---
type: Article
title: Detecting Logic Vulnerabilities in E-Commerce Applications
description: A static analysis that combines symbolic execution with taint tracking to find logic flaws in PHP e-commerce checkout code, using the invariant that a secure checkout preserves the integrity and authenticity of order ID, order total, merchant ID and currency. Violations let a shopper pay the wrong amount, pay a different merchant, or take goods without paying.
resource: "https://www.ndss-symposium.org/ndss2014/ndss-2014-programme/detecting-logic-vulnerabilities-e-commerce-applications/"
tags: [article, webseclist-reference, static-analysis, php, auth-bypass, detection, tooling, case-study, owasp-a01-2021, owasp-a09-2021]
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resource: "https://www.ndss-symposium.org/ndss2014/ndss-2014-programme/detecting-logic-vulnerabilities-e-commerce-applications/"
title: Detecting Logic Vulnerabilities in E-Commerce Applications
author: Fangqi Sun, Liang Xu, Zhendong Su
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- Fangqi Sun
- Liang Xu
- Zhendong Su
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# Detecting Logic Vulnerabilities in E-Commerce Applications
**Detecting Logic Vulnerabilities in E-Commerce Applications** - Fangqi Sun, Liang Xu, Zhendong Su, Publisher not stated.
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Detecting Logic Vulnerabilities in E-Commerce Applications
Fangqi Sun Liang Xu Zhendong Su
University of California, Davis
{fqsun, leoxu, su}@ucdavis.edu
Abstract—E-commerce has become a thriving business model.
1. Order Initialization
With easy access to various tools and third-party cashiers, it is
straightforward to create and launch e-commerce web applica- 3. Order confirmation
tions. However, it remains difficult to create secure ones. While
third-party cashiers help bridge the gap of trustiness between 2. Payment of order total in currency
merchants and customers, the involvement of cashiers as a new for order ID to merchant ID
party complicates logic flows of checkout processes. Even a small
loophole in a checkout process may lead to financial loss of Figure 1: Logic Flows in E-Commerce Web Applications.
merchants, thus logic vulnerabilities pose serious threats to the
security of e-commerce applications. Performing manual code
reviews is challenging because of the diversity of logic flows
and the sophistication of checkout processes. Consequently, it
is important to develop automated detection techniques. to financial loss and merchant embarrassment, the impact of
logic vulnerabilities in e-commerce applications is often severe.
This paper proposes the first static detection of logic vulner-
abilities in e-commerce web applications. The main difficulty Business or application logic refers to application-specific
of automated detection is the lack of a general and precise functionality and behavior. Besides general functionality (such
notion of correct payment logic. Our key insight is that secure as user authentication), each application has its unique handling
checkout processes share a common invariant: A checkout process of user inputs, user actions and communications with third-
is secure when it guarantees the integrity and authenticity of party components. Although logic vulnerability is not the
critical payment status (order ID, order total, merchant ID
most common type of web vulnerabilities, it often has serious
and currency). Our approach combines symbolic execution and
taint analysis to detect violations of the invariant by tracking
impact and is easily exploitable. A logic vulnerability typically
tainted payment status and analyzing critical logic flows among exists when an attacker abuses legitimate application-specific
merchants, cashiers and users. We have implemented a symbolic functionality against developers’ intentions [10]. A report by
execution framework for PHP. In our evaluation of 22 unique WhiteHat Security lists seven examples of logic flaws [16].
payment modules, our tool detected 12 logic vulnerabilities, 11 When building an application, developers often have a clear
of which are new. We have also performed successful proof-of- picture of what the ideal application should be in their
concept experiments on live websites to confirm our findings. minds. Unfortunately, in practice, the implemented application
often does more than what is intended. Put it another way,
I. I NTRODUCTION unexpected user inputs and logic flows can allow attackers to
abuse insufficiently guarded application-specific functionality
E-commerce web applications, a special type of web in dangerous ways. The uniqueness and complexity of logic
applications designed for online shopping, play an important flows complicate the establishment of a general line of defense
role in the modern world. The U.S. Census Bureau of the against application-specific attacks.
Department of Commerce estimated that U.S. retail e-commerce
sales for the second quarter of 2013 reached $64.8 billion, an Logic vulnerabilities in e-commerce applications, being
18.4% increase from the previous year [28]. The prevalence of a subset of general logic vulnerabilities, allow attackers to
Internet and the rise of smart mobile devices contribute to the purchase products or services with incorrect or no payment at
rapid growth of e-commerce web applications. Unfortunately, the expenses of merchants. Developers often make assumptions
the complexity of e-commerce applications and the diversity of about what user inputs are and how users navigate web
third-party cashier APIs make it difficult to implement perfectly pages during checkout. However, when such assumptions do
secure checkout processes. Since logic attacks are tied directly not hold and developers fail to implement proper security
checks, attackers can exploit logic vulnerabilities in e-commerce
applications for financial gains. CVE-2009-2039 [9] describes
Permission to freely reproduce all or part of this paper for noncommercial our motivating example where Luottokunta (version 1.2), a
purposes is granted provided that copies bear this notice and the full citation
on the first page. Reproduction for commercial purposes is strictly prohibited payment module in the osCommerce software [1], has a logic
withoutthepriorwrittenconsentoftheInternetSociety,thefirst-namedauthor vulnerability that allowed attackers to tamper with order ID,
(for reproduction of an entire paper only), and the author’s employer if the order total and merchant ID. The latest version of Luottokunta
paper was prepared within the scope of employment. (version 1.3) was released to patch this vulnerability by adding
NDSS ’14, 23-26 February 2014, San Diego, CA, USA
Copyright 2014 Internet Society, ISBN 1-891562-35-5
logic checks on some components of payment status. However,
http://dx.doi.org/OETT upon close examination, we were surprised to discover that it
amounts. These experiments clearly demonstrate that insecure
uses of third-party cashiers, such as the heavily vetted cashier
PayPal, may give merchants a false sense of protection.
The detection of logic vulnerabilities in e-commerce appli-
cations is challenging for both manual and automated analyses
since any weak link in a checkout chain can result in a
logic vulnerability. On one hand, manual code review is time-
consuming and error-prone. Security analysts often spend much
time understanding different logic flows in an e-commerce
Figure 2: Received Products from Vulnerable Websites. application before examining security checks of payment status.
In contrast, payment module developers are familiar with
logic flows but not various attack vectors. In either case, a
thorough manual code review of all possible logic flows in
is still vulnerable. The added check on order ID is insufficient, a checkout process is a nontrivial task. On the other hand,
thus attackers can pay for one order and bypass payments for automatic code scanners cannot detect logic vulnerabilities
future orders. This is one of the new vulnerabilities that we without the knowledge of application-specific business context.
detected. E-commerce applications have various application-specific logic
flows and each payment method has its unique APIs and security
The use of third-party cashiers in e-commerce applications
checks. Consequently, it is challenging to create general rules
introduces new security concerns even if the cashiers themselves
to automate the detection process.
are secure. For flexibility, a modern web application often
presents several payment options during checkout by using Researchers have proposed various techniques to detect
one payment module for each third-party cashier. However, the different logic vulnerabilities, including abnormal logic behav-
integration of cashiers also increases the complexity of logic ior [15], multi-module vulnerabilities [3] and single sign-on
flows in checkout processes. Figure 1 illustrates three critical vulnerabilities [31, 33]. Each technique targets a particular
steps in a typical checkout process that involves a merchant, domain of logic vulnerabilities and checks web applications
a cashier and a user: 1) order initiation on the merchant’s against specifications in the given domain. Wang et al. [30, 33]
server, 2) payment transaction on the cashier’s server, and 3) are the first to perform security analysis on Cashier-as-a-
order confirmation on the merchant’s server. In the first step, Service based e-commerce applications. They found several
the merchant initiates the basic payment information of an serious logic vulnerabilities in a few popular e-commerce
order. From then on, both the merchant and the cashier track applications via manual code reviews [30] and proposed a proxy-
the payment status of the order. Ideally, the merchant should based approach to dynamically secure third-party web service
either explicitly check every component of important payment integrations which include the integration of cashiers [33].
status or directly communicate with the cashier. In practice,
In this paper, we propose the first static detection of logic
miscommunications between the merchant and the cashier may
vulnerabilities in e-commerce applications. Our key observation
harm the integrity or authenticity of payment status. Insufficient
is that an invariant must be verified to secure a payment: A
or missing logic checks on payment status can allow an attacker
merchant M should accept an order O from a user if and
to skip the second step or carry it out incorrectly. In a successful
only if the user has actually made a payment to the cashier
attack, the merchant is led to believe mistakenly that the order
in the correct amount and currency for that specific order O
has been paid in full, while the cashier actually receives no
associated with merchant M . Based on this observation, we
payment or partial payment.
designed a symbolic execution framework that explores critical
With the goal of confirming the real dangers that logic control flows exhaustively, tracking taint annotations for the
vulnerabilities in e-commerce applications pose, we designed critical components of payment status (order ID, order total,
responsible proof-of-concept experiments following the exam- merchant ID and currency) and exposed signed tokens. Our
ple set by Wang et al. [30]. Each experiment was performed main contributions are:
on a live website that used a vulnerable payment module. • We provide an application-independent invariant for
Specifically, we received three products (Figure 2) from three detecting logic vulnerabilities in e-commerce web ap-
websites which integrate vulnerable payment modules. First, plications and discover a new attack vector: tampering
for payment module RBS WorldPay, we received a Ubuntu with currency.
notebook from the Ubuntu online shop by Canonical Ltd. We
paid less by changing the currency from British pounds to • We propose the first static analysis to detect logic
U.S. dollars. Second, for payment module Authorize.net Credit vulnerabilities in e-commerce applications based on
Card SIM, we received a diaper game package from a baby symbolic execution and taint tracking of payment
products online shop. We paid nothing by replaying tokens status.
from a previous order. Third, for payment module PayPal
• We implement a scalable symbolic execution frame-
Standard, we received three chocolate pieces from a California
work for PHP web applications. Our analyzer system-
chocolate online shop. We paid nothing to the merchant by
atically explores control flows to examine logic flows
changing the merchant ID from the chocolate shop owner’s ID
in checkout processes.
to our ID. After having received the products, we immediately
compensated the three merchants for the respective correct full • We evaluate our tool on 22 unique real-world payment
2
modules from various cashiers and detect logic vul- complete the payment transaction with the user (R2) and
nerabilities in 12 out of the 22 payment modules. We redirect the user back to the merchant (R3). To continue
also perform responsible proof-of-concept experiments exploring logic flows, our analyzer symbolically executes page
on live websites. Of the 12 detected vulnerabilities, 11 checkout_process.php which is a part of the return
are new. The evaluation results demonstrate that our URL. A thorough examination requires the modeling of all
approach is effective and scalable. possible responses from the cashier. Therefore, we use the
symbolic top value (>), i.e., the most conservative value
The rest of the paper is organized as follows. We first give an that denotes any possible value, for the request variables of
example to illustrate the main steps of our approach (Section II). R3. Our analyzer first propagates the end execution states
Section III describes our detailed algorithm and approach. from the previous page checkout_confirmation.php
Section IV presents the implementation of the automated to the current page checkout_process.php, and then
analyzer we developed, and Section V shows the vulnerability symbolically executes the IR of the current page. The execution
report, the details of our experiments on live websites and the eventually reaches function before_process() which has
performance of our tool on real-world e-commerce payment the following checks on payment status:
modules. Finally, we survey related work (Section VI) and
conclude (Section VII). function before_process() {
if (!isset($_GET[’orderID’])) {
tep_redirect(FILE_PAYMENT);
II. I LLUSTRATIVE E XAMPLE } else {
$orderID = $_GET[’orderID’];
This section uses payment module Luottokunta (version 1.3) }
to illustrate the major steps of our approach. This module, which
patched the vulnerability described in CVE-2009-2039 [9], is $price = $_SESSION[’order’]->info[’total’];
$tarkiste = SECRET_KEY
still vulnerable because of an insufficient check on untrusted . $price . $orderID . MERCHANT_ID;
order ID. During checkout, a user sends out the following four $mac = strtoupper(md5($tarkiste));
critical HTTP requests, the last two of which are redirections
if (($_POST[’LKMAC’] != $mac)
from HTTP responses with a status code of 302: && ($_GET[’LKMAC’] != $mac)) {
tep_redirect(FILE_PAYMENT);
R1. User > Merchant(checkout_confirmation.php) }
R2. User > Cashier(https://dmp2.luottokunta.fi) }
R3. User > Merchant(checkout_process.php), 302
R4. User > Merchant(checkout_success.php), 302
Because request variable $_GET[’orderId’] has a
symbolic top value, both branches of the first if statement
With this payment module, a merchant can integrate are feasible. For the true branch, the user is redirected to
the service of third-party cashier Luottokunta. Of the four merchant page FILE_PAYMENT. This redirection forms a
requests, the second one is sent to the cashier and the rest backward flow, which does not contribute to the detection
are sent to the merchant. The first request (R1) initializes of logic vulnerabilities. Therefore, this backward logic flow
the checkout process for an order when the user navigates to is automatically discarded. For the false branch, an MD5
page checkout_confirmation.php. The second request value is calculated and stored in variable $mac. Note that
(R2) lets the user pass on the order information generated by the value of $orderID used in the calculation comes from
the merchant to the cashier. After the user has completed an untrusted request variable $_GET[’orderId’] which is
the payment transaction on the cashier’s server, the cashier under attackers’ control.
sends the user a response that redirects the user to page
checkout_process.php (R3) on the merchant’s server Our taint analysis tracks the components of critical payment
to process the order. If the order is accepted, the merchant status across logic flows in the checkout process. Initially, order
redirects the user to page checkout_success.php (R4). ID, order total, merchant ID, currency and secret are all tainted.
Secret refers to an unpredictable value that only the merchant
Our symbolic execution starts from the first merchant page and the cashier know. Therefore, the cashier can use it to
checkout_confirmation.php in the checkout process. sign messages. For taint manipulation, we have a set of rules.
To model the first request (R1), it symbolically executes One rule removes a taint annotation when a conditional check
the intermediate representation (IR) of this page and si- verifies an untrusted value against a trusted component. For the
multaneously parses its symbolic HTML output in search last conditional in function before_process(), we have
of critical HTML form elements. The analysis eventually the following symbolic constraints for the false branch:
finds an HTTP form that serves as a communication channel
between the merchant and the cashier. Its elements record the [ or
order information and its action URL points to the cashier’s ($_POST[’LKMAC’] = strtoupper(md5(SECRET_KEY
URL (https://dmp2.luottokunta.fi). This form also . $_SESSION[’order’]->info[’total’]
. $_GET[’orderID’] . MERCHANT_ID)));
contains a return URL (checkout_process.php), which ($_GET[’LKMAC’] = strtoupper(md5(SECRET_KEY
will be used by the cashier to redirect the user back to . $_SESSION[’order’]->info[’total’]
the merchant’s server once a payment transaction has been . $_GET[’orderID’] . MERCHANT_ID)));
]
completed.
Since our analysis treats cashiers as black boxes that Among the symbolic values in the above constraints,
work correctly, we assume that the cashier would properly $_SESSION[’order’]->info[’total’] is a trusted
3
session value, while MERCHANT_ID and SECRET_KEY are the attacker to bypass payment for future orders as long as the
trusted constants defined in the merchant’s database. This order total matches the total of order 1001.
conditional check guarantees that the cashier has received
a payment in full on behalf of the merchant. Therefore,
our analyzer removes the taint annotations of order total, III. A PPROACH
merchant ID and secret. In contrast, $_GET[’orderId’] This section presents our high-level approach. We first
is an untrusted request variable, and there is no check for define logic vulnerability in e-commerce applications, lay out
currency. our assumption, and then describe the core algorithm of our
After exploring before_process(), the symbolic ex- approach.
ecution eventually redirects the user to the final page
checkout_success.php (R4). When the symbolic exe-
cution reaches this page, it means that the checkout process is A. Definitions
complete and our analysis generates a final vulnerability report. Definition 1 (Merchant). A merchant accepts an order when
In the report of this payment module, order ID and currency it has been properly paid via a third-party cashier by a
are still tainted, indicating that this module is vulnerable to user. Merchant is the central role in e-commerce applications,
two types of logic attacks. The first type of attacks allows an coordinating communications between users and cashiers
attacker to pay for one order and avoid payments of future during checkout processes. Merchants are responsible for
orders by replaying the value of $_POST[’LKMAC’] or initializing orders, tracking payment status, recording order
$_GET[’LKMAC’] of the paid order. Note that the attacker details, finalizing orders and shipping products (or providing
can easily intercept the value of $_POST[’LKMAC’] or services) to users.
$_GET[’LKMAC’] of any paid order by changing the return
URL to her own choice in R2. The second type of attacks allows Definition 2 (Cashier). A third-party cashier accepts the
an attacker to pay less by paying the cashier the correct amount payment of an order from a user on behalf of a merchant.
indicated by $_SESSION[’order’]->info[’total’], Cashiers bridge the gap between merchants and users when
but in a different currency. For example, the merchant might they lack mutual trust. Users trust cashiers with their private
list product prices in European euros, but an attacker can pay information, and merchants expect cashiers to correctly charge
in U.S. dollars instead. 1 users.
To illustrate the first type of logic attacks on order ID, Definition 3 (User). A user initiates a checkout process on
suppose a user places two orders with IDs of 1001 and 1002 a merchant’s website, chooses a third-party cashier, makes a
on a merchant’s server. For either order, the designated cashier payment to the cashier and receives products (or services) from
is assumed to generate a secret MD5 value and visit a URL the merchant. User inputs and actions drive the logic flows
of page checkout_process.php with the secret value of checkout processes. Some users are malicious, therefore
only when a full payment has been made to the cashier. The merchants need to defend against untrusted user inputs and
secret values of orders 1001 and 1002 should be different and actions.
unpredictable. The URLs for the two orders are shown in the Definition 4 (Logic Flows in E-commerce Applications).
following. Logic flows in e-commerce applications are communications
between three possible parties: merchant nodes, cashier nodes
URL1: http://merchant.com/checkout_process.php? and user. Any logic flow during checkout may influence
orderID=1001&LKMAC=SecretMD5For1001
payment status. Note that one merchant web page may be
URL2: http://merchant.com/checkout_process.php? divided into multiple merchant nodes based on the runtime
orderID=1002&LKMAC=SecretMD5For1002 values of its HTTP request variables. For instance, one page may
perform an “insert”, “update” or ”delete” operation depending
The key problem for the detected logic vulnerability is on the value of $_GET[’action’]. Our analysis starts at
that this e-commerce application does not check the request the beginning merchant node n0 of a checkout process and ends
parameter values of orderID and LKMAC against trusted order at the destination merchant node nk where orders are accepted,
ID and MD5 value. Suppose an attacker has paid for order tracking taint annotations of payment status and signed tokens
1001 and intercepted the secret value SecretMD5For1001 across logic flows. Suppose for any valid node ni in the
by changing the server in the return URL (URL1) from checkout process, we start the analysis of ni with execution
merchant.com to attacker.com. For order 1002, rather state set Qi . At the end of the analysis of ni , we would have
than making a payment and being redirected to URL2, the execution state set Qj and a node to be visited next, namely
attacker can skip payment and jump directly to a forged nj . Formally, logic flows in an e-commerce application can be
merchant URL (URL1 shown above). The attacker can use represented as ⇧ = {(ni , Qi ) ! (nj , Qj ) | 0 i, j k}.
the GET and POST request parameter values of order 1001
for order 1002 to avoid payment. This substitution leads the Definition 5 (Logic State). A logic state consists of taint
merchant to mistakenly believe that order 1002 has been paid, annotations and links to other valid nodes of a checkout process.
while the cashier actually has received nothing on behalf of The propagation of logic states reflects changes of payment
the merchant for this order. Similarly, this vulnerability allows status. Specifically, for any order that a user places on a
merchant’s website with the integration of a third-party cashier,
1 This attack can be launched when the cashier accepts multiple currencies a logic state stores taint annotations for the following payment
for payments. status components and exposed signed tokens:
4
• Order ID. The identifier of the order which should be • Tainted merchant ID. When merchant ID is tainted,
paid for before it is accepted. an attacker can set up her own merchant account
on the designated cashier’s server where the original
• Order total. The total amount that the cashier should
merchant ID was set up. This allows the attacker to
receive from the user on behalf of the merchant.
send payments to herself instead of the merchant for
• Merchant ID. The identifier used by the cashier for orders placed on the merchant’s website. Note that
the merchant who is selling products or services. The a check on the secret between the merchant and the
cashier will ultimately transfer the received money cashier can replace the check on merchant ID because
from the user to the merchant. the secret is a unique, verifiable value set by the
merchant.
• Currency. The currency (system of money) in which
the order payment should be made. • Tainted currency. For cashiers that accept multiple
currencies, it is possible to pay less for orders via the
• Exposed signed token. An encrypted value that is
use of a different currency without changing the order
signed with a secret between the merchant and the
total amounts.
cashier. It can act as a cashier’s signature and is
considered exposed when it is visible to users in the • Exposed signed token. An exposed signed token
Document Object Model (DOM) tree of a merchant invalidates any security checks against trusted symbolic
page. values. This is because such a signed request may be
Definition 6 (Logic Vulnerabilities in E-commerce Appli- forged by an attacker rather than coming from a trusted
cations). A logic vulnerability in an e-commerce application cashier.
exists when for any accepted order ID, the merchant cannot
verify that the user has correctly paid the cashier the amount B. Automated Analysis
of order total in the expected currency to merchant ID. Our
Section III-B1 presents our detection algorithm which
definition is inspired by and developed from an extensive study
explores critical logic flows in e-commerce applications among
of cashier documentation, open-source e-commerce applications
three parties (merchant, cashier and user). Section III-B2
and related work [30, 33]. A payment is secure when both
describes taint manipulation rules which reflect changes to
the integrity and authenticity of payment status are ensured.
payment status.
Tampering with currency is a new attack vector we discovered
in our study. 1) Logic Vulnerability Detection Algorithm: Figure 3
Assumption Third-party cashiers are secure. We treat third- presents our vulnerability detection algorithm which forms
party cashiers as black boxes and assume that they are perfectly the core of our approach. It integrates symbolic execution of
secure. Most third-party cashiers’ source code is unavailable, merchant nodes and taint analysis, and connects individual
but many cashiers have been vetted heavily. The security nodes to explore valid logic flows in e-commerce applications.
of a third-party cashier is orthogonal to the security of its We have four possible pairs of HTTP requests from the client
integration in an e-commerce web application. Developers of side to the server side: (user, merchant), (user, cashier), (cashier,
payment modules are often less security-conscious than those merchant) and (merchant, cashier). Attackers may skip user-to-
of cashiers, thus payment modules are generally more prone cashier requests, but they need to send the same number of user-
to logic vulnerabilities. to-merchant requests to carry out all necessary steps of during
checkout. Consequently, our algorithm analyzes merchant nodes
Based on logic vulnerabilities in e-commerce web applica- that belong to a checkout navigation path in order.
tions, it is easy to launch attacks on live websites. Simply using There are three functions in Figure 3 and the first function
browser extensions, attackers can withhold HTTP requests, D ETECT V ULS is the main function of our analysis algorithm.
modify requests or completely forge requests. Moreover, The second function A NALYZE N ODE analyzes each merchant
attackers can exploit a signed token to pose as a cashier, reuse node individually, and the third function G ET N EXT N ODE
payment information from previous orders or intercept cashiers’ connects nodes together for valid logic flows. The analysis
responses by changing return URLs in HTTP forms. begins from start node ns with a start execution state qs . Both
In summary, logic vulnerabilities in e-commerce applica- ns and qs are extracted from specifications Spec. An execution
tions are caused by the following five types of taint annotations: state q contains a logic state, memory maps for global and local
variables, alias information, etc. Our algorithm analyzes logic
• Tainted order ID. To bypass order payments, attackers flow (user, M ERCHANT(ns )) first, and continues until all valid
can replay the payment information of a previous order logic flows are explored. In the end, for each execution state qf
from the same merchant. As long as the order total in the final execution state set Qf , function C HECK L OGIC V ULS
and currency of unpaid orders match the ones of the checks the logic state in qf and reports any detected logic
previously paid order, the unpaid orders would be vulnerabilities V uls.
accepted because order ID is not verified.
Function A NALYZE N ODE recursively analyzes merchant
• Tainted order total. Attackers can pay an arbitrary nodes of valid logic flows until the final node nf is reached. The
amount for an order by tampering with the order total final execution state set Qf is only updated when a new final
sent to a third-party cashier if order total is not verified. execution state qf has a uniquely new logic state. The reason
A partial payment to the cashier is still necessary. behind this update strategy is that other data in an execution
5
D ETECT V ULS(Spec) self-cycle logic flows, and URLs that are irrelevant to checkout.
1 ns G ET S TART N ODE(Spec) Each reset function within function G ET N EXT N ODE stores
2 qs I NIT S TATE(Spec) in n the extracted value of a particular type of URL, and
3 qs A DD C OMM(user , M ERCHANT(ns ), qs ) resets the URL to null. The values of header redirection
4 Qf A NALYZE N ODE(ns , qs , ;, Spec) URL (obtained via function R ESET R EDIRECTION) and form
5 Vuls C HECK L OGIC V ULS(Qf ) action URL (obtained via function R ESET F ORM ACTION) are
6 return Vuls examined first. When URL n points to a cashier node, a logic
flow (user, C ASHIER(n)) is added. To model cashiers’ responses,
A NALYZE N ODE(n, q, Qf , Spec) this function examines the values of callback URL (obtained
1 nf G ET F INAL N ODE(Spec) via function R ESET C ALLBACK U RL) and return URL (obtained
2 if n = nf via function R ESET R ETURN U RL). Note that callback URL and
3 then Qf Qf [ {qf } return URL can only be set after a cashier has been visited.
4 return Qf Callback URL is optional and can be visited first by a cashier
5 q P ROPAGATE N ODE S TATE(n, q) to notify its merchant the completion of a payment transaction.
6 Q S YMBOLIC E XECUTION(n, q, Spec) Return URL is required and a user must relay a cashier’s
7 for each qi in Q response to this URL to confirm a paid order on a merchant’s
8 do hn 0 , qi i G ET N EXT N ODE(qi , Spec) server. The return value of function G ET N EXT N ODE is a pair
9 Qf A NALYZE N ODE(n 0 , qi , Qf , Spec) of merchant node n that should be visited next and the updated
10 return Qf state q.
G ET N EXT N ODE(q, Spec) 2) Taint Rules: To keep track of the integrity and authen-
1 n R ESET R EDIRECTION(q) ticity of payment status, we designed a few taint manipulation
2 if n = null rules. The integrity of payment status can thwart HTTP
3 then n R ESET F ORM ACTION(q) parameter tampering attacks, and the authenticity of payment
4 if I S C ASHIER(n, Spec) status defends against forged payment status which is coined
5 then q A DD C OMM(user , C ASHIER(n), q) with predictable or exposed values of request variables. To be
6 n R ESET C ALLBACK U RL(q) more specific, untainted order ID, order total, merchant ID
7 if n = null and currency ensure the integrity, while no exposure of signed
8 then n R ESET R ETURN U RL(q) tokens ensures the authenticity. The underlying assumptions
9 q A DD C OMM(user , M ERCHANT(n), q) of the taint rules are: 1) requests from users are untrusted;
10 return hn, qi 2) unsigned cashier requests sent via insecure channels are
untrusted; and 3) cashier responses that are relayed by users
to merchants via HTTP redirection (status code 302) are also
Figure 3: Algorithm for Vulnerability Detection. untrusted. Initially, order ID, order total, merchant ID and
currency are all tainted.
When a merchant correctly verifies a payment status
state have no impact on the final vulnerability results. Function component, the taint annotation of the checked component
P ROPAGATE N ODE S TATE propagates an execution state q from a should be removed. Our approach uses taint removal rules for
previous node (np ) to the current merchant node (n), performing the following three cases:
a few operations on q. Specifically, this function updates runtime
constants such as $_SERVER[’PHP_SELF’], updates array • Conditional checks. When an (in)equality conditional
$_GET based on the query string of node n, updates array check verifies an untrusted value against a trusted
$_POST based on the form elements of node np and resets the symbolic value of a payment status component, remove
memory map of local variables. By default, request variables taint from the checked payment status component.
have the symbolic top value, which represents all possible • Writes to merchant database. When a tainted value
values including null. Next, merchant node n is symbolically is written into a merchant’s database with INSERT
executed via function S YMBOLIC E XECUTION, and Q is the end or UPDATE queries, conservatively remove taint from
execution state set for n. During symbolic execution, HTML the component. Before a merchant employee ships
form action URLs, form elements and parameter values of a product or provides a service for an order, she
merchant-to-cashier cURL 2 requests are monitored in search needs to review order details retrieved from database
of links to other merchant nodes or cashier nodes. tables. If a modified component is written to database,
the merchant employee can easily spot the modified
To connect nodes of valid logic flows, function G ET N EXT N-
component and thus reject the order with the modified
ODE examines and resets four types of links: redirection URL,
component.
form action URL, callback URL and return URL. A redirection
URL or form action URL can point to either a cashier node • Secure communication channels. For synchronous
or a merchant node, while a callback URL or return URL merchant-to-cashier cURL requests, remove taint for
can only point to a merchant node. To navigate only along order total, merchant ID or currency when such com-
valid logic flows, we discard URLs that form backward or ponent are included in cURL request parameters, and
remove taint for order ID unconditionally. Synchronous
2 http://curl.haxx.se/ requests are sent via secure communication channels,
6
and thus can guarantee the authenticity of payment
status changes that pass through such channels. app
code
Our approach has one taint addition rule: When a
PHP
conditional check for a cashier-to-merchant request relies on spec ASTi
Lexer IR
an exposed signed token, add taint to the exposed signed and Constructor
token. We keep track of all signed token values that are ns, qs
Parser
disclosed in DOM trees to users (typically in hidden HTTP
form elements). Although hidden HTTP form elements are nj, Qj IRi
invisible in the presentation layer of HTML pages, attackers
can obtain their values by simply viewing the source code
Qi Symbolic
of web pages. Note that not all exposed signed tokens are
Navigator Execution
tainted. The taint addition rule only applies when an exposed
Engine
signed token is used as an unpredictable value in a conditional
check for a cashier-to-merchant request. Once a signed token
is exposed, it is no longer unpredictable and therefore should nf, Qf
not be used in a conditional check. For example, suppose we
have a signed token in a hidden HTML form with symbolic
value md5($secret.$orderId.$orderTotal). If our Logic Vulnerability
analysis encounters equality check $_GET[’hash’] == Analzyer Report
md5($secret.$_GET[’oId’].$_GET[’oTotal’]),
it adds taint to the exposed signed token. This is because
although $secret is unpredictable, the values of the three
request variables are predictable. To pass the check, an attacker Figure 4: Symbolic Execution Framework.
can use the exposed signed token for $_GET[’hash’],
the order ID and order total associated with the exposed
signed token for $_GET[’oId’] and $_GET[’oTotal’] To guide our automated analysis, we need developers
respectively. to specify application-specific variable names of payment
status components, critical merchant pages in the checkout
IV. I MPLEMENTATION process, cashier URLs, callback URL, return URL, configurable
constants defined in the database and runtime values of a
We developed a symbolic execution framework that in-
few variables that are used for the resolution of dynamic file
tegrates taint analysis for PHP, one of the most prevalent
inclusion and class construction. For instance, a payment class
languages for building web applications. We extended the
can be dynamically constructed based on a user’s choice of
PHP lexer and parser of a static string analyzer [23, 27, 32]
payment methods. If the user chooses PayPal Standard as
written in OCaml. Our tool handles object-oriented features of
the payment method, we can specify the value of runtime
PHP, including classes, objects and method calls. We wrote
variable $_SESSION[’payment’] to be “paypal standard”
transfer functions for built-in PHP library functions, which
to precisely resolve the target of class $payment.
include string functions, database functions, I/O functions, etc.
Our tool consults Satisfiability Modulo Theories (SMT) solver
A. Symbolic Execution
Z3 [13] for branch feasibility, supporting arithmetic constraints,
simple string constraints and some other types of constraints. For each merchant page in the checkout process of an e-
Our implementation contains a total of 25, 113 lines of OCaml commerce application, our PHP lexer and parser transform its
code. Although our implementation targets the PHP language, source code into an IR. We followed the PHP language reference
the high-level approach is general and applicable to e-commerce and carefully wrote parsing rules to resolve reduce/reduce
software written in other languages. conflicts, assigned operator precedence to resolve shift/reduce
conflicts and used associativity to resolve other types of
Figure 4 shows the architecture of our framework. Given
conflicts. We observed that a PHP page can either statically or
the source code of an e-commerce web application and a
dynamically include other pages via PHP include or iframe,
specification for it, our analysis starts with a single execution
and the pages that are included can in turn include other pages.
state qs at merchant node ns , the first node in the checkout
To fully expand a PHP page, our analyzer infers static targets
process. For each merchant node ni , our PHP lexer and parser
of included pages when possible, and resorts to specification
transform the corresponding merchant page into an Abstract
when targets can only be decided at run time. For example,
Syntax Tree ASTi , which is then transformed into an Internal
static include require(DIRS_CLASSES.‘cart.php’)
Representation IRi by our IR constructor. After the symbolic
depends on the value of constant DIRS_CLASSES, while
execution engine explores all possible control flow paths of IRi ,
dynamic include require($language.‘.php’) depends
we have a set of end execution states Qi . Next, the navigator
on the runtime variable $language.
searches for valid logic flows, and continues symbolic execution
for new merchant nodes until the final merchant node nf is For heap modeling, our tool uses five variable maps:
reached. Finally, the logic analyzer checks all the unique logic a variable-to-symbolic-value memory map, an instance-to-
states of final execution state set Qf , and then reports any class-name map, an alias-to-variable map, an array-parent-to-
detected logic vulnerabilities. array-elements map and an object-parent-to-object-properties
7
map. First, the variable-to-symbolic-value map allows us to other hand, to explore all possible inter-procedural edges, our
model a heap symbolically. A symbolic value is a recursive approach adopts a global call stack which stores snapshots of
data structure composed of the following types: literal, basic previous function environment before function calls. A function
symbolic PHP variable, library function call, concatenation environment snapshot includes a parameter-argument map for
of two symbolic values, arithmetic expression, comparison the inter-procedural function call which is going to be explored,
expression and symbolic PHP resource. For instance, symbolic the work list and end execution states of the current function,
value md5("hello".$_GET[’orderID’]) represents a etc.
call to library function md5 with a symbolic argument of a
Our tool consults the SMT solver Z3 for constraint solving.
concatenation of two symbolic values: a string literal “hello”
When a conditional is encountered during symbolic execution,
and a basic symbolic variable $orderID of type integer.
our analyzer transforms the conditional into a formula of the
Second, given a class instance and a method name, the
smtlib2 format, conjuncts the new formula with the current
instance-to-class-name map enables us to quickly retrieve
path condition, and feeds the merged path condition to Z3 to
the corresponding class method definition. Third, the alias-
get an answer. When both branches are feasible, we select one
to-variable map allows us to correctly update a symbolic heap.
branch to explore first, and add the other branch to the current
Aliases are created when: a method is called from within
work list. We support the following types in our constraints:
an object context ($this becomes available); a variable is
boolean, integer, real, string, array, object, resource, null and
assigned by reference; and a function/method has pass-by-
>. We try to infer the satisfiability of simple string constraints,
reference arguments or returns a reference. Last, the two maps
which can contain literals, string variables, and operators
for array and object variables enable us to track the children of
such as =, 6=, <, , > and . To symbolically represent PHP
arrays and objects respectively. Our tool uses one memory map
library function calls, we use define-fun in Z3 for function
for global variables and one memory map for local variables.
declarations.
To model arrays and objects in PHP, we adopt the McCarthy
$error = false;
rule for list manipulations [13]. Given an array a, an array if ($_POST[’x_response_code’] == ’1’) {
element e and array index i, let a[i] represent an array select if (tep_not_null(AUTHORIZENET_MD5_HASH) &&
and a{i e} represent an array store with the element at ($_POST[’x_MD5_Hash’] != strtoupper(
md5(AUTHORIZENET_MD5_HASH .
index i set to e. By the McCarthy rule, we have the following: AUTHORIZENET_LOGIN_ID .
$_POST[’x_trans_id’] .
(8 array a)(8 element e)(8 index i, j) $this->format_raw($order->info[’total’])
i = j ! a{i e}[j] = e )))) {
$error = ’verification’;
^ i 6= j ! a{i e}[j] = a[j] } elseif ($_POST[’x_amount’] !=
$this->format_raw($order->info[’total’])) {
$error = ’verification’;
Our implementation precisely retrieves and updates array }
elements (or object properties) whenever possible. Otherwise, } elseif ($_POST[’x_response_code’] == ’2’) {
when an index of an array variable (or the field of an object $error = ’declined’;
} else {
property) is >, all possible values of the array elements (or $error = ’general’;
object properties) are merged. For example, suppose we have a }
simple array $arr=array(1=>"x",2=>"y"). If the value
if ($error != false) {
of array index $i is >, the value of $arr[$i] is either “x” or tep_redirect(tep_href_link(
“y”. We also use the McCarthy rule to symbolically represent FILENAME_CHECKOUT_PAYMENT,
arrays and objects. As an example, the symbolic representation ’payment_error=’ . $this->code .
’&error=’ . $error, ’SSL’, true, false));
of $arr is: }
array update(array update(array(), 1, “x”), 2, “y”)
Figure 5: Example for Path Exploration.
B. Path Exploration
Given a start execution state, our goal is to explore all Consider the example in Figure 5 for path exploration.
possible intra-procedural and inter-procedural edges in the Since the default values of request variables are >,
control-flow graph (CFG) of a merchant node. We use a work- all possible control-flow edges are explored. Only one
list-based algorithm and explore CFG edges with a depth-first exploration path in the example leads to a valid logic
strategy. On one hand, to explore all possible control flows flow, while the other paths redirect users to a payment
within a function/method body, a work list stores execution node with an error message in $error. For the second
states for feasible branches that have not been explored yet. if conditional on the valid path, there is a method call
Each execution state includes a program counter (consists of a $this->format_raw($order->info[’total’]).
basic block number and a statement number within the basic To follow this inter-procedural edge, our analyzer first looks
block), a logic state, path condition, memory maps of global up the class name of object $this and then the definition
and local variables, etc. We set a configurable quota for the of method format_raw in the corresponding class. Next,
maximum number of similar execution states in a work list the analyzer updates aliases for pass-by-reference parameters
to avoid state explosion. When the quota for a work list is which include $this, initializes parameter values based on
exhausted, we only add an execution state to the work list if it the arguments of the method call, passes on the memory
has either a new program counter or a new logic state. On the map of global variables and pushes a snapshot of the current
8
function environment into the global call stack. At the end of TABLE I: Payment Modules for Cashiers.
the symbolic execution for format_raw, we have a set of
execution states Q. After the method call returns, our analyzer Cashier Modules Unique Callback
pops the function environment from the global call stack 2Checkout 1 1 N
and maps Q to Q0 to update method arguments that have Authorize.net 2 2 N
pass-by-reference parameters. To continue path exploration ChronoPay 1 1 Y
after the call, Q0 is added to the current work list. When all inpay 1 1 Y
possible paths are explored, merchant ID and order total are iPayment 3 1 Y
untainted in the execution state of the valid flow which keeps Luottokunta 2 2 N
the value of $error unchanged. Moneybookers 23 1 Y
NOCHEX 1 1 N
C. Logic Flows PayPal 5 5 Y
PayPoint.net 1 1 N
The focus of our analysis is critical logic flows of a PSiGate 1 1 N
successful checkout process. We discard backward flows, error RBS WorldPay 1 1 Y
flows or aborted flows since they are irrelevant to our security Sage Pay 3 3 Y
analysis. First, a backward flow happens when an error has Sofortüberweisung 1 1 Y
occurred in a merchant node n, and the user is redirected to a
previous merchant node or the same merchant node n. Second, Sum 46 22 8
an error flow refers to a redirection to a special error page or a
visited page with an error message in a request variable. In the
first case, the special error page does not belong to the critical total. If these two request parameters are written to a merchant’s
checkout process and the flow to this page is discarded. In the database, they will be read from the database and displayed
second case, flows to pages with a symbolic error message clearly to a merchant employee. Since she needs to review order
variable are backward flows, which are automatically discarded. details before accepting an order, she may reject any order with
Last, an aborted flow happens when a serious error occurs and abnormal payment status. Consequently, the taint annotations
the rendering of a merchant page is stopped with an exit of order ID and order total should be removed based on the
statement. specifications for $_GET[’v1’] and $_GET[’v2’].
In search of links to other nodes, our analyzer parses
symbolic values of HTTP forms and cURL parameters. Since V. E MPIRICAL E VALUATION
string literal is not the only type that a symbolic value can
represent, we cannot simply use regular expressions such To evaluate the effectiveness and performance of our
as