The document discusses techniques for rapidly testing web applications through automation to find security vulnerabilities within a limited time frame (T) and network requests (Q). It proposes prioritizing testing based on features like platform, number of inputs, and response status. Algorithmic approaches are suggested like using polyglot payloads to check for multiple issues simultaneously, building a decision tree to classify hackability, and calculating page priorities to guide the scan. Whitebox testing techniques like custom grep scripts to find code vulnerabilities are also covered. The goal is to build an efficient automated web application scanner that traverses the "pwning paths graph" to find bugs within the constraints.

1. Bugs -> max
Time <= T
Omar Ganiev
11/10/2015

2. Hi!
• I’m Beched
• I do application security assessment and penetration
testing at IncSecurity
• Also I compete as RDot.Org independent team
member

3. Contents
• Intro
• Technical view
• Algorithmic view
• Whitebox
• Outro

4. The problem
Common situations where the problem of rapid testing of web
application arises:
• Pentesting a huge scope full of web apps. You have a
couple of weeks to analyse and pwn them
• The similar case – bug bounty. You want to collect (low)
hanging fruits faster than others
• Customer asks about the costs of your work. You want to
estimate the cost by looking at web app for 5 minutes
• Competition (Capture The Flag). You want to pwn the
tasks quickly to focus on others and to get extra points

5. The solution
• Prioritizing
• Parallelizing
• Automation
• Guessing
• Heuristics
• ???
• PROFIT
• That’s it?

6. Manual testing
• Tons of articles and books are written about
testing methodology (including OWASP Testing
Guide)
• Manual testing includes application logic
analysis along with fuzzing
• Manual testing is more careful (no or 1=1 in
DROP-queries, etc)

7. Manual testing
• You can capture low hanging fruits in <= T time
manually, but not in N applications
• Generally automated scanning surely sucks
• But anyway we’ll focus on improving tools
rather than hands =)

8. Semi-automated testing
• NMap, Burp Suite, Recon-NG, CMSMap, RAFT,
etc…
• The tools are cool and save time, but still, you
need to do a lot by hands, and the
combination of such tools is poorly scalable

9. Automated testing
• Most of pentesters write their own specialized
tools for automated pentesting
• Generally It is rather complex task with a bit of
rocket science
• There’re a lot of problems like rich application
crawling or natural language processing (your
program actually needs to read human
language to understand the application)

10. Automated testing
• There’re two main variables for measuring complexity
(speed) of the testing methodology: time (depends on
CPU & memory usage) and number of network
requests
• They correlate, and time can be decreased by technical
measures
• This is coverage vs requests count trade-of
• Bugs -> max; time <= T; requests <= Q
• We’ll mainly focus on the second parameter

11. Automated testing
• Let’s take a look at some tips’n’tricks useful for
pentesting toolkit
• We’ll observe technical and algorithmic ways
to decrease testing time and number of
network requests

12. Contents
• Intro
• Technical view
• Algorithmic view
• Whitebox
• Outro

13. Technical view
• Well-known things first
• HTTP speed-up: Keep-Alive & HEAD
• HEAD method can be used for directory listing and
any other checks, which only need response
headers (length- or time-based payloads,
response splitting, etc)
• Keep-Alive is always useful, decreases number of
connections and hanshakes and hence the server
load

14. Technical view
• Trivial paths first
• Why crawling the whole site, if there’re
sitemap.xml, robots.txt and Google dorks?
• Why scanning the whole site, when you can
detect a CMS and version and check for vulns
in database?
• Why fuzzing a login form a lot, when you can
hit top passwords?

15. Technical view
• Scaling
• Threading and horizontal scaling increase the
speed very much, hence they can provide
better covering (if we limit time, but not
requests)
• Recent example of distributed scanning
platform is https://github.com/yahoo/gryffin/
written in Go

16. Contents
• Intro
• Technical view
• Algorithmic view
• Whitebox
• Outro

17. Algorithmic view
• Algorithmic view is quite interesting. How can
we increase the number of fuzzed points and
checked vulnerabilities without increasing
requests count?
• Let’s remember the problems we face while
conducting (semi-)manual testing

18. Algorithmic view
• Ever seen such?
• How do you process it
manually?
• URL patterns, similar
pages

19. Algorithmic view
• Already mentiond Gryffin project by Yahoo uses quite a
handy algorithm – Simhash
• Take a look:
http://www.wwwconference.org/www2007/papers/pape
r215.pdf
• If we build a Simhash-index of pages, we can skip
duplicates, saving a lot of time
• Possibly it’s better to take into account not only response
body, but response status, headers, parameters, etc

20. Algorithmic view
• How to gather input points (GET, POST, Cookie,
headers, …)?
• Classical way: automate browser (PhantomJS)
and crawl the website, process each request
• Quick way:
• Parse forms, parse links with query strings,
parse XHR parameters from JS

21. Algorithmic view
• How to gather unknown input points?
• Brute force
• Quick: Iterative binary search
• Collect a list of common parameter names, hit
them all in query string at once and check
page for changes, then perform dichotomy

22. Algorithmic view
• How to fuzz input points?
• Long way: take a big list of fuzzing strings and
fuzz each parameter
• Quick way: construct polyglot payloads and
check for a bunch of vulns at once
• Take a look:
http://www.slideshare.net/MathiasKarlsson2/p
olyglot-payloads-in-practice-by-avlidienbrun
n-at-hackpra

23. Algorithmic view
• Polyglot payloads can be constructed because of
ignored contexts (such as comments) in diferent
languages
• Example of polyglot string: <tagxss>
%0dresponse:splitting'"attributexss
• Null-byte or backslash should be placed last
• Time-based for (Postgre|My)SQL-injection: '/*!
+sleep(10)*/+n1#(select 2 from (select
pg_sleep(10))x)n+'

24. Algorithmic view
• Ok, what do we actually do, when we look at
web app by eyes?
• We estimate “hackableness” of app or page
and then think how can we hack it
• Why not automate thinking? %)

25. Algorithmic view
• The thinking flow is like this:
“Hm… It’s enterprise .NET site with a single login form.
Probably not that hackable
…
Hm… It’s default WordPress installation without plugins and
custom themes. Probably not hackable
…
Hm… It’s shitty custom PHP engine with a lot of forms and
input parameters. Instantly hackable! 8)
“

26. Algorithmic view
• What makes us think one or another way? Let’s point out
some of features:
Platform (PHP, ASP, Ruby, …)
Server (Apache, Nginx, IIS, …)
Engine (WordPress, Django, RoR, …)
Queries (count parameters in links on main page)
Scripts (number of script tags on main page)
Inputs (number of input tags on main page)
SSL (if the site works with HTTPS or not)

27. Algorithmic view
• The simpliest vulnerable-vs-secure classifier
ever:
if PHP:
vulnerable = True
else:
vulnerable = False
• Ok, just kidding =)

28. Algorithmic view
Machine learning FTW!

29. Algorithmic view

30. Algorithmic view
• Today before the talk I scanned about a
thousand sites and built this decision tree on
obtained data
• Actual classifier is a bit bigger than simpliest,
but yet the common sense is preserved %)
• If the main page is PHP-script, there’re at least
4 GET-parameters in the links on it, and there’s
at least one script tag, then site is probably
vulnerable =)

31. Algorithmic view
• Ok, this is about cost estimation, but how can
this help us to scan the site?
• Ever seen this?

32. Algorithmic view
• Let’s calculate more features for each page
and build priority queue during scan
• If you do it right, /favicon.ico will be scanned
last, and /admin.php will be scanned first

33. Algorithmic view
• Which features can we calculate?
• Dynamic/static page: detected platform
(dynamic language vs none), content-type (html
vs static), extension
• Response status: OK vs Forbidden vs Redirect vs
Not Found vs …
• A bit of NLP: if the path contains important
words like admin, password, login, etc

34. Algorithmic view

35. Algorithmic view
• Lower priority() – higher priority:

36. Contents
• Intro
• Technical view
• Algorithmic view
• Whitebox
• Outro

37. Whitebox
• Static code analysis is a lot more rocket science
thing, than blackbox testing
• Modern enterprise static code analysis systems
are big and still not enough good (some of
them still not better than grep)
• They may have nice ads with samples, but ads-
samples can probably be constructed by
hand ;)

38. Whitebox
• Most pentesters have their own dirty hacks and
regexps for finding the vulns
• I also use a simple grep wrapper, which allows to spot
out security bottlenecks and obvious bugs in no time
• Especially useful during CTF, when the source code is
not that big
• If integrated with IDE, can be rather cool semi-manual
analyser

39. Whitebox
• Collect a list of dangerous sinks for various
languages
• Take a pattern for variable (like $.* in PHP)
• Take a list of securing functions
• Generate regexps with negative lookahead,
which will search for patterns like this:
danger_func(…not_a_securing_func(…$var

40. Whitebox
• Get the result like this
• Parse it into any IDE and analyse traces

41. Contents
• Intro
• Technical view
• Algorithmic view
• Whitebox
• Outro

42. Summary
• Application testing can be made faster in many
ways
• Some of ways are achievable during manual
assessment, some of them are not
• We can build fast and scalable web application
scanner for this
• It will traverse pwning paths graph in an efficient
way and halt after hitting the requests limit

43. Results
• Some of reviewed techniques are already
implemented in reps on my GitHub
(libpywebhack repository not updated for
years): https://github.com/beched
• It will be updated as soon as I finish debugging
PoC scripts

44. Questions?
beched@incsecurity.ru