Secure Web Applications via Automatic Partitioning

1. Secure Web Applications via Automatic Partitioning Stephen Chong, Jed Liu, Andrew C. Meyers, Xin Qi, K. Vikram, Lantian Zheng, Xin Zheng. Cornell University

2. Outline • Introduction • Swift Architecture • Writing Swift Applications • WebIL • Swift Runtime • Evaluation • Conclusion

3. Introduction Web applications are a critical part of today’s infrastructure

4. Introduction • Web applications account for 69% of internet vulnerabilities • Developer dilemma • Performance vs security

5. Introduction • Guess a number game • Confidentiality requirement • Client cannot see number • Integrity requirement • Client cannot affect number of guesses • Only server can decide if guess is correct • Client side only implementation • Best performance • Client can cheat

6. Swift • Building web applications that are secure by construction • Automatic partitioning of code and data • Security critical code/data placed on server side only • Code/data placed on client side for performance

7. Swift Architecture • Jif Source Code • WebIL • WebIL Optimization • Splitting Code • JavaScript and Java Output • Partitioning and Replication

8. Swift Architecture

9. Writing Swift Applications • Extensions of Jif programming language • Security policies expressed using labels • Confidentiality and Integrity policies • Labels refer to principals • *(server) and client principals • Compiler statically checks that information flow is consistent with policies • Trust model • Un trusted client • Trusted server

10. Sample Policies

11. Guess a number Application

12. WebIL • Concerned with placement of code and data • Replace Jif labels with placement annotations • Placements chosen to optimize responsiveness without sacrificing security • Partitioning solved as Integer programming problem

13. Placement Annotations • 9 placement annotations

14. Guess-a-Number in WebIL

15. Partitioning Algorithm • Represent control flow as weighted directed graph • Graph nodes are statements • Edge weights are exec. frequencies • Integer programming problem • Reduce to instance of max flow problem • Solution is placements of code/data

16. Partioning of Guess-a-Number

17. Swift Runtime • Controls synchronization and communication • JavaScript runs on Client • Java code runs on server • Asymmetric trust model • Execution blocks • Closures • Activation Records

18. Execution Block • Methods divided into execution blocks • Single entry • Multiple exits • Unique ids • Control transfer message • Branch to block executing on different host

19. Execution Blocks of Guess-a-Number

20. Activation Records • Execution blocks run in context of activation records • Client/server have different views of same activation record • Activation record updates forwarding between hosts • Security restrictions of forwarding

21. Closures • Next execution block id and activation record id • Stack of closures • Correct simulation of method calls/exceptions • Integrity of control flow • Clients invoke high integrity closures in controlled way

22. Evaluation • Swift Compiler • Jif compiler + 20K LOC • Runtime system = 2.6K LOC • Six web applications implemented

23. Generated code size

24. Network messages

25. Conclusion • Constructing secure web applications • Automatic partitioning of functionality • Enforcement of information security policies • Programmer effort to add annotations