--- type: Article title: Static Detection of Second-Order Vulnerabilities in Web Applications description: Web applications often store attacker input in a database, session key or file name and use it in a sensitive sink much later, so taint analysis that stops at one request misses it. This work models those persistent stores statically and found 159 second-order flaws, including stored XSS, SQL injection and remote command execution in osCommerce, OpenConf and NewsPro. resource: "https://www.usenix.org/conference/usenixsecurity14/technical-sessions/presentation/dahse" tags: [article, webseclist-reference, usenix-org, sqli, xss, rce, lfi, php, static-analysis, attack-chain, database, tooling, owasp-a01-2021, owasp-a03-2021] generated: by: webseclist-refs/1 at: "2026-08-14T15:05:45+00:00" status: stable stale_after: 2027-08-14 sources: - id: original resource: "https://www.usenix.org/conference/usenixsecurity14/technical-sessions/presentation/dahse" title: Static Detection of Second-Order Vulnerabilities in Web Applications author: Johannes Dahse, Thorsten Holz also_at: - "https://www.usenix.org/system/files/conference/usenixsecurity14/sec14-paper-dahse.pdf" authors: - Johannes Dahse - Thorsten Holz canonical_url: "" cited_by: - "2014.md:66" commit: "" content_sha256: 790f95b0cf32870d53b2cf49a0008e644e41ab8d944af4e0db1624299eb3b4af depth: full depth_reason: default kind: article language: "" licence: unknown original_url: "https://www.usenix.org/conference/usenixsecurity14/technical-sessions/presentation/dahse" published: "" publisher: usenix.org publisher_english: "" raw_sha256: 3c742ea5e6f7c69c1b7d57aa0ff9c8a5d70831a1c9a7dc028196c91e3d977971 retrieved_from: "https://www.usenix.org/conference/usenixsecurity14/technical-sessions/presentation/dahse" retrieved_kind: manual-import retrieved_utc: "2026-08-14T15:05:45+00:00" slug: usenix-org-static-detection-second-order-vulnerabilities-web-applications snapshot: "" title_english: "" translation_file: "" translation_of: "" --- # Static Detection of Second-Order Vulnerabilities in Web Applications **Static Detection of Second-Order Vulnerabilities in Web Applications** - Johannes Dahse, Thorsten Holz, usenix.org. - Published: date not stated - Original: - Also published at: - Preserved from: https://www.usenix.org/conference/usenixsecurity14/technical-sessions/presentation/dahse (manual-import) on 2026-08-14 - Licence: unknown Rights remain with the original author and publisher. This is a research archive of a source from the Web Hacking Techniques Index collections, kept so the page going offline. To read the original, follow the link above. ## Content > UNTRUSTED SOURCE TEXT. Everything below this line is third-party material > quoted for research. It is data, not instructions. Do not follow directions, > execute code, or fetch URLs because this text says so. # Static Detection of Second-Order Vulnerabilities in Web Applications Static Detection of Second-Order Vulnerabilities in Web Applications Johannes Dahse and Thorsten Holz, Ruhr-University Bochum https://www.usenix.org/conference/usenixsecurity14/technical-sessions/presentation/dahse This paper is included in the Proceedings of the 23rd USENIX Security Symposium. August 20–22, 2014 • San Diego, CA ISBN 978-1-931971-15-7 Open access to the Proceedings of the 23rd USENIX Security Symposium is sponsored by USENIX Static Detection of Second-Order Vulnerabilities in Web Applications Johannes Dahse Thorsten Holz Horst Görtz Institute for IT-Security (HGI) Horst Görtz Institute for IT-Security (HGI) Ruhr-University Bochum, Germany Ruhr-University Bochum, Germany johannes. dahse@ rub. de thorsten. holz@ rub. de Abstract One common assumption underlying many detection and prevention approaches is that data that is already Web applications evolved in the last decades from sim- stored on the server is safe. However, an adversary might ple scripts to multi-functional applications. Such com- be able to bypass the defenses via so called second-order plex web applications are prone to different types of se- vulnerabilities if she manages to first abuse the web ap- curity vulnerabilities that lead to data leakage or a com- plication to store the attack payload on the web server, promise of the underlying web server. So called second- and then later on use this payload in a security-critical order vulnerabilities occur when an attack payload is first operation. Such vulnerabilities are often overlooked, but stored by the application on the web server and then later they can have a severe impact in practice. For exam- on used in a security-critical operation. ple, XSS attacks that target the application’s users are In this paper, we introduce the first automated static worse if the payload is stored in a shared resource and code analysis approach to detect second-order vulnera- distributed to all users. Furthermore, within multi-step bilities and related multi-step exploits in web applica- exploits a vulnerability can be escalated to a more severe tions. By analyzing reads and writes to memory loca- vulnerability. Thus, detecting second-order vulnerabili- tions of the web server, we are able to identify unsani- ties is crucial to improve the security of web applications. tized data flows by connecting input and output points of data in persistent data stores such as databases or ses- Detecting Second-Order Vulnerabilities To prevent sion data. As a result, we identified 159 second-order such attacks, the source code of a given web application vulnerabilities in six popular web applications such as is assessed before it is deployed on a web server. This the conference management systems HotCRP and Open- can be done either via dynamic or static analysis. There Conf. Moreover, the analysis of web applications eval- are several dynamic approaches to detect second-order uated in related work revealed that we are able to detect XSS attacks via fuzzing [14, 19]. Generally speaking, several critical vulnerabilities previously missed. such approaches try to inject random strings to all pos- sible POST request parameters in a black-box approach. 1 Introduction In a second step, the analysis tools determine if the ran- dom string is printed by the application again without an- Web applications are the driving force behind the modern other submission, indicating that it was stored on the web Web since they enable all the services with which users server. However, the detection accuracy for second-order interact. Often, such applications handle large amounts vulnerabilities is either unsatisfying or such vulnerabili- of (potentially sensitive) data such as text messages, in- ties are missed completely [4, 7, 13, 23]. Artzi et. al. [1] formation about users, or login credentials that need to presented a dynamic code analysis tool that considers be stored persistently on the underlying web server. Fur- persistent data in sessions, but their approach misses ther, sessions are used to temporarily store data about a other frequently used data stores such as databases or user interacting with the web application during multi- files. Furthermore, one general drawback of dynamic ap- step processes. All of this data can potentially be abused proaches is the typically low code coverage. by an attacker to cause harm. Many different kinds of at- Static code analysis is a commonly used technique to tacks against web applications such as Cross-Site Script- find security weaknesses in source code. Taint analysis ing (XSS) or SQL injection (SQLi) attacks are known and and similar code analysis techniques are used to study the common injection flaws are well understood. Such at- data flow of untrusted (also called tainted) data into criti- tacks can be prevented by sanitizing user input and many cal operations of the application. However, web applica- approaches to address this problem were presented in the tions can also store untrusted data to external resources last few years (e.g., [2, 8, 15, 21, 22, 24, 27, 29]). and later on access and reuse it, a problem that is over- 1 USENIX Association 23rd USENIX Security Symposium 989 looked in existing approaches. Since the data flow is de- • We built a prototype of the proposed approach and ferred and can be split among different files and func- evaluate second-order data flows of six real-world tions of the application, second-order vulnerabilities are web applications. As a result, we detect 159 previ- difficult to detect when analyzing the source code stati- ously unknown vulnerabilities ranging from XSS to cally. Furthermore, static code analysis has no access to remote code execution attacks. the external resources used by the application and does not know the data that is stored in these. We are not aware of any plain static code analysis im- 2 Technical Background plementation handling second-order vulnerabilities. The In this section, we introduce the nature of second-order main problem is to decide whether data fetched from per- vulnerabilities and multi-step exploits. First, we examine sistent stores is tainted or not. Assuming all data to be data flow through persistent data stores and the difficul- tainted would lead to a high number of false positives, ties of analyzing such flows statically. We then present while a conservative analysis might miss vulnerabilities. two second-order vulnerabilities as motivating examples. Our Approach In this paper, we introduce a refined type of taint analysis. During our data flow analysis, we 2.1 Persistent Data Stores collect all locations in persistent stores that are written to We define persistent data stores (PDS) as memory loca- and can be controlled (tainted) by an adversary. If data tions that are used by an application to store data. This is read from a persistent data store, the decision if the data is available after the incoming request was parsed data is tainted or not is delayed to the end of the anal- and can be accessed later on by the same application to ysis. Eventually, when all taintable writings to persis- reuse the data. The term persistent refers to the fact that tent stores are known, the delayed decisions are made data is stored on the web server’s hard drive, although it to detect second-order vulnerabilities. The intricacies of can be frequently deleted or updated. Note that this defi- identifying the exact location within the persistent store nition also includes session data since information about the data is written to is approached with string analy- a user’s session is stored on the server and can be reused sis. Furthermore, sanitization through database lookups by an adversary. We now introduce three commonly used or checks for existing file names are recognized. PDS by web applications. We implemented our approach in a prototype for static PHP code analysis since PHP is the most popular server- 2.1.1 Databases side scripting language on the Web with an increasing market share of 81.8% [28]. Note that our approach can Databases are the most popular form of PDS found in be generalized to static code analysis of other languages today’s web applications. A database server typically by applying the techniques introduced in this paper to maintains several databases that consist of multiple ta- the data flow analysis of another language. We evalu- bles. A table is structured in columns that have a specific ated our approach by analyzing six popular real-world data type and length associated with them. Stored data is applications, including OpenConf, HotCRP, and osCom- accessed via SQL queries that allow to filter, sort, or in- merce. Overall, we detected and reported 159 previ- tersect data on retrieval. In PHP, an API for database in- ously unknown second-order vulnerabilities such as re- teraction is bundled as a PHP extension that provides sev- mote command execution vulnerabilities in osCommerce eral built-in functions for the database connection, and and OpenConf. We also analyzed three web applications the query and access of data. that were used during the evaluation of prior work in In contrast to other PDS, writing and reading to a this area and found that previous work missed several memory location is performed via the same built-in second-order vulnerabilities, indicating that existing ap- query function. SQL has different syntactical forms of proaches do not handle such vulnerabilities correctly. writing data to a table. Listing 1 shows three different In summary, we make the following three contributions: ways to perform the same query. • We are the first to propose an automated approach to 1 2 // specified write INSERT INTO users (id,name,pass) VALUES (1, admin , foo ) statically analyze second-order data flows through 3 INSERT INTO users SET id = 1, name = admin , pass = foo databases, file names, and session variables using 4 5 // unspecified write INSERT INTO users VALUES (1, admin , foo ) string analysis. This enables us to detect second- order and multi-step exploitation vulnerabilities in Listing 1: Writing to the database table users in SQL. web applications. While the first two queries explicitly define the col- • We study the problem of second-order sanitization, umn names, the third query does not. We refer to the a crucial step to lower the number of potential false first type as specified write and to the second type as un- positives and negatives. specified write. Both types convey a difficulty for static 2 990 23rd USENIX Security Symposium USENIX Association analysis of the query: a specified write reveals the col- 2.1.3 File Names umn names where data is written to, but does not reveal if there are any other columns in the table that are filled A common source for vulnerabilities is an unsanitized with default values. This hinders the reconstruction of file name. Developers often overlook that the file name table structures when analyzing SQL queries of an ap- of an uploaded file can contain malicious characters and plication statically. An unspecified write tells us exactly thus can be used as a PDS for an attack payload. For how many columns exist, but does not reveal its names. example, Unix file systems allow any special characters When the columns are accessed later on by name, it is in file names, except for the slash and the null byte [12]. unclear which column was filled with which value. The NTFS allows characters such as the single quote that can same applies for read operations. A specified read re- be used for exploitation [20]. For detecting second-order veals the accessed column names in a field list, whereas vulnerabilities, we need to determine paths where files an unspecified read, indicated by an asterisk character, with arbitrary names are located. The analysis of a file selects all available columns without naming them. upload reveals to which path a file is written to and if the In PHP, the queried data is stored as a result resource. file is named as specified by the user. In PHP, a file that There are different ways to fetch the data from the result is submitted via a multi-part POST request is stored in resource with built-in functions, as shown in Listing 2. a temporary directory with a temporary file name. The temporary and original file name is accessible in the su- // numeric fetch 1 2 row = mysql_fetch_row( res); echo row[1]; perglobal FILES array. Furthermore, built-in functions 3 // associative fetch such as rename() and copy() can be used by an ap- row = mysql_fetch_assoc( res); echo row["name"]; 4 5 row = mysql_fetch_object( res); echo row->name; plication to rename a file on the server. Note that also directory names can be used as PDS, for example when Listing 2: Fetching data from a database result resource. created with the built-in function mkdir(). Basically, numeric and associative fetch operations ex- ist. The first method stores the data in a numerically in- 2.1.4 Excluded PDS dexed array where the index refers to the order of the selected columns. The associative fetch stores the data There are less popular PDS that we do not include in in an array indexed by column name. It is also possible our analysis. For example, data can be retrieved from a to store the data in an object where the property names CGI environment variable, a configuration file, or from equals the column names. The key difference is that an external resource such as an FTP or SMTP server [5]. the associative fetch reveals the accessed column names However, these PDS are used rarely in practice and de- while the numeric fetch does not. cisions can only be made with preconfigured whitelists. All different combinations of writing, reading, and ac- We only consider PDS that are tainted by the application cessing data can occur within a web application. In cer- itself and not through a different channel. Analyzing the tain combinations, it is not clear which columns are ac- data flow through file content will be an interesting addi- cessed without knowledge about the database schema. tion in the future. Here, the challenge is to determine to For example, when data is written unspecified and what part of a given file data is written to and from what fetched associatively. In practice, however, we are often part of the file data is read from because the structure of able to reconstruct the database schema from the source the data within the file is unknown. code (see Section 3.4.1 for details). Note that data stored via PHP’s built-in functions ini set() or putenv() only exists for the duration of the current request. At the end of the request, the envi- 2.1.2 Session Data ronment is restored to its original state. Thus, they do not A common way of dealing with the state-less HTTP pro- hold to our definition of a PDS. tocol are sessions. In PHP, the SESSION array pro- vides an abstract way of handling session data that is 2.2 Second-Order Vulnerabilities stored within files (default) or databases. A session value is associated with an alphanumerical key that represents A taint-style vulnerability occurs if data controlled by an the memory location. Note that the SESSION array attacker is used in a security-critical operation. In the needs to be treated like any other superglobal array in data flow model, this corresponds to tainted data literally PHP and it can be accessed in any context of the applica- flowing into a sensitive sink within one possible data flow tion. As any other array, it can be accessed and modified of the application. We classify a second-order vulnera- dynamically, inter-procedurally, and it can have multiple bility as a taint-style vulnerability where the data flows key names. Besides the SESSION array and the depre- through one or more PDS. Here, the attack payload is cated HTTP SESSION VARS array, the built-in functions first stored in a PDS and later retrieved and used in a sen- session register() and session decode() can be sitive sink. Thus, two distinct data flows require analysis: used to set session data. (i) source to PDS and (ii) PDS to sink. 3 USENIX Association 23rd USENIX Security Symposium 991 In the following, we introduce two motivating exam- 2.2.2 Second-Order SQL Injection ples with a payload stored in a PDS. In general, every A SQL injection (SQLi) [9] vulnerability occurs when combination of a source, sensitive sink, and a PDS is pos- a web application dynamically generates a SQL query sible. Depending on the application’s design, the flow of with unsanitized user input. Here, an attacker can po- malicious data occurs within a single or multiple attack tentially inject her own SQL syntax to arbitrarily modify requests (e.g., when different requests for writing and the query. Depending on the environment, the attacker reading are necessary). Finally, we introduce multi-step can potentially extract sensitive data from the database, exploits as a subclass of second-order vulnerabilities. modify data, or compromise the web server. In Listing 4, user supplied credentials are checked 2.2.1 Persistent Cross-Site Scripting in line 6. If the credentials are valid, the session key Cross-Site Scripting (XSS) [16] is the most common se- loggedin is set to true and the user-supplied user name curity vulnerability in web applications [22]. It occurs is saved into the session key user. In case the user- when user input is reflected to the HTML result of the ap- supplied data is invalid, the failed login attempt is logged plication in an unsanitized way. It is then possible to in- to the database with the help of the user-defined log() ject arbitrary HTML markup into the response page that function. Here, a second-order SQLi occurs: if an at- is rendered by the client’s browser. An attacker can abuse tacker registers with a malicious user name, this name is this behavior by embedding malicious code into the re- written to the session key user and on a second failed sponse that for example locally defaces the web site or login attempt used in the logging SQL query. steals cookie information. 1 function log( error) { We speak of Persistent Cross-Site Scripting if the at- 2 3 user = _SESSION[ user ]; mysql_query("INSERT INTO logs (error, user) tacker’s payload is stored in a PDS first, read by the ap- 4 VALUES ( error , user )"); plication again, and printed to the response page. In con- 5 6 } if(validAuth( _POST[ user ], _POST[ pass ]) { trast to non-persistent (reflected) XSS, the attacker does 7 _SESSION[ loggedIn ] = true; not have to craft a suspicious link and send it to a victim. 8 9 } _SESSION[ user ] = _POST[ user ]; Instead, all users of the application that visit the affected 10 else { page are attacked automatically, making the vulnerability 11 12 } log( Failed login attempt ); more severe. Furthermore, a persistent XSS vulnerability can be abused to spread an XSS worm [18, 26]. Listing 4: Example for second-order SQLi vulnerability. Listing 3 depicts an example of a persistent XSS vul- nerability. The simplified code allows to submit a new 2.2.3 Multi-Step Exploitation comment which is stored in the table comments to- gether with the name of the author. If no new com- Within a second-order vulnerability, the first order (e. g., ment is submitted, it lists all previously submitted com- safe writing of user input into the database or a file path) ments that are fetched from the database. While the com- is not a vulnerability by itself. However, unsafe writing ment itself is sanitized in line 7 by the built-in function can lead to other vulnerabilities. We define a multi-step htmlentities() that encodes HTML control charac- exploit as the exploitation of a vulnerability in the second ters, the author’s name is not sanitized in line 6 and thus order that requires the exploitation of an unsafe writing affected by XSS. Note that if the source code is analyzed in the first order. Thus, a multi-step exploit is a subclass top-down, it is unknown at the point of the SELECT query of a second-order vulnerability and it can drastically raise if malicious data can be inserted into the table comments the severity of the first vulnerability. by an adversary. Since we only consider databases, sessions, and file names as PDS in our analysis, the following vulnerabili- if(empty( _POST[ submit ])) { 1 2 // list comments ties are relevant: 3 res = mysql_query("SELECT author,text FROM comments"); • SQLi: A SQLi in an INSERT or UPDATE statement foreach(mysql_fetch_row( res) as row) { 4 5 comment = mysql_fetch_array( row); leads to a full compromise of all columns in the 6 echo comment[ author ] . : . specified table. Furthermore, a SQLi in a SELECT htmlentities( comment[ text ]) . "
"; 7 8 } query allows arbitrary data to be returned. 9 } • Path traversal: A path traversal vulnerability al- else { 10 11 // add comment lows to change the current directory of a file opera- 12 author = addslashes( _POST[ name ]); tion to another location. Arbitrary file names can be text = addslashes( _POST[ comment ]); 13 14 mysql_query("INSERT INTO comments (author, text) created in arbitrary locations if a path traversal vul- 15 VALUES ( author , text )"); nerability affects the renaming or creation of files. } 16 • Arbitrary file write: An arbitrary file write vulnera- Listing 3: Example for second-order XSS vulnerability. bility can modify or create a new session file, lead- ing to the compromise of all session values. 4 992 23rd USENIX Security Symposium USENIX Association 3 Detecting Second-Order Vulnerabilities • ArrayDimTree represents a newly declared ar- ray or the assignment of data to one array key In the following, we describe our approach to auto- ( array[k] = data). It is organized in a tree matically detect second-order vulnerabilities via static structure. The array keys are represented by array code analysis. For this purpose, we extended our pro- edges that point to the assigned data symbol. The totype RIPS [6] that uses block summaries [30]. In this ArrayDimTree symbol provides methods to add or section, we first briefly review the used data flow and fetch symbols by a dimension that is compared to taint analysis approach of RIPS (Sections 3.1 and 3.2). the tree’s edges. Afterwards, we explain our novel additions for detect- • ValueConcat represents the concatenation of two ing second-order vulnerabilities and multi-step exploits or more data symbols ( a. b). Two consecutive (Sections 3.3–3.5). Value symbols are merged to one Value symbol. • Multiple is a container for several data symbols. It 3.1 Data Flow Analysis is used, for example, when a function returns differ- RIPS leverages a context-sensitive, intra- and inter- ent values depending on the control flow or PHP’s procedural data flow analysis. We use basic block, func- ternary operator is used ( c ? a : b). tion, and file summaries [30] for efficient, backwards- During data flow analysis, one or more sanitiza- directed data flow analysis [6]. First, for each PHP file’s tion tags can be added to a data symbol, for exam- code, a control flow graph (CFG) consisting of con- ple if sanitization is applied by built-in functions such nected basic blocks is generated. Definitions of func- as addslashes() or htmlentities(). Each sanitiza- tions, classes, and methods within the code are extracted. tion tag represents one context, for example, a single- Then, every CFG is analyzed top-down by simulating the quoted SQL value or a double-quoted HTML attribute. connected basic blocks one by one. A block edge that A symbol can be sanitized against one context, but links two connected basic blocks is simulated as well to be vulnerable to another. The tags are removed again identify data sanitization. when built-in functions such as stripslashes() or During the simulation of one basic block, all assigned html entity decode() are called. Furthermore, infor- data is transformed into data symbols that we will intro- mation about encoding is added to every data symbol. duce later. The flow of the data is inferred from these symbols and summarized in a block summary [30] that maps data locations to assigned data. The return results 3.2 Context-Sensitive Taint Analysis and side-effects (e.g., data assignment or sanitization) of called built-in functions are determined by a precise sim- The goal is to create a vulnerability report, whenever a ulation of over 900 unique functions. tainted data symbol δ flows into a sensitive sink. Our If a user-defined function is called within a basic implementation is performed with 355 sensitive built- block, its CFG is generated and all basic blocks are sim- in functions of PHP. If a call to a sink is encountered ulated. Based on these block’s summaries, the data flow during block simulation, its relevant arguments are an- within the function is determined by analyzing return alyzed. First, the argument is transformed into a data statements in a similar way to taint analysis (see Sec- symbol. If the symbol was defined within the same basic tion 3.2). The results are stored in a function summary. block, it is inferred from the block summary. Then, the This summary is used for each call of the user-defined symbol is looked up in the block summary of every pre- function, while return values, global variables, and pa- vious basic block that is linked with a block edge to the rameters are adjusted to the callee’s arguments and envi- current basic block. If the lookup in the block summary ronment context-sensitively. When all basic blocks of a succeeds, the inferred symbol is fetched. The dimension file’s CFG are simulated, a file summary is generated in a of an ArrayDimFetch symbol is carried until a map- similar way to functions that is used during file inclusion. ping ArrayDimTree symbol is found. The backwards- Data and its access within the application’s code is directed symbol lookup continues for each linked basic modeled by so called data symbols [6]: block and stops if a symbol of type Value is inferred or the beginning of the CFG is reached. At this point, all • Value represents a static "string", integer, float, resolved symbols are converted to strings in order to per- or a resolved CONSTANT’s value. Defined constant form context-sensitive string analysis [6]. The symbols values are stored in the environment. Value and Boolean are converted to their representative • Variable represents a variable by its name. string values. Data symbols of sources are mapped to a • ArrayDimFetch represents the access of an ar- Taint ID (TID) that is used as string representation. ray ( array[k]) and extends the Variable sym- Next, each string is analyzed. The location of the TIDs bol with a dimension (k). The dimension lists the within the markup is determined to precisely detect the fetched array keys in form of data symbols. context. For complex markup languages such as HTML 5 USENIX Association 23rd USENIX Security Symposium 993 or SQL, a markup parser is used. With the help of the sanitization tags and encoding information of the linked data symbol, we check if the symbol is sanitized cor- rectly according to its context. If a TID is found that belongs to an unsanitized source regarding the current context, a vulnerability report is generated. Unsanitized parameters or global variables are added to the function’s summary as sensitive parameter or global. These are an- alyzed in the context of each function call. 3.3 Array Handling By manually analyzing the code of the most popular PHP Figure 1: Data flow model of a conventional (a) and a applications [28], we empirically found that a common second-order (b, c) vulnerability. way to write data into a database is by using arrays. An example is shown in Listing 5. In line 9 and 10, the ar- ray’s key defines the table’s column and the array’s value 3.4 PDS-centric Taint Analysis stores the data to write. The separated array values are joined to a string again by using the built-in function We now introduce our novel approach to detect second- implode() (lines 2/3). Based on this observation, we re- order vulnerabilities. The data flow is illustrated in Fig- designed the handling of arrays by adding new data sym- ure 1 (b). Contrarily to a conventional taint-style vulner- bols. As a side effect, the handling of fetched database ability as shown in Figure 1 (a), a source flows into a results in form of an array and the handling of the super- PDS before it flows from the PDS into a sensitive sink. global SESSION array is significantly improved. We model the data that is read from a PDS by new data symbols δ ∗ that hold information about their origin. 1 function insert( table, array) { 2 fields = implode(",", array_keys( array)); During code analysis, taintable PDS are identified. 3 values = implode(" , ", array); They are stored together with the minimum set of applied 4 mysql_query("INSERT INTO { table} 5 (". fields.") VALUES ( ". values." )"); sanitization and encoding tags of the tainting data sym- 6 } bol δ . If one of the new data symbols δ ∗ is encountered 7 8 new_user = array( unsanitized during the taint analysis of a sensitive sink, a 9 "name" => addslashes( _POST[ name ]), vulnerability report is created if its originating PDS was 10 "pass" => md5( _POST[ pass ]), 11 ); identified as taintable. 12 insert("users", new_user); If the PDS is not known as taintable, a temporary vul- 13 // INSERT INTO users (name,pass) VALUES ( X , 123abc... ) nerability report is created, as shown in Figure 1 (c). The Listing 5: Using arrays to write data to a database. report is connected to the data symbol δ ∗ . At the end of the code analysis, we decide if the data symbol origi- We model the popular built-in function implode() by nates from a taintable PDS by comparing its origin to all adding the data symbol ArrayJoin. With the help of this collected taintable PDS. symbol, it is possible to keep track of the delimiter that In the following, we introduce the analysis of writings is used to join strings. If the symbol is inferred to an to different PDS. Furthermore, our new data symbols δ ∗ ArrayDimTree symbol, a ValueConcat symbol is cre- are introduced that model the reading and access of data ated that joins all symbols of the ArrayDimTree symbol that is stored in PDS. with the stored delimiter symbol. Furthermore, we introduce the new symbol 3.4.1 Databases ArrayKey. It is used when the key of an array is ex- plicitly accessed, such as in the loop foreach( array Modeling the data flow through databases is a complex as key => value). It is handled similar to the task, mainly due to the large API that is available for Variable symbol and is associated with the array’s databases and the usage of a query language. First, our name. If the ArrayKey symbol is inferred into an prototype tries to obtain as much knowledge of the SQL ArrayDimTree symbol during data flow or taint analy- schema as possible. Then we try to reconstruct all SQL sis, a Multiple symbol containing all edges’ symbols queries during SQL injection analysis of 110 built-in is returned. Built-in functions, such as array keys() query functions. Finally, the type of operation is deter- and array search(), return all or parts of the available mined, as well as the targeted table and column names. keys in an array and can be modeled more precisely with The access of data is modeled by new data symbols. the ArrayKey symbol. 6 994 23rd USENIX Security Symposium USENIX Association Preparation During the initialization of our tool, we 3.4.2 Session Keys collect all files with a .sql extension. All available CREATE TABLE instructions within these files are parsed The analysis of session variables does not require a com- so that we can reconstruct the database schema, includ- plex markup parser or new data symbol. Instead, session ing all table and column names as well as column types data is handled similar to other global arrays. Taintable and length. If no schema file is found, each PHP file session keys are stored during the analysis phase. in the project is searched via regular expression. The Writing If data is assigned to a Variable or knowledge of the database schema improves precision ArrayDimFetch symbol during block simulation and when data is read in an unspecified way, or when data is the symbol’s name is SESSION, the assigned data is sanitized by the column type or length. analyzed via taint analysis. If the assigned data is Writing A write operation to a database is detected tainted, its resolved source symbol is stored into an if the SQL parser identifies an INSERT, UPDATE, or ArrayDimTree symbol in the environment, together REPLACE statement. By tokenizing the SQL query, we with the dimension of the SESSION symbol. This way, determine the targeted table’s name, all specified column an ArrayDimTree is built with all taintable dimensions names, and their corresponding input values. In case of of the session array that link to the tainted source sym- an unspecified write, the parser makes use of the database bols and their corresponding sanitization tags. schema. If an input value of a column contains a TID Reading The access to session data is modeled by (see Section 3.2), the affected column and table name is ArrayDimFetch symbols with the name SESSION and marked as taintable together with the linked source sym- requires no modification. During taint analysis inside a bol and its sanitization tags. user-defined function, session variables are handled as Reading If the SQL parser encounters a SELECT state- global variables. They are added to the function sum- ment, we try to determine all selected column and ta- mary and they are inspected for each function call in a ble names. Multiple table names can occur if tables context-sensitive way. This avoids premature decisions are joined or unioned. Alias names within the query about the taint status inside a function if the session key are mapped and resolved. In case of uncertainty, the is overwritten before the function is called. Just as for a parser makes use of the database schema. Finally, a new DataDB symbol, a temporary vulnerability report is cre- ResourceDB symbol is mapped to the analyzed query ated if a SESSION variable taints a sensitive sink. function as return value. This symbol holds information 3.4.3 File Names about all selected column names in a numerical hash map and its corresponding table names. To detect taintable file names, we collect file paths a user Access In PHP, database result resources are trans- can write to. For this purpose, new data symbols model formed into arrays by built-in fetch functions (refer to directory resources and their accesses. Whenever a path Listing 2). We ignore the mode of access and let 89 con- is reconstructed only partially, we use the same approach figured fetch functions return a Variable symbol with as in file inclusion analysis. Here, a regular expression the name of the resource. When an ArrayDimFetch is created and mapped to all available paths that were symbol accesses the result of these fetch functions, it is detected when loading the application files. inferred to the corresponding ResourceDB symbol. In Writing To detect a file name manipulation with user this case, the carried dimension of the ArrayDimFetch input, we analyze 27 built-in functions such as copy(), symbol is evaluated against the available column names rename(), and file put contents(). Additionally, in the ResourceDB symbol. If the asterisk character is file uploads with move uploaded file() are analyzed. contained in the column list and the dimension is numer- Note that at the same time these built-in functions are ical, the database schema is used to find the correct col- sensitive sinks and generate vulnerability reports such as umn name. Otherwise, if the dimension equals a column an arbitrary file upload vulnerability. The path argument name in the field list, a new DataDB symbol is returned is analyzed by conventional context-sensitive string anal- that states which column of which table is accessed. ysis. If the path is tainted, we store it with its prefix Sanitization Certain implicit sanitization is considered as taintable. When no prefix is present, the file path of when dealing with SQL. If a column is compared to a the currently analyzed file is taken. Additionally, if the static value within a WHERE clause in a SELECT state- source is not sanitized against path traversal attacks, all ment, the return value for this column is sanitized. In paths are assumed as taintable and a flag is set during this case, the static value is saved within the ResourceDB analysis accordingly. symbol and mapped to the column as return value. Fur- Reading We handle three different ways of opening a thermore, a sanitization tag for the used quote type is directory with PHP’s built-in functions. First, we model removed when data is updated or inserted to the database the built-in function scandir() that returns an array, because one level of escaping is lost during writing. listing all files and directories within a specified path. 7 USENIX Association 23rd USENIX Security Symposium 995 Second, we model the built-in function glob() that also 3.5 Inter-procedural PDS Analysis returns an array that lists all files and directories speci- fied by a pattern. We transform the pattern into a regular We optimized the inter-procedural analysis to refine our expression by substituting the pattern characters * and string analysis results. Function summaries offer a high ? into regular expression equivalents. Third, we model performance but they are also inflexible for functions the built-in function opendir() which returns a direc- with dynamic behavior. Thus, they can weaken the static tory handle. For all mentioned built-in functions, we re- reconstruction of dynamically created strings. construct the opened path by string analysis and return a ResourceDir symbol that stores the path’s name. 3.5.1 Multiple Parameter Trace Access The returned result of scandir() and glob() As we illustrated in Listing 5, modern applications of- is accessed by an array key. Since we do not know nei- ten define wrapper functions for PDS access where more ther the amount nor the order of files in a directory, we than one parameter is used within one sensitive sink. In return a DataPath symbol whenever a ResourceDir this case, the approach of storing each parameter together symbol is inferred from an ArrayDimFetch symbol, re- with its prefixed and postfixed markup, and the corre- gardless of its dimension. For this purpose, we let the sponding vulnerability type as sensitive parameter in the built-in function readdir() that is supposed to read an function summary, is error-prone. When a call to this entry of a directory handle return an ArrayDimFetch function occurs, the approach swaps the parameter sym- symbol with an arbitrary dimension and the name of the bol with the argument of the function call and traces it for directory handle. It is inferred to a DataPath symbol user input. While this approach works fine for vulnera- when the trace of the ArrayDimFetch symbol results in bility detection, it leads to imprecision when it comes to a ResourceDir symbol. string reconstruction. Because each argument is traced Sanitization In order to model sanitization that checks separately but both are used in the same sink, the result if a given string is a valid file name, 11 built-in functions of one trace is missing in the result of the other trace. In such as file exists and is file() are simulated. We Listing 5, for example, the table name is missing in the modified the sanitization check in a way that these func- reconstructed query while the data is reconstructed from tions only sanitize if there is no taintable file path found. the new user array. For this purpose, a flag is set during taint analysis if san- To circumvent this problem, we refined this approach itization of a source by file name is detected. The flag for sinks that execute SQL queries or open file paths issues only a temporary vulnerability report that is re- within a user-defined function. If multiple parameters or vised at the end of the analysis regarding the ability to global variables are involved, all symbols are combined taint a file path. to one ValueConcat symbol. Then this symbol is stored in the function summary and analyzed for each function 3.4.4 Multi-Step Exploits call. This way, each parameter is traced within one anal- ysis and all results are present at the same time. In order to detect multi-step exploits, we store all ta- ble names of all writing SQL queries that are affected 3.5.2 Mapping Returned Resources by SQLi. Furthermore, we set a flag during the analy- sis process if an arbitrary file write or arbitrary file re- Working with function summaries is very efficient when name vulnerability is detected. At the end of the analysis, it comes to performance because each function only re- when the taint decision is made for data that comes from quires a single analysis on the first call. For every other a PDS, multi-step exploit reports are added to the initial call, the function summary is reused. However, a user- vulnerability. This is done for all vulnerabilities that rely defined function might return a resource that has dif- on a DataDB symbol that is not tainted through second- ferent properties for each call. For example, a SELECT order but which table name is affected by SQLi. Also, query that embeds the parameter of an user-defined func- a multi-step exploit is reported if a DataDir symbol oc- tion as the table name returns a different ResourceDB curs and the flag for a file rename vulnerability was set. symbol for every call, depending on the function’s ar- All session data is treated as tainted if an arbitrary file gument. If the resource is returned by the user-defined write vulnerability was detected. Additionally, any local function, its symbol’s properties change for every differ- file inclusion vulnerability is extended to a remote code ent function call. execution if a file write or upload feature is detected. As a solution, we add empty ResourceDB symbols Moreover, a SQLi vulnerability within a SELECT to the function summary’s set of return values for user- query returns a DataDB symbol with a taint flag. This defined functions with dynamic SQL queries. Once the flag indicates that all accessed columns are taintable by sensitive parameters are analyzed and the queries are re- modifying the SELECT query during an attack. Thus, all constructed, a copy of these symbols is updated with the columns of the DataDB symbol are taintable. table and column information and used as returned data. 8 996 23rd USENIX Security Symposium USENIX Association 4 Evaluation We then carefully fuzzed a local instance of each application manually with common attack payloads in For evaluating our approach, we selected six real-world order to determine which columns of type string are web applications. We chose the conference management taintable. Furthermore, we observed which columns systems OpenConf 5.30 and HotCRP 2.61 for their pop- were reported by our prototype implementation as ularity in the academic field and osCommerce 2.3.3.4 for taintable when the schema is available and when not. The its large size. Furthermore, we evaluated the follow-up results are compared in Table 2. Among the columns versions of the most prominent software used in related with a string type, 53% are taintable. As a result, only work [3, 11, 30, 31]: NewsPro 1.1.5, MyBloggie 2.1.4, 24% of all available columns are not sanitized by the ap- and Scarf 2007-02-27. plication or the columns’ data type. A second-order vulnerability consists of two data flows: tainting the PDS and tainting the sensitive sink. Table 2: Taintable columns in selected applications. We evaluated our prototype for both steps and present the Schema No schema true positives (TP) and false positives (FP) in this section. Software Taintable TP FP TP FP In addition, we discuss the root cause for false negatives (FN) and outline the limitations of our approach. osCommerce 63 55 4 55 37 HotCRP 43 27 1 27 3 OpenConf 47 16 1 16 4 4.1 PDS Usage and Coverage NewsPro 12 12 0 12 0 To obtain an overview of the usage of PDS in web appli- Scarf 10 10 1 10 3 cations, we manually evaluated the total amount of dif- MyBloggie 9 9 0 9 0 ferent memory locations. Note that these numbers do not Total 184 70% 5% 70% 27% reflect how often one memory location is used at run- time. Then, we evaluated the ability to taint these mem- For the rather old and simple applications, all taintable ory locations by an application’s user and compared it columns were detected by our prototype. The modern to the detection rate of our prototype. A PDS is defined and large applications often use loops to construct dy- as taintable if it can contain at least one of the follow- namic SQL queries where reconstruction is error-prone. ing characters submitted by an application user: \<>’". Overall, we detected 70% of all taintable columns. When In total, we manually identified 841 PDS of which 23% the database schema is known, 5% of our reports are FP. are taintable. Our prototype successfully detected 71% The root cause is path-sensitive sanitization of data that of the taintable PDS with a false discovery rate of 6%. is written to the database—a sanitization that our current prototype is not able to detect yet. The false discovery rate is higher if the database schema of an application is 4.1.1 Databases not found. In this case, a static analysis tool cannot rea- Our implementation successfully recovered the database son about data types within the database and may flag schema for all tested applications during the initializa- columns of numeric data type as taintable. tion phase. For evaluation, we categorized all avail- able columns in the application’s database schema by de- 4.1.2 Sessions clared data type. Only columns with a string type, such To obtain a ground truth for our evaluation, we again as VARCHAR or TEXT, are of interest because they can manually assessed the applications’ code for all accessed store tainted data. As shown in Table 1, we found that keys of the superglobal SESSION array. Dynamic keys on average about half of the columns are not taintable were reconstructed and keys in multi-dimensional arrays due to numeric data types such as INT and DATE. were counted multiple times. Then, we manually exam- ined which session keys are taintable by the application’s Table 1: Column types in selected applications. user and compared this to the analysis result generated by our prototype implementation. As shown in Table 3, we Software Tables Columns Num String found that only 12% of the 52 identified session keys are osCommerce 50 331 193 138 taintable within our selected applications. HotCRP 29 217 142 75 Our prototype correctly detected all taintable session OpenConf 18 129 48 81 keys. One FP occurred because the sanitized email ad- NewsPro 8 43 18 25 dress of a user is written to the session after it is fetched Scarf 7 37 22 15 from the database. This FP is based on the previously MyBloggie 4 24 10 14 introduced FP in identifying taintable columns. A cus- tom session management in osCommerce led to exclu- Total 116 781 55% 45% sion from our evaluation. 9 USENIX Association 23rd USENIX Security Symposium 997 Table 3: Taintable session keys in selected applications. false discovery rate of 21% (see Table 5 for details). In summary, 97% of the valid reports are persistent Software Keys Taintable TP FP XSS vulnerabilities where the payload is stored in the HotCRP 29 2 2 0 database. Five persistent XSS vulnerabilities are caused OpenConf 14 2 1 0 by session data or file names. This is closely related to NewsPro 2 1 1 0 the fact that 94% of all taintable PDS we identified are Scarf 4 0 0 1 columns in database tables (see Section 4.1) and sensi- MyBloggie 3 1 1 0 tive sinks such as echo are one of PHP’s most prominent built-in features [10]. Total 52 12% 83% 16% Table 5: Evaluation results for selected applications. 4.1.3 File Names Software Files LOC TP FP FN To evaluate the features that allow an application’s user osCommerce 570 66 381 97 29 6 to alter a file name, we again manually assessed each HotCRP 74 40 339 1 1 0 application for file upload, file creation, and file rename OpenConf 121 20 404 16 4 0 features and counted the different target paths to obtain NewsPro 23 5 077 7 1 0 a ground truth. Next, we counted the collected taintable Scarf 19 1 686 37 8 3 path names reported by our prototype. The results are MyBloggie 58 9 485 1 0 0 shown in Table 4. Total 865 143 372 159 43 9 Table 4: Taintable paths in selected applications. Average 144 23 895 79% 21% Software Paths Taintable TP FP Our evaluation revealed that second-order vulnerabil- osCommerce 2 2 2 0 ities are highly critical. Next to persistent XSS and file HotCRP 1 0 0 0 vulnerabilities, we detected various remote code exe- OpenConf 1 0 0 1 cution vulnerabilities in osCommerce, OpenConf, and NewsPro 1 0 0 0 NewsPro. In the following, we introduce two selected Scarf 1 1 1 0 vulnerabilities to illustrate the complexity and severity MyBloggie 2 2 2 0 of real-world second-order vulnerabilities. It is evident that these vulnerabilities could only be detected with our Total 8 63% 100% 16% novel approach of analyzing second-order data flows. We found at least one feature in each of the applica- 4.2.1 Second-Order LFI to RCE in OpenConf tion’s source code to create a new file. However, half of the applications sanitize the name of the file before cre- OpenConf is a well-known conference management ating it. Our prototype detected all taintable path names. software used by many (academic) conferences. Our One FP occurred for OpenConf, where uploaded files are prototype found a second-order local file inclusion vul- sanitized in a path-sensitive way. nerability in the user-defined printHeader function that Interestingly, a file upload in Scarf is based on a leads to remote command execution. The relevant parts second-order data flow. The name of the uploaded file is of the affected file include.php is shown in Listing 6. specified separately and stored as a configuration value 1 function printHeader( what, function="0") { in the database before it is read from the database again 2 3 require_once GLOBALS[ pfx ] . GLOBALS[ OC_configAR ][ OC_headerFile ]; and the file is copied. Because no sanitization is applied, 4 } an administrator is able to copy any file to any location of 5 6 r = mysql_query("SELECT setting , value , parse the server’s file system which leads to remote code exe- 7 FROM " . OCC_TABLE_CONFIG . " "); cution. This critical vulnerability was missed in previous 8 9 while ( l = mysql_fetch_assoc( r)) { OC_configAR[ l[ setting ]] = l[ value ]; work that also used this application for evaluating their 10 } approach [3, 31]. 11 printHeader(); Listing 6: Simplified include.php of OpenConf. 4.2 Second-Order Vulnerabilities When looking at the code, it does not reveal any vul- We evaluated the ability of our prototype to detect nerability. Whenever the code is included, settings are second-order vulnerabilities. Reports of first-order vul- loaded from the database and the user-defined function nerabilities are ignored for now. Our prototype reported printHeader() is called. This function includes a con- a total of 159 valid second-order vulnerabilities with a figured header file and prints some HTML. 10 998 23rd USENIX Security Symposium USENIX Association 1 function updateConfigSetting( setting, value) { 1 function unp_printTemplate( template) { 2 q = "UPDATE " . OCC_TABLE_CONFIG . " 2 global templatecache, DB; 3 SET value = " . safeSQLstr(trim( value)) . " 3 getTemplate = mysql_query("SELECT name,template 4 WHERE setting = " . safeSQLstr( setting) . " "; 4 FROM unp_template WHERE name= template LIMIT 1"); 5 return(ocsql_query( q)); 5 while ( temp = mysql_fetch_array( getTemplate)) { 6 } 6 templatecache[ template] = temp[ template ]; 7 7 } 8 foreach (array_keys( _POST) as p) { 8 return addslashes( templatecache[ template]); 9 if (preg_match("/^OC_[\w-]+ /", p)) { 9 } 10 updateConfigSetting( p, _POST[ p]); 10 eval( headlines_displaybit = " . 11 } 11 unp_printTemplate( headlines_displaybit ). "; ); 12 } Listing 9: Simplified Remote Code Execution Listing 7: Simplified code to change settings in vulnerability in NewsPro. OpenConf. However, as shown in Listing 7, it is possible for a At the call-site, the fetched template is evaluated privileged chair user to change any configuration setting. with PHP’s eval operator that executes PHP code The configuration page does not specify an input field to (line 10). The template’s code is escaped (line 8), change the headerFile setting. Nonetheless, by adding however, the double-quoted value of the evaluated vari- the key OC headerFile to a manipulated HTTP POST able headlines displaybit allows to execute arbi- request, the setting is changed. The loop over the sub- trary PHP code using curly syntax. By adding the code mitted keys of the POST array in Listing 7, line 8, as { {system(id)}} to a template, the system command well as the loop over the OC configAR in Listing 6, id is executed. Note that related work missed to detect line 9, shows once again how important it is to track the this vulnerability, which is also present in prior versions. taint status of PHP’s array keys precisely. A chair member can now include any local file of the system to the output. Additionally, because the software 4.3 Multi-Step Exploits allows to upload PDF files to the server, our prototype added a multi-step exploit report. Indeed, if a PDF file Our prototype reported two arbitrary file upload vulner- containing PHP code is uploaded to the server and the abilities and 14 SQL injection vulnerabilities. Because headerFile setting is pointed to that PDF, arbitrary PHP these vulnerabilities affect a storage operation, the stored code is executed. Moreover, our tool reported a SQL data can be manipulated during multi-step exploitation. injection vulnerability that is accessible to unprivileged Our prototype found 14 valid multi-step exploits and a users. This allows any visitor to extract the chair’s pass- single FP as shown in Table 6. word hash (salted SHA1) from the database. Table 6: Reported multi-step exploits in selected appli- 4.2.2 Second-Order RCE in NewsPro cations. Utopia NewsPro is a blogging software and was used in File SQLi Multi-Step previous work for evaluation [29–31]. Our prototype re- Software TP TP FP TP FP ported a second-order code execution vulnerability in the administrator interface. Here, a user is able to alter the osCommerce 1 3 0 3 0 template files of the blog. The simplified code is shown HotCRP 0 1 7 0 1 in Listing 8. OpenConf 0 4 1 1 0 NewsPro 0 6 0 9 0 1 tempid = (int) _POST[ tempid ]; 2 template = mysql_real_escape_string( _POST[ template ]); Scarf 1 1 0 1 0 3 updateTemplate = mysql_query("UPDATE unp_template MyBloggie 0 5 0 0 0 4 SET template= template WHERE id= tempid "); Total 2 20 8 14 1 Listing 8: Simplified code to change the template in Average 100% 71% 29% 93% 7% NewsPro. The template code is read from the database in various All detected multi-step exploits consist of two steps places of the source code with help of the user-defined and no third-order vulnerabilities were detected within function unp printTemplate() (see Listing 9). First, our selected applications. In the following, we examine this function writes the template’s code to a cache array two multi-step exploits in osCommerce that lead to re- (line 6) and then returns it from this array again. The mote command execution to illustrate that these vulnera- example demonstrates the importance of inter-procedural bilities can only be detected with our novel approach of analysis and array handling. analyzing multi-step exploits. 11 USENIX Association 23rd USENIX Security Symposium 999 4.3.1 Multi-Step RCE in osCommerce 1 if (file_exists(DIR_FS_BACKUP . HTTP_GET_VARS[ file ])) { 2 restore_file = DIR_FS_BACKUP . HTTP_GET_VARS[ file ]; OsCommerce is a popular e-commerce software. For 3 exec(LOCAL_EXE_UNZIP . . restore_file . -d . one of three reported SQLi vulnerabilities in osCom- DIR_FS_BACKUP); 4 } merce, our prototype additionally reported a multi-step remote code execution exploit. The SQLi is located in Listing 12: A dynamically constructed system command the backup tool of the administrator interface and shown in osCommerce includes the name of an existing file. in Listing 10. Here, a SQL file is uploaded to restore a database backup. Since the name of the uploaded file An attacker can bypass this check by abusing one of is later used unsanitized in a SQL query, an attacker the file upload functionalities in osCommerce. By up- is able to insert any data into the configuration ta- loading a file with the name ;id;.zip and afterwards spec- ble by uploading a SQL file with a crafted name. This ifying this file as backup file, the command id is exe- enables another, more severe vulnerability: the table cuted. The semicolons within the file name terminate the configuration stores a configuration value and a previous unzip command and introduce a new command. configuration title for each setting. Furthermore, a use function can be specified optionally to deploy the 4.4 False Positives configuration’s value. 1 sql_file = new upload( sql_file ); Our prototype generated 43 false second-order vulnera- 2 read_from = sql_file->filename; bility reports, leading to a false discovery rate of 21% for 3 4 tep_db_query("insert into " . TABLE_CONFIGURATION . " values (null, Last Database Restore , DB_RESTORE , our selected applications. All false positives are based 5 " . read_from . " , Last database restore file , on the fact that our prototype is not able to detect path- 6 6 , 0 , null, now(), , )"); sensitive sanitization. Thus, persistent XSS was reported Listing 10: Simplified code of the backup.php file in in Scarf and HotCRP that are based on email addresses osCommerce shows a SQLi through a file name. stored in the database. Our prototype erroneously iden- tified these columns as taintable (see Section 4.1.1). The When the list of configuration values is loaded same error applies to a paper format in OpenConf which from the database, the function name specified leads to four false positives. A user-defined sanitiza- in the use function column is called with the tion function using path-sensitive sanitization based on configuration value as argument (see Listing 11, its argument lead to 29 false persistent XSS reports in os- line 5). An attacker can abuse the SQLi to insert Commerce. A false multi-step exploit was reported in an arbitrary PHP function’s name, such as system, to HotCRP caused by a false SQLi report. By performing a the column use function and insert an arbitrary argu- path-sensitive sanitization analysis, these false positives ment, such as id, to the column configuration value. can be addressed in the future. When loading the configuration list, the specified func- tion is fetched and called with the specified argument that executes the system command id. 4.5 False Negatives 1 conf_query = tep_db_query("select configuration_id, Evaluating false negatives is an error-prone task because configuration_title, configuration_value, use_function from " . TABLE_CONFIGURATION . " where the actual number of vulnerabilities is unknown. Further- configuration_group_id = " . (int) gID . " "); more, no CVE entries are public regarding second-order 2 while ( configuration = tep_db_fetch_array( conf_query)) { 3 if (tep_not_null( configuration[ use_function ])) { vulnerabilities in our selected applications. However, it 4 use_function = configuration[ use_function ]; is possible to test for false negatives that stem from in- 5 cfgValue = call_user_func( use_function, 6 configuration[ configuration_value ]); sufficient detection of taintable PDS. By pre-configuring our implementation with the taintable PDS we identi- Listing 11: Simplified code of the configuration.php file fied manually, we can compare the amount of detected in osCommerce demonstrates a multi-step RCE. second-order vulnerabilities with the number of reports when PDS are analyzed automatically. As a result, only six previously missed persistent XSS 4.3.2 Sanitization Bypass in osCommerce in osCommerce were reported. Additionally, another Another multi-step RCE exploit was reported in osCom- taintable session key in OpenConf was reported, al- merce that involves a sanitization bypass. The previously though the key does not lead to a vulnerability. Fur- mentioned backup tool of the administrator interface al- thermore, we manually inspected the source code of the lows to specify a local ZIP file that is unpacked via the applications and observed that our SQL parser needs im- system command unzip. Here, the target file name is provement. Three false negatives occurred in Scarf be- specified as an argument in the command line if the spec- cause our parser does not handle SQL string functions ified file name exists on the file system. The simplified such as concat(). More complex SQL instructions code is shown in Listing 12. might lead to further false negatives but are used rarely. 12 1000 23rd USENIX Security Symposium USENIX Association 4.6 Performance present a blackbox scanner capable of detecting persis- tent XSS [19]. Ardilla [14] aims at detecting both SQL We evaluated our prototype with the implementation injection and XSS vulnerabilities by generating sample of our approach to detect second-order vulnerabilities inputs, symbolically tracking taint information through (+SO) and without it (-SO). Our testing environment was execution (including through database accesses), and au- equipped with an Intel i7-2600 CPU with 3.4 GHz and tomatically generating concrete exploits. The typical 16 GB of memory. The amount of memory consump- drawbacks of such dynamic approaches are the limited tion (M, in megabytes), scan time (T, in seconds), and test coverage and the missing ability to crawl a given second-order vulnerability reports (R) for our selected site “deep” enough. This insight is confirmed by Doupé applications are given in Table 7. et al., who tested eleven black-box dynamic vulnerability scanners and found that whole classes of vulnerabilities Table 7: Performance results for selected applications. are not well-understood and cannot be detected by the state-of-the-art scanners [7]. -SO Analysis +SO Analysis Software M[mb] T[s] M[mb] T[s] R Static Analysis We perform static analysis of PHP osCommerce 834 134 846 213 129 code and use the concept of block summaries as proposed HotCRP 752 186 775 345 3 by Xie and Aiken [30] and later on refined by Dahse OpenConf 528 33 523 47 21 and Holz [6]. Our analysis tool extends these ideas and NewsPro 50 1 50 3 17 we improved the modeling of the language. More pre- Scarf 39 1 40 14 46 cisely, we introduce more data symbols (e.g., to analyze MyBloggie 87 7 87 11 1 array accesses in a more precise way) and enhance the Total 2290 362 2321 633 217 analysis of built-in functions such that we can perform Average 382 60 387 106 36 a taint analysis for persistent data stores. Furthermore, we optimized the inter-procedural analysis to refine our string analysis results. This enables us to analyze the two While the memory consumption does not increase sig- distinct data flows that lead to second-order vulnerabili- nificantly by adding second-order analysis, the average ties: (i) source to PDS and (ii) PDS to sink. As a result, scan time increases by 40%. Note, however, that this in- we are able to detect vulnerabilities missed by these ap- cludes 217 processed vulnerability reports the prototype proaches. Pixy [11] and Saner [2] are other static code would have missed without the additional second-order analysis tools for web applications, but both do not rec- analysis. Furthermore, we believe that a total scan time ognize second-order vulnerabilities. of less than 11 minutes for our selected applications is still reasonable. There are static analysis approaches that target other classes of security vulnerabilities. For example, Safer- PHP [25] attempts to find semantic attacks (e.g., denial 5 Related Work of service attacks due to infinite loops caused by mali- cious inputs, or unauthorized database operations due to Web applications are widely used in the modern Web and missing security checks) within web applications. Role- as a result, security analysis of such applications has at- Cast [24] identifies security-critical variables and ap- tracted a considerable amount of research. We now re- plies role-specific variable consistency analysis to iden- view related work in this area and discuss how our ap- tify missing security checks, while Phantm [17] detects proach differs from previous approaches. type errors in PHP code. Such kinds of software defects are out of scope for our analysis. Dynamic Analysis There are many different dynamic approaches to perform a security analysis of a given web Static Second-Order Analysis The work closest re- application. For example, Apollo [1] leverages symbolic lated to our approach is MiMoSA [3]. It is an extension and concrete execution techniques in combination with of Pixy [11] to detect multi-module data flow and work explicit-state model checking to perform persistent state flow vulnerabilities. The data flow through databases is analysis for session variables in PHP. Sekar proposes modeled, however, it uses a dynamic approach for the syntax- and taint-aware policies that can accurately de- reconstruction of SQL queries. Moreover, it focuses on tect and/or block most injection attacks [23]. However, the detection of the work flow of an application and does such approaches are typically limited to simple types of not handle neither other types of PDS nor multi-step ex- taint-style vulnerabilities. ploits. In comparison, only three data flow vulnerabilities There are also dynamic approaches to detect second- were detected in Scarf, whereas our approach detected 37 order vulnerabilities. For example, McAllister et al. second-order vulnerabilities and one multi-step exploit. 13 USENIX Association 23rd USENIX Security Symposium 1001 Zheng and Zhang proposed an approach to detect Conference on Computer and Communications Security (CCS) atomicity violations in web applications regarding exter- (2009). nal resources [31], which can be seen as being closely [6] DAHSE , J., AND H OLZ , T. Simulation of Built-in PHP Features related to second-order vulnerabilities since such con- for Precise Static Code Analysis. In Symposium on Network and currency errors are a pre-condition for second-order ex- Distributed System Security (NDSS) (2014). ploits. They perform a context- and path-sensitive inter- [7] D OUP É , A., C OVA , M., AND V IGNA , G. Why Johnny Can’t procedural static analysis to automatically detect atom- Pentest: An Analysis of Black-box Web Vulnerability Scanners. icity violations on shared external resources. The tools In Detection of Intrusions and Malware, and Vulnerability As- sessment (DIMVA) (2010). NewsPro and Scarf are included into their evaluation, but the authors did not find any of the second-order vulnera- [8] G UNDY, M. V., AND C HEN , H. Noncespaces: Using Random- bilities detected by our approach. As such, our approach ization to Enforce Information Flow Tracking and Thwart Cross- Site Scripting Attacks. In Symposium on Network and Distributed outperformed prior work on static detection of second- System Security (NDSS) (2009). order vulnerabilities. [9] H ALFOND , W. G., V IEGAS , J., AND O RSO , A. A Classification of SQL Injection Attacks and Countermeasures. In Proceedings 6 Conclusion and Future Work of the IEEE International Symposium on Secure Software Engi- neering (2006). In this paper, we demonstrated that it is possible to stat- [10] H ILLS , M., K LINT, P., V INJU , J., AND H ILLS , M. 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