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Eliminating Web Software Vulnerabilities with Automated Verification
Tevfik BultanVerification Lab
Department of Computer ScienceUniversity of California, Santa Barbara
[email protected] http://www.cs.ucsb.edu/~vlab
University of California at Santa Barbara
Verification Lab (VLab) at UCSB
• Application of automated verification techniques to software – Automated verification of web applications
• Checking input validation, sanitization, string analysis (PHP)• Checking navigation correctness (MVC farmeworks)• Checking data models (Ruby on Rails)
– Automated verification of web services• Modular testing and verification of web services (WSDL)• Formal modeling and analysis of choreography and
orchestration (WS-CDL, WS-BPEL)– Automated verification of access control policies
• Policy composition (XACML)– Automated verification of concurrency
• Analyzing concurrency, deadlock detection (Java)
VLab Publications on String Analysis
• Relational String Verification Using Multi-Track Automata [Yu, Bultan, Ibarra CIAA’10]
• Stranger: An Automata-based String Analysis Tool for PHP [Yu, Alkhalaf, Bultan TACAS’10]
• Generating Vulnerability Signatures for String Manipulating Programs Using Automata-based Forward and Backward Symbolic Analyses [Yu, Alkhalaf, Bultan ASE’09]
• Symbolic String Verification: Combining String Analysis and Size Analysis [Yu, Bultan, Ibarra TACAS’09]
• Symbolic String Verification: An Automata-based Approach [Yu, Bultan, Cova, Ibarra SPIN’08]
Web software
• Web software is becoming increasingly dominant• Web applications are used extensively in many areas:
– Commerce: online banking, online shopping, …– Entertainment: online music & videos, …– Interaction: social networks
• We will rely on web applications more in the future:– Health records
• Google Health, Microsoft HealthVault– Controlling and monitoring of national infrastructures:
• Google Powermeter• Web software is also rapidly replacing desktop applications
– Could computing + software-as-service• Google Docs, Google …
One Major Road Block
• Web applications are not trustworthy!
• Web applications are notorious for security vulnerabilities – Their global accessibility makes them a target for many malicious
users
• As web applications are becoming increasingly dominant – and as their use in safety critical areas is increasing
• their trustworthiness is becoming a critical issue
Web applications are not secure
• There are many well-known security vulnerabilities that exist in many web applications. Here are some examples:– Malicious file execution: where a malicious user causes the
server to execute malicious code– SQL injection: where a malicious user executes SQL commands
on the back-end database by providing specially formatted input– Cross site scripting (XSS): causes the attacker to execute a
malicious script at a user’s browser
• These vulnerabilities are typically due to – errors in user input validation or – lack of user input validation
Web Application Vulnerabilities
Web Application Vulnerabilities
• The top two vulnerabilities of the Open Web Application Security Project (OWASP)’s top ten list in 2007– Cross Site Scripting (XSS)– Injection Flaws (such as SQL Injection)
• The top two vulnerabilities of the OWASPs top ten list in 2010– Injection Flaws (such as SQL Injection)– Cross Site Scripting (XSS)
Why are web applications error prone?
• Extensive string manipulation:– Web applications use extensive string manipulation
• To construct html pages, to construct database queries in SQL, etc.
– The user input comes in string form and must be validated and sanitized before it can be used
• This requires the use of complex string manipulation functions such as string-replace
– String manipulation is error prone
String Related Vulnerabilities String related web application vulnerabilities occur when:
a sensitive function is passed a malicious string input from the user
This input contains an attack It is not properly sanitized before it reaches the sensitive function
String analysis: Discover these vulnerabilities automatically
XSS Vulnerability A PHP Example:
1:<?php2: $www = $_GET[”www”];3: $l_otherinfo = ”URL”;4: echo ”<td>” . $l_otherinfo . ”: ” . $www . ”</td>”;5:?>
The echo statement in line 4 is a sensitive function It contains a Cross Site Scripting (XSS) vulnerability
<script ...
Is it Vulnerable? A simple taint analysis, e.g., [Huang et al. WWW04], can report this
segment vulnerable using taint propagation
1:<?php2: $www = $_GET[”www”];3: $l_otherinfo = ”URL”;4: echo ”<td>” . $l_otherinfo . ”: ” .$www. ”</td>”;5:?>
echo is tainted → script is vulnerable
tainted
How to Fix it? To fix the vulnerability we added a sanitization routine at line s Taint analysis will assume that $www is untainted and report that the
segment is NOT vulnerable
1:<?php2: $www = $_GET[”www”];3: $l_otherinfo = ”URL”;s: $www = preg_replace(”[^A-Za-z0-9 .-@://]”,””,$www);4: echo ”<td>” . $l_otherinfo . ”: ” .$www. ”</td>”;5:?>
tainted
untainted
Is It Really Sanitized?
1:<?php2: $www = $_GET[”www”];3: $l_otherinfo = ”URL”;s: $www = preg_replace(”[^A-Za-z0-9 .-@://]”,””,$www);4: echo ”<td>” . $l_otherinfo . ”: ” .$www. ”</td>”;5:?>
<script ...
Sanitization Routines can be Erroneous The sanitization statement is not correct!
preg_replace(”[^A-Za-z0-9 .-@://]”,””,$www); – Removes all characters that are not in {A-Za-z0-9 .-@:/}– “.-@” denotes all characters between “.” and “@” (including
“<” and “>”)– “.-@” should have been “.\-@”
This example is from a buggy sanitization routine used in MyEasyMarket-4.1 (line 218 in file trans.php)
String Analysis String analysis determines all possible values that a string expression
can take during any program execution
Using string analysis we can identify all possible input values of the sensitive functions
Then we can check if inputs of sensitive functions can contain attack strings
How can we characterize attack strings? Use regular expressions to specify the attack patterns
Attack pattern for XSS: Σ <scriptΣ∗ ∗
String Analysis If string analysis determines that the intersection of the attack pattern
and possible inputs of the sensitive function is empty then we can conclude that the program is secure
If the intersection is not empty, then we can again use string analysis to generate a vulnerability signature
characterizes all malicious inputs
Given Σ <scriptΣ∗ ∗ as an attack pattern: The vulnerability signature for $_GET[”www”] is
Σ <α sα cα rα iα pα tΣ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗where α∉ {A-Za-z0-9 .-@:/}
Vulnerabilities can be tricky
• Input <!sc+rip!t ...> does not match the attack pattern – but it matches the vulnerability signature – and it can cause an attack
• 1:<?php• 2: $www = <!sc+rip!t ...>;• 3: $l otherinfo = ”URL”;• s: $www = preg replace(”[^A-Za-z0-9 .-@://]”,””, <!sc+rip!t...>);• 4: echo ”<td>” . $l otherinfo . ”: ” . <script ...> .”</td>”;• 5:?>
Automata-based String Analysis
• Finite State Automata can be used to characterize sets of string values
• We use automata based string analysis– Associate each string expression in the program with an automaton– The automaton accepts an over approximation of all possible
values that the string expression can take during program execution
• Using this automata representation we symbolically execute the program, only paying attention to string manipulation operations
Symbolic Automata Representation
• We use the MONA DFA Package for automata manipulation – [Klarlund and Møller, 2001]
• Compact Representation:– The transition relation of the DFA is represented as a multi-terminal
BDD (MBDD)
• Exploits the MBDD structure in the implementation of DFA operations– Union, Intersection, and Emptiness Checking– Projection and Minimization
• Cannot Handle Nondeterminism:– We extended the alphabet with dummy bits to encode
nondeterminism
Symbolic Automata RepresentationExplicit DFArepresentation
Symbolic DFArepresentation
String Analysis Stages Construct dependency graphs for the PHP programs Combine symbolic forward and backward symbolic reachability
analyses Forward analysis
Assume that the user input can be any string Propagate this information on the dependency graph When a sensitive function is reached, intersect with attack pattern
Backward analysis If the intersection is not empty, propagate the result backwards to
identify which inputs can cause an attack
Front Front EndEnd
ForwardForwardAnalysisAnalysis
BackwardBackwardAnalysisAnalysis
Attackpatterns
PHPProgram
VulnerabilitySignatures
Dependency Graphs
Given a PHP program,
first construct the:
Dependency graph
1:<?php2: $www = $ GET[”www”];3: $l_otherinfo = ”URL”;4: $www = preg_replace(
”[^A-Za-z0-9 .-@://]”,””,$www);
5: echo $l_otherinfo . ”: ” .$www;
6:?>echo, 5
str_concat, 5
$www, 4
“”, 4
preg_replace, 4
[^A-Za-z0-9 .-@://], 4 $www, 2
$_GET[www], 2
“: “, 5$l_otherinfo, 3
“URL”, 3
str_concat, 5
Dependency Graph
Forward Analysis
• Using the dependency graph we conduct vulnerability analysis • Automata-based forward symbolic analysis that identifies the possible
values of each node– Each node in the dependency graph is associated with a DFA
• DFA accepts an over-approximation of the strings values that the string expression represented by that node can take at runtime
• The DFAs for the input nodes accept Σ∗– Intersecting the DFA for the sink nodes with the DFA for the attack
pattern identifies the vulnerabilities• Uses post-image computations of string operations:
– postConcat(M1, M2)
returns M, where M=M1.M2– postReplace(M1, M2, M3) (language-based replacement)
returns M, where M=replace(M1, M2, M3)
Forward Analysis
echo, 5
str_concat, 5
$www, 4
“”, 4
preg_replace, 4
[^A-Za-z0-9 .-@://], 4 $www, 2
$_GET[www], 2
“: “, 5$l_otherinfo, 3
“URL”, 3
str_concat, 5
Forward = URL: [A-Za-z0-9 .-@/]*
Forward = URL: [A-Za-z0-9 .-@/]*
Forward = [A-Za-z0-9 .-@/]*
Forward = [A-Za-z0-9 .-@/]*
Forward = Σ*Forward = εForward = [^A-Za-z0-9 .-@/]
Forward = Σ*
Forward = :
Forward = URL
Forward = URL
Forward = URL:
L(URL: [A-Za-z0-9 .-;=-@/]*<[A-Za-z0-9 .-@/]*)
Attack Pattern = Σ*<Σ*
∩
≠ Ø
L(URL: [A-Za-z0-9 .-@/]*) =L(Σ*<Σ*)
Intersection Result Automaton
U
R
L
:
Space
<
[A-Za-z0-9 .-;=-@/]
URL: [A-Za-z0-9 .-;=-@/]*<[A-Za-z0-9 .-@/]*
[A-Za-z0-9 .-@/]
Widening
• String verification problem is undecidable in general– Two string variables, equality checks and concatenation is enough
to get to undecidability
• The forward fixpoint computation is not guaranteed to converge in the presence of loops and recursion
• We compute a sound approximation– During fixpoint we compute an over approximation of the least
fixpoint that corresponds to the reachable states
• We use an automata based widening operation to over-approximate the fixpoint– Widening operation over-approximates the union operation and
accelerates the convergence of the fixpoint computation
Backward Analysis
• A vulnerability signature is a characterization of all malicious inputs that can be used to generate attack strings
• We identify vulnerability signatures using an automata-based backward symbolic analysis starting from the sink node
• Uses pre-image computations on string operations:– preConcatPrefix(M, M2)
returns M1 and where M = M1.M2– preConcatSuffix(M, M1)
returns M2, where M = M1.M2– preReplace(M, M1, M2)
returns M3, where M=replace(M1, M2, M3)
Backward Analysis
echo, 5
str_concat, 5
$www, 4
“”, 4
preg_replace, 4
[^A-Za-z0-9 .-@://], 4 $www, 2
$_GET[www], 2
“: “, 5
$l_otherinfo, 3
“URL”, 3
str_concat, 5
Forward = URL: [A-Za-z0-9 .-@/]*
Backward = URL: [A-Za-z0-9 .-;=-@/]*<[A-Za-z0-9 .-@/]*
Forward = URL: [A-Za-z0-9 .-@/]*
Backward = URL: [A-Za-z0-9 .-;=-@/]*<[A-Za-z0-9 .-@/]*
Forward = [A-Za-z0-9 .-@/]*
Backward =[A-Za-z0-9 .-;=-@/]*<[A-Za-z0-9 .-@/]*
Forward = [A-Za-z0-9 .-@/]*
Backward = [A-Za-z0-9 .-;=-@/]*<[A-Za-z0-9 .-@/]*
Forward = Σ*
Backward = [^<]*<Σ*
Forward = ε
Backward = Do not care
Forward = [^A-Za-z0-9 .-@/]
Backward = Do not care
Forward = Σ*
Backward = [^<]*<Σ*
Forward = :
Backward = Do not care
Forward = URL
Backward = Do not care
Forward = URL
Backward = Do not care
Forward = URL:
Backward = Do not care
node 3 node 6
node 10
node 11
node 12
Vulnerability Signature = [^<]*<Σ*Vulnerability Signature = [^<]*<Σ*
Vulnerability Signature Automaton
[^<]*<Σ*
<[^<]
Σ
NotASCII
Vulnerability Signatures
• The vulnerability signature is the result of the input node, which includes all possible malicious inputs
• An input that does not match this signature cannot exploit the vulnerability
• After generating the vulnerability signature– Can we generate a patch based on the vulnerability signature?
The vulnerability signature automaton for the running example
[^<]
<
Σ
Patches from Vulnerability Signatures
• Main idea: – Given a vulnerability signature automaton, find a cut that separates
initial and accepting states– Remove the characters in the cut from the user input to sanitize
• This means, that if we just delete “<“ from the user input, then the vulnerability can be removed
[^<]
<
Σ
min-cut is {<}
Patches from Vulnerability Signatures
• Ideally, we want to modify the input (as little as possible) so that it does not match the vulnerability signature
• Given a DFA, an alphabet cut is – a set of characters that after ”removing” the edges that are
associated with the characters in the set, the modified DFA does not accept any non-empty string
• Finding a minimal alphabet cut of a DFA is an NP-hard problem (one can reduce the vertex cover problem to this problem)– We use a min-cut algorithm instead – The set of characters that are associated with the edges of the min
cut is an alphabet cut • but not necessarily the minimum alphabet cut
Automatically Generated Patch
Automatically generated patch will make sure that no string that matches the attack pattern reaches the sensitive function
<?php
if (preg match(’/[^ <]*<.*/’,$ GET[”www”]))
$ GET[”www”] = preg replace(<,””,$ GET[”www”]); $www = $_GET[”www”]; $l_otherinfo = ”URL”; $www = preg_replace(”[^A-Za-z0-9 .-@://]”,””,$www); echo ”<td>” . $l_otherinfo . ”: ” .$www. ”</td>”;?>
Experiments
• We evaluated our approach on five vulnerabilities from three open source web applications:
(1) MyEasyMarket-4.1: A shopping cart program
(2) BloggIT-1.0: A blog engine
(3) proManager-0.72: A project management system
• We used the following XSS attack pattern:
Σ∗<scriptΣ∗
Forward Analysis Results
• The dependency graphs of these benchmarks are reduced based on the sinks– Unrelated parts are removed
Input Results
#nodes #edges #sinks #inputs Time(s) Mem (kb) #states/#bdds
21 20 1 1 0.08 2599 23/219
29 29 1 1 0.53 13633 48/495
25 25 1 2 0.12 1955 125/1200
23 22 1 1 0.12 4022 133/1222
25 25 1 1 0.12 3387 125/1200
Backward Analysis Results
• We use the backward analysis to generate the vulnerability signatures– Backward analysis starts from the vulnerable sinks identified during
forward analysis
Input Results
#nodes #edges #sinks #inputs Time(s) Mem (kb) #states/#bdds
21 20 1 1 0.46 2963 9/199
29 29 1 1 41.03 1859767 811/8389
25 25 1 2 2.35 5673 20/302,20/302
23 22 1 1 2.33 32035 91/1127
25 25 1 1 5.02 14958 20/302
Alphabet Cuts
• We generate alphabet cuts from the vulnerability signatures using a min-cut algorithm
• Problem: When there are two user inputs the patch will block everything and delete everything– Cannot interpret the relations among input variables (e.g. only block
when the concatenation of two inputs contains <script)
Input Results
#nodes #edges #sinks #inputs AlphabetCut
21 20 1 1 {<}
29 29 1 1 {s,’,”}
25 25 1 2 Σ , Σ
23 22 1 1 {<,’,”}
25 25 1 1 {<,’,”}
Vulnerabilitysignaturedepends ontwo inputs
Relational String Analysis
• Instead of using multiple single-track DFAs use one multi-track DFA– Each track represents the values of one string variable
• Using multi-track DFAs:– Identifies the relations among string variables– Generates relational vulnerability signatures for multiple user inputs
of a vulnerable application– Improves the precision of string analysis– Proves properties that depend on relations among string variables,
e.g., $file = $usr.txt
Multi-track Automata
• Let X (the first track), Y (the second track), be two string variables• λ is a padding symbol• A multi-track automaton that encodes X = Y.txt
(a,a), (b,b) …
(λ,t) (λ,x) (λ,t)
Relational Vulnerability Signature
• Performs forward analysis using multi-track automata to generate relational vulnerability signatures
• Each track represents one user input– An auxiliary track represents the values of the current node– Intersects the auxiliary track with the attack pattern upon
termination
Relational Vulnerability Signature
• Consider a simple example having multiple user inputs
<?php
1: $www = $_GET[”www”];
2: $url = $_GET[”url”];
3: echo $url. $www;
?>
• Let the attack pattern be Σ < Σ ∗ ∗
Relational Vulnerability Signature
• A multi-track automaton: ($url, $www, aux)• Identifies the fact that the concatenation of two inputs contains <
(a,λ,a),(b,λ,b),… (<,λ,<)
(λ,a,a),(λ,b,b),…
(λ,a,a),(λ,b,b),…
(λ,<,<)
(λ,<,<)
(λ,a,a),(λ,b,b),…
(λ,a,a),(λ,b,b),…
(a,λ,a),(b,λ,b),…
Relational Vulnerability Signature
• Project away the auxiliary variable• Find the min-cut• This min-cut identifies the alphabet cuts {<} for the first track ($url) and
{<} for the second track ($www)
(a,λ),(b,λ),… (<,λ)
(λ,a),(λ,b),…
(λ,a),(λ,b),…
(λ,<)
(λ,<)
(λ,a),(λ,b),…
(λ,a),(λ,b),…
(a,λ),(b,λ),…
min-cut is {<},{<}
Patch for Multiple Inputs
• Patch: If the inputs match the signature, delete its alphabet cut
<?php
if (preg match(’/[^ <]*<.*/’, $ GET[”url”].$ GET[”www”]))
{
$ GET[”url”] = preg replace(<,””,$ GET[”url”]);
$ GET[”www”] = preg replace(<,””,$ GET[”www”]);
}
1: $www = $ GET[”www”];
2: $url = $ GET[”url”];
3: echo $url. $www;
?>
Technical Issues
• To conduct relational string analysis, we need to compute ”intersection” of multi-track automata– Intersection is closed under aligned multi-track automata
• λs are right justified in all tracks, e.g., abλλ instead of aλbλ– However, there exist unaligned multi-track automata that are not
describable by aligned ones– We proposed an alignment algorithm that constructs aligned
automata which over or under approximate unaligned ones
Other Technical Issues
• Modeling Word Equations:– Intractability of X = cZ
• The number of states of the corresponding aligned multi-track DFA is exponential to the length of c.
– Irregularity of X = YZ • X = YZ is not describable by an aligned multi-track automata
• We proposed a conservative analysis– Constructs multi-track automata that over or under-approximate the
word equations
Composite Analysis
• What I have talked about so far focuses only on string contents– It does not handle constraints on string lengths– It cannot handle comparisons among integer variables and string
lengths
• We extended our string analysis techniques to analyze systems that have unbounded string and integer variables
• We proposed a composite static analysis approach that combines string analysis and size analysis
Size Analysis
• Size Analysis: The goal of size analysis is to provide properties about string lengths– It can be used to discover buffer overflow vulnerabilities
• Integer Analysis: At each program point, statically compute the possible states of the values of all integer variables.– These infinite states are symbolically over-approximated as linear
arithmetic constraints that can be represented as an arithmetic automaton
• Integer analysis can be used to perform size analysis by representing lengths of string variables as integer variables.
An Example
• Consider the following segment:
1: <?php
2: $www = $ GET[”www”];
3: $l otherinfo = ”URL”;
4: $www = preg replace(”[^A-Za-z0-9 ./-@://]”,””,$www);
5: if(strlen($www) < $limit)
6: echo ”<td>” . $l otherinfo . ”: ” . $www . ”</td>”;
7:?>
• If we perform size analysis solely, after line 4, we do not know the length of $www
• If we perform string analysis solely, at line 5, we cannot check/enforce the branch condition.
Composite Analysis
• We need a composite analysis that combines string analysis with size analysis.– Challenge: How to transfer information between string automata
and arithmetic automata?
• A string automaton is a single-track DFA that accepts a regular language, whose length forms a semi-linear set– For example: {4, 6} {2 + 3k | k ≥ 0}∪
• The unary encoding of a semi-linear set is uniquely identified by a unary automaton
• The unary automaton can be constructed by replacing the alphabet of a string automaton with a unary alphabet
Arithmetic Automata
• An arithmetic automaton is a multi-track DFA, where each track represents the value of one variable over a binary alphabet
• If the language of an arithmetic automaton satisfies a Presburger formula, the value of each variable forms a semi-linear set
• The semi-linear set is accepted by the binary automaton that projects away all other tracks from the arithmetic automaton
Connecting the Dots
• We developed novel algorithms to convert unary automata to binary automata and vice versa
• Using these conversion algorithms we can conduct a composite analysis that subsumes size analysis and string analysis
StringAutomata
Unary LengthAutomata
Binary LengthAutomata
ArithmeticAutomata
Stranger: A String Analysis Tool
– Uses Pixy [Jovanovic et al., 2006] as a PHP front end– Uses MONA [Klarlund and Møller, 2001] automata package for
automata manipulation
ParserParser
DependencyDependencyAnalyzerAnalyzer
StringString AnalyzerAnalyzer
MONA Automata MONA Automata PackagePackage
Automata BasedAutomata BasedString ManipulationString Manipulation
LibraryLibrary
CFG
DependencyGraphs
Symbolic String AnalysisSymbolic String Analysis
DFAs
Pixy Front EndPixy Front EndString/Automata
Operations
Stranger Automata
String Analysis Report
(Vulnerability Signatures)
PHP program
Attackpatterns
Stranger is available at:www.cs.ucsb.edu/~vlab/stranger
Case Study Schoolmate 1.5.4
Number of PHP files: 63 Lines of code: 8181
Forward Analysis results
After manual inspection we found the following:
Actual Vulnerabilities False Positives
105 48
Time Memory Number of XSS sensitive sinks
Number of XSS Vulnerabilities
22 minutes 281 MB 898 153
Case Study – False Positives Why false positives?
– Path insensitivity: 39 Path to vulnerable program point is not feasible
– Un-modeled built in PHP functions : 6– Unfound user written functions: 3
– PHP programs have more than one execution entry point
– We can remove all these false positives by extending our analysis to a path sensitive analysis and modeling more PHP functions
Case Study - Sanitization We patched all actual vulnerabilities by adding sanitization routines
We ran stranger the second time– Stranger proved that our patches are correct with respect to the
attack pattern we are using
Related Work: String Analysis
• String analysis based on context free grammars: [Christensen et al., SAS’03] [Minamide, WWW’05]
• Application of string analysis to web applications: [Wassermann and Su, PLDI’07, ICSE’08] [Halfond and Orso, ASE’05, ICSE’06]
• Automata based string analysis: [Xiang et al., COMPSAC’07] [Shannon et al., MUTATION’07]
• String analysis based on symbolic/concolic execution: [Bjorner et al., TACAS’09]
• Bounded string analysis : [Kiezun et al., ISSTA’09]
Related Work
• Size Analysis– Size analysis: [Hughes et al., POPL’96] [Chin et al., ICSE’05] [Yu et
al., FSE’07] [Yang et al., CAV’08]– Composite analysis: [Bultan et al., TOSEM’00] [Xu et al., ISSTA’08]
[Gulwani et al., POPL’08] [Halbwachs et al., PLDI’08]
• Vulnerability Signature Generation– Test input/Attack generation: [Wassermann et al., ISSTA’08]
[Kiezun et al., ICSE’09]– Vulnerability signature generation: [Brumley et al., S&P’06]
[Brumley et al., CSF’07] [Costa et al., SOSP’07]
THE END
