, we use traces in whichusers log in, add items into the cart, and check out to buy theproducts. Nothing prevents the tester from generating tracesthat also contain other functionalities, such as browsing theonline catalog or posting product reviews. However, focusingonly on one aspect of the business logic helps our system toÞnd the relevant operations with a minimum number of inputtraces.Web applications often involve multiple parties. For in-stance, E-commerce web applications typically involve theclient, the store, and the payment service. However, the com-munication between them is normally channeled through theclient and, therefore, we focus on this point to collect thetraces. In addition, it is useful to collect data from different de-ployments of the same web application, to allow our inferencemethod to identify parameter values hard-coded in a certaininstallation.The Þrst phase consists of building the model of theapplication, callednavigation graph. This is done in two steps:resource abstraction, and resource clustering.2
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Fig. 1: Architecture of our approach.1)Resource AbstractionInput traces are sequences of pairs of HTTP requests andresponses. The Þrst step of the inference phase consists ofcreating a synthesis of the resources. Our approach currentlysupports JSON data objects [17] and HTML pages. However,it can be easily extended to other types such as SOAPmessages [36].We callabstract HTML pagethe collection of (i) its URL,(ii) the POST data, (iii) the anchors and forms contained inthe HTML code and their DOM paths, (iv) the URL in themeta refresh tag, and, if any, (v) the HTTP redirection locationheader. We callabstract JSON objecta collection of (i) itsURL, (ii) the POST data, (iii) the pairs of value and path inthe object, and (iv) the HTML links if any HTML code iscontained. For example, Figure 2 shows the abstract resourceof the following JSON object:{ÔitemsÕ: {Ôitem1Õ: [ÔpriceÕ:19.9,ÔtaxÕ:1.6],Ôitem2Õ: [ ... ]}}From each abstract resource we extract a set of elementscorresponding to all possible parameters that appear in theURLs, in the POST data, and in all the links. Each elementis characterized by a name, a value, a path, and an inferredsyntactic type. Our approach supports the integer type, decimaltype, URL type, email address type, word type (alphabeti-cal strings e.g., ÒaddÓ, ÒremoveÓ, . . . ), string type, list type(comma-separated values), andunknowntype (i.e., everythingelse). The type is associated to each element by inspecting thevalues of the element. Obvious priority rules are applied inFig. 2: Resource abstraction and syntactic type inference of aJSON data objectcase of ambiguity Ð e.g., id=20 can be both a number and astring, but being the Þrst a subset of the second, it is consideredto be a number.2)Resource ClusteringModern web applications map application logic operationsto different resources. For instance, the operation of displayingthe shopping cart could involve an initial HTML page contain-ing the skeleton of the web page and then use a number ofasynchronous AJAX requests to populate the page with thelist of items, tax, available vouchers, and so on. We clusterthese resources in three phases. First, we relate asynchronousrequests to the resource that originated them, i.e., synchronousresource. Then we group together resources considering both3
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Otherwise, we try to remove the part of the navigation graphbetween two waypoints, reconstructing the propagation chainsby fetching the missing data values from another user session.Figure 7.d shows an example of this pattern. On the leftside we skip the waypointd, while on the right side wecut the subgraph betweenaandd. In this second case, ifthe URL of nodeddepends on a value that appears in thesegment betweenaandd, we prepare another user session byselecting an input trace and interrupting it atd. The generationof this part is similar to breaking propagation chains. The Þrstuser session is then#a, b, a, c!,c!!,d$. Afterwards, we preparethe second user session that skips the sequence betweenaandd. In this example, there are two possibilities: skipping#b, a, c!,c!!$or#c!,c!!$. Figure 7.e shows only the latter. Inthis case the test case is the concatenation of#a, b, a, c!,c!!,d$and#a, b, a, d, e, f, e$. For this case we also support the variantin which the Þrst user session is not interrupted at the noded.D.Test Case ExecutionThe test cases described in Section II-C are abstract andstill miss the details to be properly executed. For example,the values of some parameters cannot be determined fromthe model and need to be collected during the test caseexecution. In addition, it is important that after each test theapplication is reset to its initial state to avoid interferencesbetween consecutive executions. For example, a test may leavea number of items in the shopping cart, thus affecting followingexperiments. In general, it is often sufÞcient to delete thecookies and empty the shopping cart at the end of each test.The execution engine iterates over each node of the testcase, concretizes the POST/GET parameters, and submitsthe HTTP request. The responses are parsed according tothe propagation chains in order to extract server-generatedparameters to be used in latter requests. If the execution engineis not able to properly reconstruct a chain (e.g., because thepage that was supposed to generate its value returned an error)the execution engine aborts the execution and reports that thetest wasnot executed; it reportsexecutedotherwise.E.Test OracleThe approach we propose in this paper is completelyindependent from the business logic of the web application.Our technique can automatically identify behavioral patterns,and then generate test cases to break those patterns in a numberof different ways. The system can also determine if a giventest was executed correctly, but this is as far as it is possibleto go with an application-agnostic approach. For example,replacing the value of a security token in a payment workßowwould probably make the entire process fail. Unfortunately,without any knowledge about the underlying business logic,the test verdict could only say whether the pattern was appliedsuccessfully, but it can not draw any conclusion about thepossible implications. Therefore, if we want our tool to beable to report possible violations of the application logic, weneed to extract the sequence of events that occur during a testcase execution and compare them with thelogic propertythatwe want to violate.A simple way to express a logic property for shoppingcarts could be the following:if an order is approved for auser, then the user must have completed a payment for thecorresponding amount. In this formulation two events play acentral role: the fact that an order is placed, and the fact thata user has paid a certain amount. Another important aspect ofthis property is the time dependency between the two events.Since propositional logic can only express truth regardless ofthe time, in our approach, we model logic properties as LinearTemporal Logic (LTL) formulas [30, 23]. LTL adds temporalconnectives likeO(once in the past) to traditional logicaloperators like%(and), and=&(implies). This enables us toverify whether one event will eventually happen in the futureor it already happened in the past.For example, the above logic property can be written inLTL as follows:ordplaced%onStore(S)=&O(paid(U, I))(1)whereordplaced,onStore(S), andpaid(U, I)are respectivelythe eventsorder placed,operation performed on the storeS,anduserUpaid the price of itemI. Now, the problem ofidentifying a violation of the logic property is recast into theproblem of checking whether the LTL formula is satisÞed ornot by a given test case.In our approach, theTest Oracleis the component thatgiven an execution of a test case returnstrueif a certainpredeÞned logic property is violated, andfalseotherwise. Theoracle is composed of two parts: anevents extractorand anLTL formula checker. The extractor collects from the executedtest a partially ordered set of events (events can happen insequence or in parallel) grouped by user sessions. The secondpart veriÞes whether all sequences satisfy the provided LTLformula.It is important to note that both the events and the LTLformula depend on the type of applications under test and onthe type of vulnerabilities that we are interested to Þnd. Forexample, to Þnd authentication bypass vulnerabilities it wouldbe interesting to observe events related to the user login andto the access of private pages. However, since in this paper wefocus on the test of E-commerce applications, we are moreinterested in monitoring the money transfer and the value ofthe purchased items, as described in more details in the nextsection.III.EXPERIMENTSWe could use our tool to test online stores (e.g., Amazon).However, our tests require to attempt malformed operationsand to complete a large number of checkout processes. Thiswould be both unethical, since the application can malfunctionas a result of our tests, and very expensive, since it requiresto buy at least one product for each test case. Therefore,we opted to run our tests on seven well-known open sourceapplications available for ofßine testing, as reported in TableI. The table also shows an estimation of their popularity,measured with the search results obtained by performing a7
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Web App. No. of InstallationsOpenCart 9,710,000Magento 3,130,000PrestaShop 650,000CS-Cart 260,000TomatoCart 119,000osCommerce 80,500AbanteCart 21,200Total 13,970,700TABLE I: Popularity indexnumber ofgoogledorks[1]. Each Google query was builtby combining both the URL structure (e.g., the path of thecheckout endpoint) and some static HTML content extractedfrom the web pages (e.g., Òpowered by. . . Ó of the footer). Assuch, the numbers reported in the table are only a lower boundof the number of publicly-accessible installations available onthe Internet. This conservative measurement shows that theseseven applications are used by almost 14 million E-commerceinstallations. As a comparison, the two applications tested byWang et al. [34] returned less than 40,000 hits using similarGoogle dorks.A.General SetupWe installed two instances of each web application (here-inafter StoreAand StoreB). All installations except forAbanteCart and PrestaShop were then conÞgured to use boththe PayPal Express Checkout [3] and the PayPal PaymentsStandard [4] methods. In total we prepared 12 conÞgurations1.All applications were conÞgured insandboxmode. In thisconÞguration, each application performs transactions by usingthe PayPal sandbox payment gateway. These payments do notinvolve real money as they are performed between the sellerand buyer testing accounts.B.Testing OracleIn their experiments, Wang et al. [35] used the followingshopping cart property:ÒThe storeSchanges the status of an itemIto ÒpaidÓ withregard to a purchase being made by userUif and only if (i)SownsI; (ii) a payment is guaranteed to be transferred froman account ofUto that ofSin the CaaS; (iii) the paymentis for the purchase ofI, and is valid for only one piece ofI;(iv) the amount of this payment is equal to the price ofI.ÓHowever, this property is not entirely veriÞable in a black-box setting. For instance, it is not possible to test the truth ofthe predicate ÒSownsIÓ nor to check whether the due amounthas been transferred to the merchantÕs account. Therefore, wesimpliÞed the above invariant by removing the non-veriÞableclauses. The new property that can be used for automatedblack-box testing becomes:1When we did the experiments, AbanteCart and PrestaShop were providing,respectively, only PayPal Payments Standard and PayPal Express Checkout.When the storeSconÞrms the userUthat an order hasbeen placed, then in the pastUpaidSthe amount equal tothe price ofIandUagreed on purchasingIfromS.We modeled the logic property using the following eventsextracted during each test case execution:¥ordplacedwhen the shop conÞrms that the order hasbeen placed;¥onStore(S)when an operation has been performedon the storeS;¥paid(U, I)when the userUauthorizes the paymentgateway to pay the price ofI;¥toStore(S)when the payment is meant for the storeS;¥ack(I), when the user acknowledges to buyI.The logic property is then formulated as:ordplaced%onStore(S)=&O(paid(U, I)%toStore(S)%O(ack(U, I)%onStore(S)))(2)C.Input TracesTo generate the input traces we created two user accounts,U1andU2, each controlling a PayPal buyer testing account.For each web application we captured in total six HTTPconversations, three for each store: one withU1buying oneitem, one withU2buying another item, and one withU1buying two different items. All the input traces satisfy the logicproperty 2. These input traces were sufÞcient to stimulate themain shopping cart functionalities, but a better training couldbe used in the future to expose also more subtle features, orfor detecting different types of logic ßaws.D.Test Case GenerationTable II shows the test cases grouped by attack pattern.The test case generation produced about 3100 test cases,an average of 262 per application. Table II shows also thetest execution result. An execution failed when the test casebrought the application in a state in which it was impossibleto proceed (e.g., because of error pages in intermediate steps).This is a common result, since by deÞnition our tests stress theapplication to expose some unexpected behavior. The numberof test cases violating the LTL formula is reported in Table III.As mentioned before, there are events that are not visible tothe oracle. Therefore, a violation to the LTL formula does notalways correspond to a vulnerability. In fact, it is possible thatfurther checks performed in the back end of the applicationwould detect and block the attack. To distinguish logic vul-nerabilities from other bugs (e.g., erroneously reporting to theuser a failed transaction as successful) we manually inspectedthe balance sheets of the merchant, the list of orders, and theirstatus. Whenever the result was not conÞrmed by our manual8
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Test Case GenerationTest Case ExecutionWeb App.Time hh:ss(a) (b) (c) (d), (e)Time hh:ssExec. Not Exec.TotalAbanteCart Std'00:019 51 21 15204:5174 159233Magento Exp00:0210 82 5 24616:23240 103343Std00:0214 62 7 30314:50210 176386OpenCart Exp00:0110 77 3 8302:34140 33173Std00:0115 38 22 6002:0871 64135osCommerce Exp'00:014 13 6 14203:22117 48165Std00:018 63 10 14403:42128 97225PrestaShop Exp'00:0112 22 3 10002:4285 52137TomatoCart Exp00:029 68 10 21504:54238 64302Std00:0217 32 37 13804:36115 109224CS-Cart Exp00:058 24 6 56212:02347 253600Std00:0216 54 15 13705:29127 95222Total 132 586 145 22821892 12533145TABLE II: Statistics per application on the test case generation and test case execution phases. Columns (a), (b), (c), (d), and (e)are the attack patterns in Figure 7 while columns Exec. and Not Exec. refer to the two possible outcomes of the test executionengine.No.Caused byWeb App.of Viols.Bugs Vulns.AbanteCart Std1716 1Magento Exp6565 -Std126126 -OpenCart Exp5846 12Std3030 -osCommerce Exp4222 20Std3534 1PrestaShop Exp---TomatoCart Exp9065 25Std2424 -CS-Cart Exp313313 -Std109108 1Total 909849 60100%93.4% 6.6%TABLE III: Number of test cases violating Property 2 and theroot cause.inspection, we classiÞed it as a normal bug. The remainingcases correspond instead to anomalous behaviors associated toreal software vulnerabilities, as explained in the next Section. Itis important to note that over 28.9% of the test cases generatedby our approach brought the application in a state that violatedthe LTL formula, and 1 test out of 52 exposed a previously-unknown logic vulnerability.Test case generation does not require much resources, whilethe execution phase can be quite time consuming (16h forMagento). This is largely due to the lack of parallelization inour experiments, and to the fact that the PayPal sandbox ismuch slower than its live counterpart. The model inferenceÐ omitted from Figure II Ð required an average of 9m perapplication to build the navigation graphs that, in average,contained 34 nodes and 48 edges.IV.RESULTSTable III reports the total number of violations of thesecurity property 2. In other words, by tampering with eitherthe workßow or the data ßow according to our attack patterns,our system was able to bring the web application in a faultystate in 909 cases. All these cases corresponded to teststhat were executed until the Þnal page in which the storecongratulates the customer for the successful purchase (thatcaused the generation of the eventsordplaced%onStore(S))even though the paid amount was not correct. While theseviolations are all the consequences of bugs in the applicationcode, not all of them can be exploited by an attacker.This is an important point and a fundamental limitationof black-box approaches. Our tool can only observe theapplication state Òfrom the outsideÓ, and therefore it cannotdistinguish between a presentation bug (in which the informa-tion displayed on the web pages are wrong but the internal stateof the application is correct) and a more serious vulnerability(in which also the internal state is compromised).To distinguish between the two types of bugs, we manuallyinspected the state of the backend database: the result isthe distinction summarized in Table III between harmlesspresentation bugs (93.4%) and real vulnerabilities (6.6%).While these results indicate that thetrue positiverate ofour tool is 6.6%, also the remaining 93.4% of the violationscorrespond to real bugs in the application that need to be Þxedby the developers. Once all the presentation issues have beensolved, the alarms raised by our tool would correspond onlyto exploitable vulnerabilities.A.VulnerabilitiesTable III shows that 60 of our test cases (1.9% of the total)exposed a logic vulnerability in the target applications. Wediscovered the following ßaws:¥In osCommerce 2.3.1, CS-Cart 3.0.4, and Abante-Cart 1.0.4 with PayPal Payments Standard a malicious9
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Fig. 8: Shopping for free with osCommerce 2.3.1 and Aban-teCart 1.0.4customer can shop for free (exploitable)¥In OpenCart 1.5.3.1 and TomatoCart 1.1.7 with PayPalExpress Checkout a malicious customer can pay less(exploitable)¥In TomatoCart 1.1.7 with PayPal Express Checkout amalicious customer can shop for free (exploitable)¥OpenCart 1.5.3.1, TomatoCart 1.1.7, and osCom-merce 2.3.1 with PayPal Express Checkout a customercan pay an amount different from what she authorized(not exploitable)¥TomatoCart 1.1.7 with PayPal Express Checkout acustomer pays another customerÕs cart (not ex-ploitable)All the exploitable ßaws have been already responsiblydisclosed. When the developers did not answer within twoweeks of our notiÞcation, we reported the vulnerabilities alsoto the US Cert2. In the following we describe each class ofvulnerability we discovered in our experiments.1)osCommerce, CS-Cart, and AbanteCart with PayPal Pay-ments Standard - Shopping for FreeThese ßaws were discovered by tests that interrupted theserver-generated propagation chain transporting the PayPalaccount of the merchant. An example is shown in Figure 8.The left-hand side of the Figure shows the message sequencechart while the right-hand side shows events grouped by usersession. Each user session begins with aloginmessage. Theevents show how the violation was detected by the oracle. At2See http://www.kb.cert.org/vuls, IDs 459446, 207540, and 583564Fig. 9: Paying less with OpenCart 1.5.3.1 and Tomato-Cart 1.1.7the end of the execution,ordplaced%onStore(ÒStore BÓ)issatisÞed as all the events in it were observed. However, theleft-hand side of the Formula (1) is not satisÞed because noneof the events in it were observed.The manual inspection veriÞed that (i) no payment wasmade to the StoreB, (ii) the status of the order in the backofÞce of StoreBwas ÒcompletedÓ, and (iii) the invoice waspaid. It is straightforward to turn the above test into a realattack. Indeed, when redirected to PayPal, an attacker canreplace the seller PayPal account with another PayPal accountunder her control. In this case, the attacker can pay herself foran item she buys in an online shop.2)OpenCart and TomatoCart with PayPal Express Checkout -Pay LessIn OpenCart and TomatoCart with PayPal Express Check-out an attacker can pay less than the value of the items.The ßaw has been detected by using the waypoints detourpattern. The test case generator produced 11 test cases forOpenCart and 11 for TomatoCart in which the userU2skipsthe nodes of the redirection to PayPal for the payment andreconstructs the URL with values taken from the user sessionofU2. A representative test case is shown in Figure 9. Inthe second user sessionordplaced%onStore(ÒStore AÓ)issatisÞed. However, the other clauses of the formula are notsatisÞed because neither the user acknowledgment nor thepayment were observed.The manual inspection found two distinct orders in the listof orders, one forIand forI!. Both orders were in the stateÒpaidÓ and ready for shipping. However, the balance sheet ofthe merchant contains only the transaction forI, while nothingis recorded forI!.10
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!!!"#!"$"%%&'()*&"+,)+-./("%%&'()*&"#0.-)1.2%)2&3$"+)%&'1&4(.2)&567)1(80.-)1"/(,.2'9)&3"#*)1(&(.&"++$0.-)1:&!"#)2;<0.-)1:&!"#)2;<.2%)2&3$"+)%&'1&4(.2)&50.-)1:&!"#)2;<$.='1$.='14(.2)$!"%&'(!)*+",*-./0-12(3/0-12+$4,-1567%&25-./0-12(3/0-12+$4,6%85(!)*+",0-/0-12(3/0-12+$4,-1567%&25-./0-12(3/0-12+$4,
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!!"!"#!"$#%&'()%&'()"**+,&)-+$./).0'1&"**+,&)-+$%./).0'1&".."2+"-&$%2+,34$%2+567""..#2+"-&$%2+,34$%2+567"+"..#2+"-&"2+,34$2+567#+"1&/'(,8)+9"#-)3&+&'+"..#()&1(3+&'+567"567"'(*)(+9$".)*+,3+%&'()+:&'()*+,-(&./0&'-12/0&'-3"45)+6(1!783$%50&/0&'-12/0&'-3#45;4)3&<+,91!783$58&./0&'-12/0&'-3#45$'=,3$'=,3+,91!783$%58&./0&'-12/0&'-3"45
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Fig. 10: Shopping for free with TomatoCart 1.1.7This test can be turned into an attack by Þrst buying a cheapitem and intercepting the redirection URL from PayPal to thestore. Then the attacker can login again, add an expensive itemto the cart, and replay the URL captured before to completethe transaction. Even worse, we veriÞed that the attacker (orany other user) can reuse the sameTokenIDandPayerIDtocomplete an arbitrary number of additional fake transactions.This process is only bounded by the timeout set by PayPal onthe token.3)TomatoCart with PayPal Express Checkout - Shopping forFreeThis problem has been identiÞed by 11 different test casesgenerated with the waypoint detour pattern. A representativetest case is shown in Figure 10. Figure 10 shows that inthe second user sessionordplaced%onStore(ÒStore AÓ)issatisÞed. However, the other clauses of the formula are notsatisÞed because neither user acknowledgment nor the paymentwere observed.The manual inspection veriÞed that no payment forIandforI!were done. However, the list of orders contained theorder forI!in a ÒpaidÓ state and ready for shipping. Thistest case can be turned into an attack as shown before withthe difference that the attacker ends the Þrst user session afterreceivingTokenandPayerIDfrom PayPal.4)osCommerce, OpenCart and TomatoCart with PayPal Ex-press Checkout - Pay LessIn osCommerce the test was generated by the waypointsdetour pattern, while in OpenCart and TomatoCart tests weregenerated by breaking server-generated propagation chains.In osCommerce, the test is similar to the one shown in Fig-ure 10 while for OpenCart and TomatoCart, the tests are similarto the one in Figure 8. When PayPal Express Checkout isFig. 11: Session Þxation in TomatoCart 1.1.7selected, the store and PayPal are exchanging theTokenviaredirections. Here, the pattern interrupted the chain ofTokenwhen the user is redirected to PayPal for the payment. In bothcases the oracle veriÞed that the userU2had a conÞrmationand thatpaid(U2,I!)%toStore(A)is satisÞed. However, theoracle could not verifyO(ack(U2,I!)%onStore(A))becauseit observedO(ack(U2,I)%onStore(A)).A manual inspection conÞrmed that only the order forI!was in the list of the orders with status ÒpaidÓ, while the orderforIwas still Òpayment pendingÓ. However, in the balancesheet of the merchant, the payment forI!was done byU1instead ofU2. In this case,U1authorized PayPal to pay forIwhile her credit card was charged forI!.In order to turn this tests into a real attacks, the attackerneeds to intercept the redirection URL that is carried overSSL/TLS channels. In addition, it must block the user-victimfrom executing the redirection. This could require the attackerto either break the SSL/TLS encryption layer or to mounta SSL/TLS MITM (Man-In-The-Middle) attack. However, inboth cases the attacker will be able to capture also the paymentdata of the victim enabling her to shop for free in any case.5)TomatoCart with PayPal Express Checkout - Session Fixa-tionOur experiments discovered a session Þxation vulnerabilityin whichU2could impersonate another user. The test caseswere created by breaking the propagation chain of the param-etersidin two points. Figure 11 shows one of them. Theevents of Figure 11 did not satisfy the formula because thepaymentI!was of a different amount than the one the useracknowledged forI.The parametersidcarries the same value in the cookie andbreaking it causes asession Þxationin which, in our case,U211
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!!!"#!"$"%%&'()*&"#$+'%,-),./0(#$+'%"%%&'()*&"%1&+'%2/3%)3&4$",)%&'5&6(/3)&789)5(+$/:'5$/:'56(/3)&!",-),./0(#$+'%"0(-/3';)&4"#*)5(&(/&",,&4"#*)5(&/.4"#4"#4"#*)5(&/.'()*!+#$",#-./0-12*3/0-12$&4,5'67*!+#$"%,0-/0-12*3/0-12$&4,-1758'(27-./0-12*3/0-12$&4,
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!!!"#!"$"%%&'()*&"+,)+-./("%%&'()*&"#0.-)1.2%)2&3$"+)%&'1&4(.2)&567)1(80.-)1"/(,.2'9)&3"#*)1(&(.&"++$0.-)1:&!"#)2;<0.-)1:&!"#)2;<$.='1$.='14(.2)$!"%&'(!)*+",*-./0-12(3/0-12+$4,5%67(!)*+",0-/0-12(3/0-12+$4,-1758%&27-./0-12(3/0-12+$4,
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results logged in asU1. From that point on,U2can access thedata ofU1. As a consequence,U2(now logged asU1) paysthe cart ofU1. However, we could not Þnd any exploitationof this ßaw. Supposing that the victim (i.e.U2) ÒclicksÓ on anURL crafted by the attacker (U1), then the victim could noticethe fraud in three moments (i) when checking the summaryof order, (ii) when providing the shipping address (it showsthe attackerÕs one), and (iii) during the payment because theamount is different.V.LIMITATIONSOur approach uses attack patterns that tamper with theobserved data ßow and workßow. However, it does not testfor other types of logic vulnerabilities such as unauthorizedaccess to resources. Moreover, we did not consider cases inwhich the attacker can also play the role of a malicious store,or the cases in which the attacker can intercept and tamperwith the messages between the application and the paymentservice. We believe that our techniques could also be effectiveat detecting other kinds of logic ßaws, even though we have notexperimentally tested this hypothesis. This could be achievedby adding input traces of privileged user (e.g., admin), byadding other behavioral patterns, or by adding new attackpatterns.Second, the test generation favors efÞciency over coverage.This means that only a few values are used for each testcategory, to maximize the possibility to Þnd bugs in a limitedamount of time. A more thorough exploration of the attackspace could be used to discover more vulnerabilities, howeverthis could require a considerable amount of execution time.The focus of this paper is to show how an automated approachcan be used to Þnd logic vulnerabilities in many real-wordapplications, and not to analyze in depth a single application(a scenario that would also require more input traces to betterexplore the applicationÕs logic).Finally, we modeled logic properties in LTL. The useof LTL enables us to verify events with time dependency.However, LTL do not support algebra whose terms appear atdifferent moment of the execution. For example, our oraclescannot verify whether the payment is the sum of the itemsthe user added into the cart at some point in the past. Thereare works that extend LTL with constraints on integer num-bers [10], and they could be used by our oracle for checkingmore Þne-grained properties.VI.RELATEDWORKA large number of solutions have been proposed to detectvulnerabilities in web applications. However, most of theprevious work focus on the automated detection of well-knownclasses of vulnerabilities related to insufÞcient input validation,such as Cross-Site Scripting (XSS) [26], Cross-Site RequestForgery (CSRF) [2, 27] and SQL injection [22]. Since ourgoal is to Þnd logic ßaws, we will not present these solutionsin this section.a)Detection of Logic VulnerabilitiesWhen the source code of the application is available, toolssuch as MiMoSA [9], Waler [21], and Swaddler [16] can beused to discover logic vulnerabilities. MiMoSA and Walerextract a model from the source code and then use a modelchecker to detect a violation of invariants. Swaddler [16]detects attacks when the software is at the deployment phaseof its life-cycle. It Þrst learns the normal behavior of the ap-plication and then monitors state variables at runtime lookingfor deviations from the normal behavior.When the source code is not available, the problem ofextracting a model becomes more difÞcult. Doup«e et al. [19]and Li and Xue [28] proposed two black-box testing tools. Theformer presents a state-aware input fuzzer to detect XSS andSQLi vulnerabilities. The tool infers a model that is used as anoracle for choosing the next URL to crawl. Both our approachand this technique infer models to improve the automaticdetection of vulnerabilities. However, we use a passive learningtechnique tailored to generate test cases to detect logic ßaws,and not an active scanning to drive an input fuzzer. Thesecond work presents BLOCK, a tool that learns model andinvariants by observing HTTP conversations and then detectsauthentication bypass attacks. As opposed to BLOCK ourapproach does not aim at intercepting attacks, but at generatingsecurity tests for detecting ßaws. Both works could not be usedto Þnd this class of vulnerabilities. The former work proposesa stateful crawler with an input fuzzer that does not attempt toviolate the logic of the application. The latter focuses on thedetection of authentication bypass attacks by inferring sessionvariable invariants.An approach similar to BLOCK is InteGuard [37]. Inte-Guard aims at protecting multi-party web applications fromexploitation of vulnerabilities in the API integration. Inte-Guard focuses mainly on the browser-relayed messages inwhich data values are exchanged between the parties throughthe web browser. In particular, InteGuard uses a passivemodel inference technique based on data-ßow analysis anddifferential analysis to extract inter-services dataßow-relatedinvariants. The former is used to extract the ßows of datavalues while the latter is used to detect properties of dataßows such as transaction-speciÞc or implementation-speciÞcvalues. Our approach uses similar techniques to extract thesetype of invariants. However, in addition to that, it extractsalso invariants of the observable workßow of the application,and takes into account both intra-service invariants, e.g., idem-potent operations, and inter-service invariants, e.g., multi-stepoperations.Given the limited success of automated black-box tech-niques, manual methodologies have been recently proposed.Our work is mainly inspired by Wang et al. [34, 35], whopresented an analysis of Cashier as a Service (CaaS) basedweb stores, and a large-scale analysis of web Single Sign-Onprotocols. The former work describes a black-box methodol-ogy that given a number of HTTP conversations, labels APIarguments and shows with which ones an attacker could play inthe attempt of violating security invariants. The latter reÞnesthe previous one by (i) considering the role played by theattacker during the protocol execution and (ii) adding semanticand syntactic labels to protocol parameters. Both techniques12
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had a large impact due to the severe vulnerabilities the authorswere able to Þnd in real-world applications. However, thesepapers propose techniques and guidelines that need to bemanually applied by a security expert. Our work extends theirtechnique in four ways. First, it infers a model from setof HTTP conversations. Second, it generalizes the notion ofpropagation chain of a single trace into propagation chain of anapplication model. Third, it infers observable characteristics ofthe workßow of the business function. Finally, it automaticallygenerates and executes test cases using a number of attackpatterns.AUTHSCAN [8] is an approach similar to our work. Itinfers a model from implementations combining white-box andblack-box techniques. AUTHSCAN focuses on the detectionof ßaws speciÞc to authentication protocols (See Lowe etal.[29] for a survey of authentication property) and it requiresa list of application-speciÞc JavaScript function signatures inorder to infer an accurate model of the protocol participants.On the contrary, our approach focuses on business-relatedweb application properties and uses an application-independentmodel inference technique.b)Model InferenceThere is a large body of works addressing the problemof inferring a model for testing purposes. Model inference isdivided in two categories: active learning and passive learning.Active learning techniques interact with the application underinference in order to explore its behavior whereas passivelearning techniques build a model from a set of observations.Hossen et al. [25] proposed to apply the active learningalgorithm L* [5] to infer a deterministic Þnite automatonand reÞning it with testing. Dury et al. [20] described anapproach based on passive learning of web-based businessapplications. They used Parameterized Finite Automaton (PFA)that enriches the classic notion of Þnite automaton [24] withguards on transitions and parameters on states. PFA modelscontrol ßow and data ßow of an application. Guards areinferred using data mining algorithms like C4.5 [31]. Modelsare then translated into the Promela language and fed to themodel checker SPIN [23] for verifying application-dependentproperties. However, in the Þrst approach the authors proposeda direction and say little on the type of ßaws they aimat detecting, while in the second the authors focus on theinference part and do not cover the actual testing.c)Model-Based Security TestingNew ideas have been proposed in order to use models forthe (semi-)automatic security testing of web applications whenmodels are available. For example, Armando et al. [7] proposedto detect logic ßaws and testing web-based security proto-cols. The approach consists of using the SAT-based ModelChecker [6] to validate a formal speciÞcation against securitydesiderata. If a violation occurs, it is executed against a realimplementation. B¬uchler et al. [13] proposed an approach thatassumes (i) a model is given (ii) and the model is secure. Thenthey propose to mutate the model by injecting vulnerabilitiesand to use a model checker for detecting violations. If aproblem is found, then they use the counterexample returnedby the model checker as an abstract test case for testingimplementations. Bodei et al. [11] proposed to model Service-Oriented applications in CaSPiS (Calculus of Services withPipelines and Sessions), a process calculus with the notion ofsession and pipelines [12], to perform a control ßow analysisfor detecting misuse of the application. The authors tested theirtechnique on a known vulnerable version of the CyberOfÞceshopping cart detecting the price-modiÞcation attack. However,for all these works still remains the problem that a model ofthe application is often not available in practice.VII.CONCLUSIONSIn this paper we presented a new technique for the black-box detection of logic ßaws in web applications. Our ap-proach uses a passive model inference technique that buildsa navigation graph from a set of network traces. We thenapply a number of heuristics to extract behavioral patterns thatare likely related to the underlying application logic. Thesebehaviors, together with a number of attack patterns, are usedfor generating test cases.We developed a prototype tool and tested seven E-commerce applications. The prototype generated and executedmore than 3100 test cases, 900 of which violated the expectedbehavior of the application. As a result, our tool detected tenpreviously-unknown logic vulnerabilities in the applicationsunder test. Five of them allow an attacker to pay less or evenshop for free.ACKNOWLEDGMENTThis work has been partially supported by the EuropeanUnion Seventh Framework Programme under grant agreementno. 257007 (project SysSec) and no. 257876 (project SPaCIoSSecure Provision and Consumption in the Internet of Services).REFERENCES[1]ÒThe google hacking database at hacking for charity.Ó[Online]. Available: http://www.hackersforcharity.org/ghdb/[2]ÒRequestrodeo: Client side protection against sessionriding,Ó inthe OWASP Europe 2006 Conference, ReportCW448, Departement Computerwetenschappen, KU Leu-ven, May 2006, 2006.[3]ÒPaypal express checkout integrationguide,Ó August 2012. [Online]. Available:https://cms.paypal.com/cmscontent/US/enUS/Þles/developer/PPExpressCheckoutIntegrationGuide.pdf[4]ÒPaypal payments standard integration guide,ÓJune 2012. [Online]. Available: https://cms.paypal.com/cmscontent/US/enUS/Þles/developer/PPWebsitePaymentsStandardIntegrationGuide.pdf[5]D. Angluin, ÒLearning regular sets from queries andcounterexamples,ÓInf. Comput., vol. 75, no. 2, Nov. 1987.13
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[6]A. Armando, R. Carbone, and L. Compagna, ÒLtl modelchecking for security protocols,Ó inComputer SecurityFoundations Symposium, 2007. CSF Õ07. 20th IEEE, July2007, pp. 385Ð396.[7]A. Armando, G. Pellegrino, R. Carbone, A. Merlo, andD. Balzarotti, ÒFrom model-checking to automated test-ing of security protocols: Bridging the gap,Ó inTAP, ser.LNCS, A. D. Brucker and J. Julliand, Eds., vol. 7305.Springer, 2012.[8]G. Bai, J. Lei, G. Meng, S. S. Venkatraman, P. Saxena,J. Sun, Y. Liu, and J. S. Dong, ÒAuthscan: Automatic ex-traction of web authentication protocols from implemen-tations,Ó in20th Annual Network and Distributed SystemSecurity Symposium, NDSS 2013, San Diego, California,USA, February 24-27, 2013, San Diego, California, USA,February 24-27, 2013.[9]D. Balzarotti, M. Cova, V. V. Felmetsger, and G. Vigna,ÒMulti-module vulnerability analysis of web-based appli-cations,Ó inProceedings of the 14th ACM conference onComputer and communications security, ser. CCS Õ07.New York, NY, USA: ACM, 2007.[10]M. M. Bersani, L. Cavallaro, A. Frigeri, M. Pradella,and M. Rossi, ÒSmt-based veriÞcation of ltl speciÞ-cations with integer constraints and its application toruntime checking of service substitutability,ÓCoRR, vol.abs/1004.2873, 2010.[11]C. Bodei, L. Brodo, and R. Bruni, ÒStatic detection oflogic ßaws in service-oriented applications,Ó inARSPA-WITS, ser. LNCS, P. Degano and L. Vigan`o, Eds., vol.5511. Springer, 2009.[12]M. Boreale, R. Bruni, R. De Nicola, and M. Loreti, ÒSes-sions and pipelines for structured service programming,ÓinFMOODS, ser. LNCS, G. Barthe and F. S. de Boer,Eds., vol. 5051. Springer, 2008.[13]M. B¬uchler, J. Oudinet, and A. Pretschner, ÒSemi-automatic security testing of web applications from asecure model,Ó inSERE. IEEE, 2012.[14]J. Caballero, P. Poosankam, C. Kreibich, and D. Song,ÒDispatcher: Enabling Active Botnet InÞltration usingAutomatic Protocol Reverse-Engineering,Ó inProceed-ings of the 16th ACM Conference on Computer andCommunication Security, Chicago, IL, November 2009.[15]P. M. Comparetti, G. Wondracek, C. Kruegel, andE. Kirda, ÒProspex: Protocol speciÞcation extraction,ÓinProceedings of the 2009 30th IEEE Symposium onSecurity and Privacy, ser. SP Õ09. Washington, DC,USA: IEEE Computer Society, 2009, pp. 110Ð125.[Online]. Available: http://dx.doi.org/10.1109/SP.2009.14[16]M. Cova, D. Balzarotti, V. Felmetsger, and G. Vigna,ÒSwaddler: An approach for the anomaly-based detectionof state violations in web applications,Ó inRAID, ser.LNCS, C. Kr¬ugel, R. Lippmann, and A. Clark, Eds., vol.4637. Springer, 2007.[17]D. Crockford, ÒRFC4627: The application/json mediatype for javascript object notation (json),Ó July 2006.[Online]. Available: http://tools.ietf.org/html/rfc4627[18]A. Doup«e, B. Boe, C. Kruegel, and G. Vigna, ÒFear theear: discovering and mitigating execution after redirectvulnerabilities,Ó inProceedings of the 18th ACM con-ference on Computer and communications security, ser.CCS Õ11. New York, NY, USA: ACM, 2011.[19]A. Doup«e, L. Cavedon, C. Kruegel, and G. Vigna, ÒEn-emy of the State: A State-Aware Black-Box VulnerabilityScanner,Ó inProceedings of the 2012 USENIX SecuritySymposium (USENIX 2012), Bellevue, WA, August 2012.[20]A. Dury, H. H. Hallal, and A. Petrenko, ÒInferring be-havioural models from traces of business applications,Ó inProceedings of the 2009 IEEE International Conferenceon Web Services, ser. ICWS Õ09. Washington, DC, USA:IEEE Computer Society, 2009.[21]V. Felmetsger, L. Cavedon, C. Kruegel, and G. Vi-gna, ÒToward automated detection of logic vulnerabil-ities in web applications,Ó inProceedings of the 19thUSENIX conference on Security, ser. USENIX Secu-rityÕ10. Berkeley, CA, USA: USENIX Association,2010.[22]W. G. Halfond, J. Viegas, and A. Orso, ÒA ClassiÞcationof SQL-Injection Attacks and Countermeasures,Ó inPro-ceedings of the IEEE International Symposium on SecureSoftware Engineering, Arlington, VA, USA, March 2006.[23]G. J. Holzmann,The SPIN Model Checker - primer andreference manual. Addison-Wesley, 2004.[24]J. E. Hopcroft, R. Motwani, and J. D. Ullman,Introduc-tion to Automata Theory, Languages, and Computation(3rd Edition). Boston, MA, USA: Addison-WesleyLongman Publishing Co., Inc., 2006.[25]K. Hossen, R. Groz, and J. Richier, ÒSecurity vulnera-bilities detection using model inference for applicationsand security protocols,Ó inSoftware Testing, VeriÞcationand Validation Workshops (ICSTW), 2011 IEEE FourthInternational Conference on, march 2011.[26]M. Johns, ÒCode injection vulnerabilities in web applica-tions: ExempliÞed at cross-site scripting,Ó Ph.D. disser-tation, 2011.[27]N. Jovanovic, E. Kirda, and C. Kruegel, ÒPreventing crosssite request forgery attacks,Ó inSecureComm. IEEE,2006.[28]X. Li and Y. Xue, ÒBlock: a black-box approach fordetection of state violation attacks towards web appli-cations,Ó inProceedings of the 27th Annual ComputerSecurity Applications Conference, ser. ACSAC Õ11. NewYork, NY, USA: ACM, 2011.[29]G. Lowe, ÒA hierarchy of authentication speciÞcations,ÓinComputer Security Foundations Workshop, 1997. Pro-ceedings., 10th, 1997, pp. 31Ð43.[30]A. Pnueli, ÒThe temporal logic of programs,Ó inFOCS.IEEE Computer Society, 1977.[31]J. R. Quinlan,C4.5: programs for machine learning. San14
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Francisco, CA, USA: Morgan Kaufmann Publishers Inc.,1993.[32]The jQuery Foundation, ÒjQuery,Ó January 2013.[Online]. Available: http://jquery.com/[33]The OWASP Foundation, ÒOWASP Testing Guide,ÓDecember 2008. [Online]. Available: https://www.owasp.org/index.php/OWASPTestingProject[34]R. Wang, S. Chen, and X. Wang, ÒSigning me ontoyour accounts through facebook and google: a trafÞc-guided security study of commercially deployed single-sign-on web services.Ó inProceedings of the 2012 IEEESymposium on Security and Privacy. IEEE ComputerSociety, 2012.[35]R. Wang, S. Chen, X. Wang, and S. Qadeer, ÒHow toshop for free online Ð security analysis of cashier-as-a-service based web stores,Ó inProceedings of the 2011IEEE Symposium on Security and Privacy, ser. SP Õ11.Washington, DC, USA: IEEE Computer Society, 2011.[36]World Wide Web Consortium, ÒSimple Object AccessProtocol (SOAP) 1.2,Ó April 2007. [Online]. Available:http://www.w3.org/TR/soap/[37]L. Xing, Y. Chen, X. Wang, and S. Chen, ÒInteguard:Toward automatic protection of third-party web serviceintegrations,Ó in20th Annual Network and DistributedSystem Security Symposium, NDSS 2013, San Diego,California, USA, February 24-27, 2013.15
--- page 29 ---
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