1. Backtracking Algorithmic Complexity Attacks Against a NIDS
Randy Smith, Cristian Estan, Somesh Jha
University of Wisconsin–Madison

2. Algorithmic Complexity Attacks
Vulnerable algorithm: algorithm whose worst case differs from typical case. The larger the difference, the more vulnerable the algorithm.
Examples:
Algorithm
Average
Worst
Quicksort
O(n log n)
O(n2)
Hash lookup
constant
O(n)

3. Algorithmic Complexity Attacks
Algorithmic Complexity Attack – an attacker induces worst-case behavior in a vulnerable algorithm.
Common observable effect is denial of service.
Crosby and Wallach: induced worst-case behavior in hash function implementations.
“Algorithms are now part of the attack surface” (Crosby and Wallach, 2003)

4. Are NIDS vulnerable? NIDS and IPS are ubiquitous, but…
Do they contain vulnerable algorithms? Can they be exploited?
YES! Only need 1 packet every 3 seconds.

5. Evading a NIDS Attacker’s Goal: Evade NIDS
Two attack vectors in an evasion attempt:
1st—alg. complexity attack targeting the NIDS
2nd—true attack targeting the network
Effect of an algorithmic complexity attack:
(NIDS) Packets enter network unexamined
(fail-closed IPS) Packets are dropped

6. Main results In Snort, vulnerability in rule-matching
worst-case vs. typical case: 6 orders of magnitude.
“Backtracking Attack”
Easily exploitable through packet payloads
Improved rule-matching algorithm limits running time differences to within 1 order of magnitude.

7. Outline Snort rule matching Inducing backtracking attacks
Countermeasures
Measurement results
Conclusion

8. Snort Rule Matching alert tcp $EXT_NET any -> $HOME_NET 99
content:”fmt=”; //P1
content:”player=”; //P3
content:”overflow”,relative; //P5
alert tcp $EXT_NET any -> $HOME_NET 99
(msg:”AudioPlayer jukebox exploit”;
pcre:”/^(mp3|ogg)/”,relative; //P2
pcre:”/.exe|.com/”,relative; //P4
sid:5678)

9. Snort Rule Matching Rule matches!
alert tcp $EXT_NET any -> $HOME_NET 99
(msg:”AudioPlayer jukebox exploit”;
content:”fmt=”; //P1
pcre:”/^(mp3|ogg)/”,relative; //P2
content:”player=”; //P3
pcre:”/.exe|.com/”,relative; //P4
content:”overflow”,relative; //P5
sid:5678)
Rule matches!
fmt=acc player=default fmt=mp3 rate=14kbps

10. Matching the packet P1 alert tcp $EXT_NET any -> $HOME_NET 99
(msg:”AudioPlayer jukebox exploit”;
content:”fmt=”; //P1 P3
pcre:”/^(mp3|ogg)/”,relative; //P2
content:”player=”; //P3
pcre:”/.exe|.com/”,relative; //P4 P4
content:”overflow”,relative; //P5
sid:5678) P5
Rule matches!
fmt=acc player=default fmt=mp3 rate=14kbps

11. Inducing Backtracking attacks
P1,P2,P3,P4 match in 3 positions each
P5 never matches
alert tcp $EXT_NET any -> $HOME_NET 99
(msg:”ReelAudio jukebox exploit”;
content:”fmt=”; //P1
pcre:”/^(mp3|ogg)/”,relative; //P2
content:”player=”; //P3
pcre:”/.exe|.com/”,relative; //P4
content:”overflow”,relative; //P5
sid:5678)
Leads to excessive packet traversals!
fmt=mp3fmt=mp3fmt=mp3player=player=player=.exe.exe.exe
fmt=acc player=default fmt=mp3 rate=14kbps

12. Matching the malicious packet
alert tcp $EXT_NET any -> $HOME_NET 99
(msg:”AudioPlayer jukebox exploit”;
content:”fmt=”; //P1
pcre:”/^(mp3|ogg)/”,relative; //P2
content:”player=”; //P3
pcre:”/.exe|.com/”,relative; //P4
content:”overflow”,relative; //P5
sid:5678) P2 P3 P4 P4 P5 P4 P5 P5 P5 P5 P5 P5
fmt=mp3fmt=mp3fmt=mp3player=player=player=.exe.exe.exe

13. Are real rules vulnerable?
Rule number
Processing (s/GB)
Slowdown
Same proto
All traffic
3682 (SMTP)
30,933,874
232,936X
1,501,644X
2611 (Oracle)
6,220,768
56,296X
301,979X
1382 (IRC)
1,956,858
134,031X
94,993X
2403 (NetBIOS)
357,777
490X
17,368X
1755 (IMAP)
89,181
444X
4,329X

14. Safer backtracking
Memoization: maintain a table of subproblem “answers”; never evaluate a predicate twice at the same starting payload offset
alert tcp $EXT_NET any -> $HOME_NET 99
(msg:”AudioPlayer jukebox exploit”;
content:”fmt=”; //P1
pcre:”/^(mp3|ogg)/”,relative; //P2
content:”player=”; //P3
pcre:”/.exe|.com/”,relative; //P4
content:”overflow”,relative; //P5
sid:5678)
Identify constrained predicate sequences
Monotone memoization: don’t re-evaluate monotone predicates that have been evaluated at lower offsets

15. Reductions in processing cost4 11 18 P5 P4 P2 P3 P5 P4 P2 P3 P2 7 14 21 P3 28 35 42 P4 P4 P4 46 50 54 P5 P5 P5 P5 P5 P5 P5 P5 P5
fmt=mp3fmt=mp3fmt=mp3player=player=player=.exe.exe.exe

16. Outline Snort rule matching Inducing backtracking attacks
Protecting against backtracking attacks
Measurement results
Conclusion

17. Slowdown factor w.r.t. same protocol
Measurement results
Rule number
Slowdown factor w.r.t. same protocol
Before
w/ Memo+
3682 (SMTP)
232,936X
0.95X
2611 (Oracle)
56,296X
1.57X
1382 (IRC)
134,031X
6.00X
2403 (NetBIOS)
490X
0.17X
1755 (IMAP)
444X
0.46X

18. Live experiment topology
Background Traffic
AC Attack
True Attack

19. Live experiment Background Traffic @ 10Mbps AC Attack
Targets Snort SMTP rule 3682
Directed at sendmail server
True Attack: NIMDA
300 exploit attempts, sent 1 byte per second.
New exploit started every second.

20. Live experiment results
Attack Description
Exploits
Detected
Required Rate (kbps)
Control (No attack)
300/300 --
2 packets every 60 s.
220/300
0.4
1 packet every 5 s.
4/300
2.4
1 packet every 3 s.
0/300
4.0
20 packets initially
0.8

21. Conclusions
NIDS operation is complex. Many opportunities for vulnerable algorithms.
In Snort, rule-matching is vulnerable and can be exploited by an attacker.
Memoization, along with other semantics-preserving operations, significantly reduces vulnerability.
Other vulnerable algoritms exist.

22. Backtracking Algorithmic Complexity Attacks Against a NIDS
Thank you.