Wh What t abou out t th the e sof oftw twar are? e? How - - PowerPoint PPT Presentation

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Wh What t abou out t th the e sof oftw twar are? e? How - - PowerPoint PPT Presentation

Jul 19, 2023 21 likes •351 views

Wh What t abou out t th the e sof oftw twar are? e? How many security vulnerabilities are there in the software implementing all this smart grid functionality and the underlying protocols? Erik Poll Radboud University Nijmegen Moti

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SLIDE 1

Wh What t abou

  • ut

t th the e sof

  • ftw

twar are? e?

How many security vulnerabilities are there in the software implementing all this smart grid functionality and the underlying protocols? Erik Poll Radboud University Nijmegen

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SLIDE 2

Moti tivati tion

  • n

Security can go wrong on many levels

  • human factor
  • organisational issues
  • authentication solutions
  • communication protocols
  • security protocols
  • cryptography
  • software
  • incl. the software implementing the 4 aspects above
  • hardware
  • ...

2 Erik Poll Radboud University Nijmegen

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SLIDE 3

Moti tivati tion

  • n

Security can go wrong on many levels

  • hu

huma man n fac actor tor

  • organisational issues
  • authentication solutions
  • communication protocols
  • security protocols
  • cryptography
  • so

softwar are e

  • incl. the software implementing the 4 aspects above
  • hardware
  • ...

3 Erik Poll Radboud University Nijmegen

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SLIDE 4

So Softw twar are e (i (in)securi )security ty

Software is major source of security problems Software is not the Achilles’ heel of ICT security, but the Achilles’ body Any piece of software that an attacker can provide malicious input to is a risk Regrettable tendency to see this as unavoidable fact of life, with as ‘solutions’ 1. regularly patch the software 2. prevent the bad guys from getting access

  • air gaps, separate physical networks, secure

communication tunnels (eg TLS or IPSEC) but: won’t any reasonably compentent attacker be able to get inside these TLS tunnels?

Erik Poll Radboud University Nijmegen 4

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SLIDE 5

La LangSec ngSec (La Langua nguage-theor theoretic etic securit curity) y)

Instructive insights into root causes, and bad vs good practices, when it comes to software security. Starting point: note the common pattern in attacks on software

  • incl. buffer overflows, format string attacks, integer overflow, OS

command injection, path traversal attacks, SQL injection, HTML injection, XSS, CSRF, database command injection, database function injection, PHP file name injection, LDAP injection, ShellShock, HeartBleed, FREAK, ...

1. attacker crafts some malicious input 2. software goes off the rails processing this, unintentionally providing the attacker weird functionality

Like social engineering or hypnosis as attack vector on humans?

Erik Poll Radboud University Nijmegen 5

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SLIDE 6

Moral al: : Pr Proce cess ssing ng input put is d s dan anger erous us!

Processing involves 1) parsing/lexing 2) interpreting/executing

Eg interpreting a string as filename, URL, or email address

This relies on some language/format/protocol 1) relies on syntax 2) on semantics Insecure processing of inputs exposes strange functionality that the attacker can abuse & possibly even program.

6 Erik Poll Radboud University Nijmegen

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SLIDE 7

Fal allac acy y of clas assi sica cal input put val alidation? tion?

Classical input validation: filter or encode harmful characters

  • r, slightly better:
  • nly let through harmless characters

But: 1. Which characters are harmful (or required!) depends on the language or format. You need context to decide which characters are dangerous. 2. Not only presence of funny characters can cause problems, but als the absence of other characters, or input fields that are too long or too short, ... The proper solution: parse the entire input before any processing

7 Erik Poll Radboud University Nijmegen

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SLIDE 8

Tower er of Babel el

The Web involves many ny and comple mplex languages & formats HTTP(S), HTML, CSS, javascript, Flash, cookies & FSOs, Ajax & XML, ActiveX, jpeg, mpeg, mp4, png, gif, SilverLight, URLs/URIs, X509 certificates, email addresses, TCP/IP (IPv4 or IPv6), file names, directories, OS commands, SQL, LDAP, JSP, PHP, ASCII, Unicode, UTF-8, ...

8 Erik Poll Radboud University Nijmegen

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SLIDE 9

Sam ample e problems lems

  • Exploits with zero-width fields in JPEG images
  • Code Red worm exploiting incorrect treatment of ASCII and

Unicode chararacters

  • Different browsers interpreting X509 certificates with

multiple Common Names differently

  • ANS.1 attacks in X509 certificates

null terminator in ANS.1 BER encoded string in an X509 Common Name

  • PKCS#10-tunneled SQL injection

SQL command inside a BMPString, UTF8String or UniversalString used as PKCS#10 Subject Name

  • ....

9 Erik Poll Radboud University Nijmegen

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SLIDE 10

1. Complex input languages (aka formats, protocols)

  • length fields in data packets are notorious source of problems

2. Unclearly defined input languages 3. Hand-written parser code which

  • mixes parsing & interpretation
  • incrementally parses parts of input,

in a piecemeal fashion Aka a shotgun parser All this results in

  • lots of chances for an attacker to trigger weird behaviour
  • possibly using parser differentials, ie. differences between

implementation of the same language

Lan angS gSec ec an anti ti-patterns (don’ts)

10 Erik Poll Radboud University Nijmegen

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SLIDE 11

1. precisely & clearly defined input languages, Eg with regular expression or EBNF grammar 2. keep the input language as simple as possible So that ideally equivalence of parsers is decidable. So that you give minimal processing power to attacker. 3. generate parsers 4. complete parsing before processing working with parsed information, not still-to-be-parsed byte sequences or strings 5. for legacy software: put a generated parser in front, to ensure correct & complete parsing before processing?

  • effectively a language-specific firewall/IPS/IDS

6. use understanding of language for testing, esp. fuzzing

LangSec best practices (do’s)

11 Erik Poll Radboud University Nijmegen

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SLIDE 12

12 Erik Poll Radboud University Nijmegen

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SLIDE 13

So Some exa xamples mples

Payments, GSM, DNP3, TLS

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SLIDE 14

Uninte ntentional ntional ca case se st study: y: co contact tactles ess paymen ents ts

  • Correctly formatted RFID traffic can crash contactless

payment terminals

[MSc thesis Jordi van Breekel, 2014]

  • dual-contact bank cards of two banks misconfigured to

accept certain commands over the contactless interface that meant for the contact interface only

14

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SLIDE 15

GSM is a extremely rich & complicated protocol

Case se study: udy: GS GSM

15

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SLIDE 16

Field size Message Type Indicator 2 bit Reject Duplicates 1 bit Validity Period Format 2 bit User Data Header Indicator 1 bit Reply Path 1 bit Message Reference integer Destination Address 2-12 byte Protocol Identifier 1 byte Data Coding Scheme (CDS) 1 byte Validity Period 1 byte/7 bytes User Data Length (UDL) integer User Data depends on CDS and UDL

SM SMS S me messa ssage e fie ields lds

16

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SLIDE 17

i.e. sending randomly generated, malformed GSM traffic to phones (using a USRP with OpenBTS software)

[Mulliner et al, SMS of Death] [Fabian van den Broek, Brinio Hond, Arturo Cedillo Torres, Security Testing of GSM Implementations, Essos 2014]

Let’s fuzz!

17

weird traffic

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SLIDE 18

Weird functionality in GSM standard and phones

Ca Case se st study: y: GSM SM protoc tocol

  • l fuzz

zzing ng

18

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SLIDE 19

Weird functionality in GSM standard and phones

  • eg possibility to send faxes (!?)

Only way to get rid if this icon; reboot the phone

Ca Case se st study: y: GSM SM protoc tocol

  • l fuzz

zzing ng

19

you have a fax!

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SLIDE 20

Malformed SMS text messages showing raw memory contents, rather than content of the text message

Ca Case se st study: y: GSM SM protoc tocol

  • l fuzz

zzing ng

20

name of Mobile Soccer game shows up inside text message

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SLIDE 21
  • Lots of success to DoS phones: phones crash, disconnect from

the network, or stop accepting calls

  • Requiring reboot or battery removal to restart, to accept calls

again, or to remove weird icons

  • After reboot, the network would redeliver the SMS if no

acknowledgement was sent before crashing, re-crashing phone But: not all our SMS messages could be sent over real network

  • Surprisingly little correlation between problems and phone

brands & firmware versions

  • how many implementation of the GSM stack does Nokia

have?

  • The scary part: what would happen if we fuzz base stations?

(For the Alliander folks here: is CDMA any better?)

Ca Case se st study: y: GSM SM protoc tocol

  • l fuzz

zzing ng

21

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SLIDE 22

Robus project by Adam Crain and Christ Sistrunk

  • wrote a dedicated fuzzer for DNP3 SA
  • extension of DNP3 with Secure Authentication
  • revealed > 30 vulnerabilities in products of over a dozen of

vendors More info at automatak.com/robus or langsec.org/dnp3

Ca Case se st study: y: DN DNP3 P3

22

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SLIDE 23

Ca Case se st study: y: TLS

Many protocols have state

  • messages are expected to arrive in a particular order
  • implementations have to keep track of this
  • by implementing a protocol state machine

As well as fuzzing with strange messages we can now also try strange sequences of messages

  • ie fuzzing the order rather than the content of messages,

in different orders than the ‘happy flow’ Using state machine learning we can even automatically infer the protocol state machine by black box testing

23

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SLIDE 24

TLS.. ... accor

  • rding

ding to NSS SS implementa lementation tion

Comforting to see it is so simple!

24

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SLIDE 25

TLS.. ... accor

  • rding

ding to Gn GnuTL TLS

25

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SLIDE 26

TLS.. ... accor

  • rding

ding to Op OpenS enSSL SL

26

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SLIDE 27

TLS.. ... accor

  • rding

ding to Java a Se Secur ure e So Socket et Ex Exensi sion

  • n

27

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SLIDE 28

Whic ich h TLS S st state te machine hine would ld you trust? st?

28

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SLIDE 29

Whic ich h TLS S st state te machine hine is th s the e correct ect one? e?

29

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SLIDE 30

Food

  • d for th

thought

  • ught

What about procotols used in the smart grid, and the software implementing them?

Eg 101, 104,.... 1. Are there clear specs of these protocols? 2. Do we understand these specs?

  • Eg. do we know
  • which sub-protocols or features are (not) used?
  • the tricky parts where we can expect implementation

differences? 3. Are there tools for fuzzing? (based on such specs) 4. Are there reliable parsers? (generated from these specs)

30

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SLIDE 31

Pointer ters

  • LangSec website, http://langsec.org
  • Bolt-on security extensions for Industial Control System protocols:

A case study of DNP3 Adam Crain and Sergey Bratus, Security & Privacy, IEEE, 2015 Also http://langsec.org/dnp3

  • Security testing of GSM implementations

Fabian van den Broek, Brinio Hond, Arturo C. Torres, ESSOS 2014

  • Protocol state fuzzing of TLS implementations

Joeri de Ruiter and Erik Poll, Usenix Security 2015

  • Protocol state machines and session language,

Erik Poll, Joeri de Ruiter, and Aleksy Schubert, Security and Privacy Workshops (SPW), IEEE, 2015

31

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SLIDE 32

Old vs vs new w e.dentif entifier2 er2

32

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SLIDE 33

Woul uld d you tr trust st th this? s?

33

Do you think who designed and implemented this are confident that it is correct and secure?

complete state machine of USB-connected e.dentifier