sqlmap internals

sqlmap internalssqlmap internals
Miroslav Stampar
(mstampar@zsis.hr; miroslav@sqlmap.org)
sqlmap internalssqlmap internals
Miroslav Stampar
(mstampar@zsis.hr; miroslav@sqlmap.org)

SecAdmin, Sevilla (Spain) November 24th, 2017 2
IntroductionIntroduction
Free and open source penetration testing tool
that automates the process of detecting and
exploiting SQL injection flaws and taking over
of database server(s)
Written in Python (2)
11 years old (July 25th
2006)
2 authors / core developers (Bernardo Damele
and Miroslav Stampar)
65K LoC (Lines of Code)
100% accuracy and 0% false-positives by
WAVSEP benchmark of 64 Web Application
Scanners (sectoolmarket.com)

CapabilitiesCapabilities
78 switches (e.g. --tor) and 91 options (e.g.
--url=”...”) in 15 categories (Target,
Request, Optimization, Injection, etc.)
Full coverage for (relational DBMS-es): MySQL,
Oracle, PostgreSQL, Microsoft SQL Server,
Microsoft Access, IBM DB2, SQLite, Firebird,
Sybase, SAP MaxDB, HSQLDB and Informix
Full support for SQLi techniques: boolean-
based blind, time-based blind, error-based,
UNION query-based and stacked queries
Database enumeration, file-system
manipulation, out-of-band communication, etc.

Sample runSample run

Socket pre-connect (1)Socket pre-connect (1)
TCP three-way handshake (SYN, SYN-ACK,
ACK) is inherently slow (“necessary evil”)
Each HTTP request requires a completed
TCP handshake procedure
sqlmap runs a “pre-connect” thread in
background filling a pool of (e.g. 3)
connections with TCP handshake done
Overrides Python’s socket.connect()
25% speed-up of a program’s run on
average

Socket pre-connect (2)Socket pre-connect (2)

NULL connection (1)NULL connection (1)
In boolean-based blind SQLi response sizes
should suffice (e.g. >1000 bytes → TRUE)
“NULL” naming because of skipping the
retrieval of complete HTTP response
Range: bytes=-1
Content-Range: bytes 4789-4789/4790
HEAD /search.aspx HTTP/1.1
Content-Length: 4790
Both are resulting (if applicable) with empty
HTTP body (faster retrieval of responses)
By looking into “length” headers we can
differentiate TRUE from FALSE answers

NULL connection (2)NULL connection (2)

HashDB (1)HashDB (1)
Storage of resumable session data at
centralized place (local SQLite3 database)
Non-ASCII values are being automatically
serialized/deserialized (pickle)
INSERT INTO storage VALUES
(INT(MD5(target_url, uid, MILESTONE_SALT)
[:8]), stored_value)
uid uniquely describes stored_value for a
given target_url (e.g.: KB_INJECTIONS, SELECT
VERSION(), etc.)
MILESTONE_SALT changed whenever there is an
incompatible update of HashDB mechanism

HashDB (2)HashDB (2)

BigArray (1)BigArray (1)
Support for huge table dumps (e.g. millions of
rows)
Raw data needs to be held somewhere before
being processed (and eventually stored)
In memory storage was a good enough choice
until user appetites went bigger (!)
Memory mapping into smaller chunks (1MB) –
memory pages
Temporary files store (compressed) chunks
In-memory caching of currently used chunk
O(1) read/write access

BigArray (2)BigArray (2)

Heuristics (1)Heuristics (1)
“Educational shortcuts to ease the cognitive
load of making a decision”
Resulting with a solution which is not
guaranteed to be optimal (though very helpful)
Type casting (e.g. ?id=1foobar)
DBMS error reporting (e.g. ?id=1())'”(”')
Character filtering (e.g. ?id=1 AND 7=(7))
Length constraining (e.g. id=1 AND 3182=
3182)
(quick) DBMS detection (e.g. ?id=1 AND
(SELECT 0x73716c)=0x73716c)

Heuristics (2)Heuristics (2)

Boolean inference (1)Boolean inference (1)
Binary search using greater-than operator
O(Log2n) complexity compared to sequential
search with O(n)
Faster than bit-by-bit extraction (on average 6
requests compared to 8 requests)
For example:
Sample initial table ['A','B',...'Z']
AND (...) > 'M' → TRUE → ['N',...'Z']
AND (...) > 'S' → FALSE → ['N',...'S']
AND (...) > 'O' → TRUE → ['P', 'R', 'S']
AND (...) > 'R' → FALSE → ['P', 'R']
AND (...) > 'P' → FALSE → 'P' (result)

Boolean inference (2)Boolean inference (2)

Boundaries / levels / risks (1)Boundaries / levels / risks (1)
SQLi detection requires working payload
(e.g. AND 1=1) together with proper
boundaries (e.g. ?query=test’ AND 1=1
AND ‘x’=’x)
Number of tested prefix/suffix boundaries is
constrained with option --level (e.g.
“)))))
Number of tested payloads is constrained
with option --risk (e.g. OR 1=1)
Greater the level and risk, greater the
number of testing cases

Boundaries / levels / risks (2)Boundaries / levels / risks (2)

Statistics (1)Statistics (1)
Network latency (or lagging) is the main
problem of time-based blind technique
For example, used deliberate delay is 1 sec,
normal response times are >0.5 and <2.0 secs,
what we can conclude for 1.5 sec response?
sqlmap learns what's normal and what's not
from non-delay based payload responses (e.g.
boolean-based blind payloads)
Normal distribution is being calculated
(Gaussian bell-shaped curve)
Everything inside is considered as “normal”,
outside as “not normal”

Statistics (2)Statistics (2)
Everything that's normal (i.e. not deliberately
delayed) should fit under the curve
μ(t) represents a mean, while σ(t) represents
a standard deviation of response times
99.99% of normal response times fall under the
upper border value μ(t) + 7σ(t)

False-positive detection (1)False-positive detection (1)
Detection of “error” in SQLi detection engine
Giving false sense of certainty while in reality
there is nothing exploitable at the other side
Almost exclusive to boolean-based blind and
time-based blind cases
Simple tests are being done after the detection
Comparing responses to boolean operations
with expected results (e.g. id=1 AND 95=27)
If any of results is contrary to the expected
value, SQLi is discarded as a false-positive (or
unexploitable)

False-positive detection (2)False-positive detection (2)

WAF/IDS/IPS detection (1)WAF/IDS/IPS detection (1)
Sending deliberately suspicious payloads and
checking response(s) for unique characteristics
(e.g.) ?id=1&bwXY=5253 AND 1=1 UNION ALL
SELECT 1,NULL,'<script>alert("XSS")
</script>',table_name FROM
information_schema.tables WHERE
2>1--/**/; EXEC xp_cmdshell('cat ../../
../etc/passwd')#
ModSecurity returns HTTP error code 501 on
detected attack, F5 BIG-IP adds its own X-
Cnection HTTP header, etc.
Fingeprinting 59 different WAF/IDS/IPS products

WAF/IDS/IPS detection (2)WAF/IDS/IPS detection (2)

Tamper scripts (1)Tamper scripts (1)
Auxiliary python scripts modifying the payload
before being sent (e.g. ?id=1 AND 2>1 to
?id=1 AND 2 NOT BETWEEN 0 AND 1)
Currently 54 tamper scripts (between.py,
space2randomblank.py, versionedkeywords.py,
etc.)
User has to choose appropriate one(s) based
on collected knowledge of target's behavior
and/or detected WAF/IDS/IPS product
Chain of tamper scripts (if required) can be
used (e.g. --tamper=”between,
ifnull2ifisnull”)

Tamper scripts (2)Tamper scripts (2)

Brute-forcing identifiers (1)Brute-forcing identifiers (1)
In some cases system tables are unreadable
(e.g. because of lack of permissions)
Hence, no way to retrieve identifier names
(tables and columns)
sqlmap does guessing by brute-forcing
availability of most common identifiers (e.g.
?id=1 AND EXISTS(SELECT 123 FROM users))
Identifiers (3369 table and 2601 column
names) have been collected and frequency-
sorted by retrieving and parsing thousands
of online SQL scripts

Brute-forcing identifiers (2)Brute-forcing identifiers (2)

Hash cracking (1)Hash cracking (1)
Automatic recognition and dictionary
cracking of 30 different hash algorithms
(e.g. mysql, mssql, md5_generic,
sha1_generic, etc.)
Included dictionary with 1.4 million wordlist
entries (RockYou, MySpace, Gawker, etc.)
Multiprocessing (# of cores)
Blazing fast (e.g. under 10 seconds for
whole dictionary pass with mysql routine)
Stores uncracked hashes to file for eventual
further processing (with other tools)

Hash cracking (2)Hash cracking (2)

Stagers / backdoors (1)Stagers / backdoors (1)
Stager uploaded in a first (dirty) stage (e.g.
possibility of a query junk in case of INTO
OUTFILE method)
Stager has a functionality of uploading
arbitrary files
Backdoor (or any binary) uploaded in second
(clean) stage by using stager
Backdoor has a functionality of executing
arbitrary OS commands
Supported platforms: PHP, ASP, ASPX, JSP

Stagers / backdoors (2)Stagers / backdoors (2)

DNS exfiltration (1)DNS exfiltration (1)
In some cases it's possible to incorporate
SQL (sub)query results into DNS resolution
requests
Microsoft SQL Server, Oracle, MySQL and
PostgreSQL
Dozens of resulting characters can be
transferred per single request (compared to
boolean-based blind and time-based blind)
Domain name server entry (e.g.
ns1.attacker.com) has to point to IP
address of machine running sqlmap

Questions?Questions?

sqlmap internals

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