April 13, 2015

Blind SQL Injection Testing and Efficient Fingerprinting

There are a number of "universal" methods that can be used to determine if a page is vulnerable to SQL injection; but once a vulnerability has been discovered, fingerprinting the database engine to determine the vendor (MySQL, PostgreSQL, Microsoft SQL Server, or others) can come with a bit of a headache and take more requests than desired. Some functionality and variables exist on multiple database engines (and in some cases all of them), while others are unique to specific services or service versions. This article will guide the reader through a couple of "universal" testing methods and eventually through efficient database fingerprinting.

Universal Injection Tests

Nearly everyone is familiar with using mathematical operators to test for SQL injection, but in some cases cross-site-scripting filters or other sanitizing may prevent mathematical characters (such as =,<, and >) from entering the vulnerable SQL query unscathed. There are two other "universal" comparison operators that can be used to confirm the presence of a vulnerability in an application: the "... between ... and ..." comparison operator, and the "is" operator.

Using the entry table from our last coverage on SQL injection, examples of the comparison operators referenced here have been provided in SQL statement examples in conjunction with a set of testing URLs that could produce similar results.

Testing with "... between ... and ..."

The "... between ... and ..." operator is simple. It tests to see if the first value is between the other two. It returns true if the value falls between the specified range, and false otherwise (queries in Figure 1-A).

Figure 1-A: Query examples using the "... between ... and ..." comparison operator
mysql> select * from entry where id=1 and 3 between 1 and 5;  # True
| id | name        |
|  1 | First entry |
1 row in set (0.01 sec)

mysql> select * from entry where id=1 and 7 between 1 and 5;  # False
Empty set (0.00 sec)

The first query displays the entry because 3 is between 1 and 5, but the second query returns an empty set because 7 is not between 1 and 5. When testing for remote injection vulnerabilities, test URL's may look something like the examples in Figure 1-B.

Figure 1-B: Integer testing examples using the "... between ... and ..." comparison operator
http://domain.tld/file.ext?id=1 and 3 between 1 and 5
http://domain.tld/file.ext?id=1 and 7 between 1 and 5

If the page at http://domain.tld/file.ext?id=1 were vulnerable to in-band or partially blind injection, the first example testing URL would return the same output as the unmodified URL, while the second example testing URL would cause data to be missing from the page.

In many cases, this operator also works on strings. Figure 1-C provides an example set of test URLs implementing a string test with this technique.

Figure 1-C: String testing examples using the "... between ... and ..." comparison operator
http://domain.tld/file.ext?title=Text' and 'b' between 'a' and 'c
http://domain.tld/file.ext?title=Text' and 'd' between 'a' and 'c

Testing with "is" and "is not"

The "is" operator is not a range or mathematical operator. It is also not to be confused with the equals (=) operator for equality, as the equals operator can be used on a variety of data types. The "is" operator works on fewer data types, but can also be used to determine where data has or hasn't been defined. SQL Servers have a way of representing it when columns have not been given a default value and a row in that column is undefined. This lack of value is referred to as null by every database engine, and every database engine allows the comparison operators of "is null" and "is not null". Example queries using this operator have been provided in Figure 2-A.

Figure 2-A: Query examples using the "is" and "is not" comparison operators
mysql> select * from entry where id=1 and null is null;      # True
| id | name        |
|  1 | First entry |
1 row in set (0.01 sec)

mysql> select * from entry where id=1 and null is not null;  # False
Empty set (0.00 sec)

Similarly to the first example, the first query returns data because null is always null. Null is also never not null, thusly the second query returns an empty set. A remote injection test using these operators may look something like Figure 2-B.

Figure 2-B: Testing examples using the "is" and "is not" comparison operators
http://domain.tld/file.ext?id=1 and null is null
http://domain.tld/file.ext?id=1 and null is not null

Remote Database Fingerprinting

By isolating the similarities and differences between different database engines, it is possible to use very few requests to determine which database server provides dynamic content support for the vulnerable application. While the techniques here do not reveal a specific version number, they do reveal the vendor of the server software. Knowing the vendor of the database server software is enough information to use vendor-specific techniques that reveal the rest of the version information.

There are enough similarities and differences between Microsoft SQL Server, PostgreSQL Server, and MySQL server to determine which of these (or if any of these) vendors provided the server software powering the application in only two requests. MySQL and PostgreSQL share a version() function that returns the server version information. Microsoft SQL Server has the @@version system variable in common with MySQL, but PostgreSQL does not. Knowing this, seeing the output of the two requests in Figure 3 from a vulnerable page gives us the ability to logically deduce which, if any, of the three vendors mentioned developed the software.

Figure 3: Example version fingerprinting URLs
http://domain.tld/file.ext?id=1 and version() is not null
http://domain.tld/file.ext?id=1 and @@version is not null

If the output of the page returns a "true" response for both requests, the vulnerable application is using a MySQL back-end. This can be deduced because MySQL is the only database server that has both the version() function and the @@version system variable. If only the test using the version() function returns true, the target application is running PostgreSQL. If only the test using the @@version system variable returns true, the vulnerability applies to a Microsoft SQL Server. If both queries return false, and characters are not being filtered, the target may be running Oracle Server, as Oracle does not support either the system variable or the version() function. Once the vendor of the database software has been determined, version information can be enumerated or selected using standard SQL injection techniques. Other efficient fingerprinting techniques can be found by inspecting reference manuals for nuances related to functions and environment variables with a little bit of testing.

April 11, 2015

Inspecting Remote Network Topography by Monitoring Response Time-To-Live

There are times when network administrators may configure a single host to forward various ports to other hosts at different tiers within their internal network. A single Internet Protocol (IP) address may actually represent as many machines on an internal network as it has open ports. While it is somewhat elementary to detect this, surprisingly popular tools have yet to incorporate information related to the discovery of tiered networks in their respective output. This post will guide the reader through the process of monitoring Time-To-Live (TTL) values during routine activities to acquire information about port forwarding and intrusion response surrounding remote hosts.

Understanding TTL Responses

Each time a host sends data to another, the data travels in a packet, from one host along a series of routers, switches, and other hosts, until reaching its destination. The TTL header exists within this packet and specifies how many hops the packet can travel before no longer being forwarded. Each "hop" the packet encounters decrements the TTL value in the header.

TTL's Can Reveal Route Changes

Route changes can occur semi-frequently across the internet as internet service providers tighten their relationships with Tier1 Backbone Providers. When these changes occur, every response from a host on the other side of a changed route may change TTL values at once. A route change may occur due to normal internet expansion, a network appliance restricting traffic from a particular source into a honeypot, or a port redirect for load-balancing purposes.

TTL's In Scan Responses

During a port scan, SYN packets are sent to the target host on a variety of ports in order to evoke a response, revealing which ports have services listening behind them. While all of the packets sent by the scanner (unless the scanner is told otherwise) will have the same TTL to start with, there are times when the IP address being scanned will actually have different TTL values in its responses. The differing TTL's can indicate additional "tiers" of networking behind the public IP address being scanned. The higher the TTL, the closer the host running the service is to the scanning host; the lower the TTL, the further the host is from the scanning host.

In other words, if the host has a baseline response TTL of 47, but a response comes in with a TTL of 45, the port the response came from may be visibly forwarded. On the other hand, if a packet is received with a TTL of 48 from the same host, it may have actually originated from a device between the scanning host and the target host.

TTL's Could Betray Firewalls and Intrusion Responses

Port scanning isn't the only time it can be advantageous to monitor for TTL anomalies. Firewalls and Intrusion Prevention Systems (IPS's) between an attacker and their target can also respond with RST packets to terminate connections which are considered harmful. Because these appliances sit between the attacker and the target, the RST packet received by the attacker will have a TTL that is higher in value than that of the packets received from the target host's normal service on that port.

ttl_mon.py: A Passive Python2 TTL Monitor

In order to monitor for TTL anomalies, the team put together ttl_mon.py on github. It runs until the user exits using control+C, and prints detailed information on a host-by-host basis about the hosts it encountered during runtime. When TTL values for individual ports change during runtime, a red notification about route modification is printed to the screen. It has the ability to monitor all hosts on a given interface or a single target host while ignoring locally emitted traffic.


ttl_mon.py depends on the dpkt and pcapy packages for python2. They can be installed using the python pip module like so:

python2 -mpip install pcapy dpkt


Usage: ttl_mon.py [options]

  -h, --help            show this help message and exit
  -i INTERFACE, --interface=INTERFACE
                        Interface to listen to
  -l LOCAL, --local=LOCAL
                        Local address to ignore
  -t TARGET, --target=TARGET
                        Only record changes for this ip