How to Stop Man in the Browser Attacks
1 like2,350 views
The document discusses the increasing threat of client-side attacks, specifically man-in-the-browser (MITB) vulnerabilities, and their implications for online security and commerce. It highlights the growing sophistication of such attacks and emphasizes the need for businesses to develop robust strategies that include shape-based and content-based tests to defend against these threats. The document also outlines Imperva's Securesphere solution for detecting and managing fraud, particularly from infected client devices.
How to Stop Man in the Browser Attacks
- 1. Stopping Fraud – Getting Rid of the Man in Your Browser Rob Rachwald, Director of Security Strategy Noa Bar-Yosef, Sr. Security Strategist
- 2. Agenda Motivation Problem Definition Shape Based Tests Content Based Tests Overall Solution Strategy Summary
- 3. Today’s Presenter Rob Rachwald, Dir. of Security Strategy, Imperva Research + Directs security strategy + Works with the Imperva Application Defense Center Security experience + Fortify Software and Coverity + Helped secure Intel’s supply chain software + Extensive international experience in Japan, China, France, and Australia Thought leadership + Presented at RSA, InfoSec, OWASP, ISACA + Appearances on CNN, SkyNews, BBC, NY Times, and USA Today Graduated from University of California, Berkeley
- 4. Today’s Presenter Noa Bar-Yosef, Senior Security Strategist, Imperva Research + Researches trends in the threat landscape Security experience + Previously, held the position of Sr. Security Researcher for Imperva’s Application Defense Center + Credited for multiple commercial applications’ vulnerabilities + Holds a Master’s thesis specializing in Information Security Thought leadership + Writes a bi-weekly column on hacker trends and techniques for SecurityWeek
- 5. Motivation
- 6. Client Side Attacks - Scope of Problem Major Attack Vectors
- 7. Client Side Attacks - Scope of Problem Major Attack Vectors Exploiting Browser Code Vulnerabilities • Expected to rise with the introduction of HTML5 Exploiting Browser Plug-ins • E.g. Java, Flash, PDF, Media Player Exploiting OS libraries • E.g. Graphics rendering
- 8. Client Side Attacks - Scope of Problem 2010 Vulnerability Figures Client side Server side • 77 IE vulnerabilities, • Only 36 vulnerabilities 106 Firefox across IIS, Apache vulnerabilities, 188 and Tomcat Chrome vulnerabilities • 73 Adobe Flash, 9 Adobe Reader related vulnerabilities • 72 Various ActiveX related vulnerabilities
- 9. Client Side Attacks - Scope of Problem Malware Distribution Methods Drive-By-Download / Malvertizing Phishing, “Spear Phishing” Torrent and P2P Physical
- 10. Client Side Attacks - Scope of Problem 2010 Attack Figures A 2010 report by Kaspersky + ~600M attempts reported to KSN, more than 5 times increase over 2009 Microsoft detects 60K-100K Zeus-infected machine per month 2010 1H – Microsoft cleaned 6.5M bot infections Rustock spanned 1M computers Consumers cannot be expected to cope with the technical problem on their own
- 11. From Consumer Attack to a Business Problem The threat to consumers is constantly growing •Number of vulnerabilities •Number of attacks We are passed the point •Types of attacks of no return •Sophistication • We cannot expect average consumers to avoid infection and mitigate attacks alone • We cannot deny service to infected consumers • We cannot let the consumer bear the consequences of a compromise Usage is expanding beyond banking and popular retail applications
- 12. From Consumer Attack to a Business Problem Potential consequences (of failing to do so): + Reduced onboarding rate + Reduced activity + Increased refunds + Increased insurance rates Consumer facing malware threatens online commerce* Forrester Feb 2011: Malware And Trojans And Bots, Oh My!
- 13. From Consumer Attack to a Business Problem Car User Safety Online User Safety
- 15. Client Side Trouble – Types of Interaction Key • No interaction between malware and application loggers • Offline interaction between attacker and application using stolen credentials
- 16. Client Side Trouble – Types of Interaction • Some interaction between browser and actual application during attack Phishing • Could be used for detection of some Phishing campaigns • Offline interaction between attacker and application using stolen credentials
- 17. Client Side Trouble – Types of Interaction Man in • Extensive interaction between malware the and application during attack • Offline interaction between attacker Browser and application using stolen credentials
- 18. Man in the Browser Attacks (aka Proxy Trojan) Attacker code runs in context of victim’s browser Original motivation + No need to attack infrastructure (DNS, tap into router, etc.) + Defeats SSL Additional benefits + Access to local resources + Access to application session data Prominent Actors + ZeuS, Gozi, URLZone, Sinowal, Limbo, and SpyEye + Silentbanker
- 19. MitB Attacks - Proxy Trojans in Action Before After
- 20. MitB Attacks - Proxy Trojans in Action Before After
- 21. MitB Attacks - Proxy Trojans in Action Before After
- 22. Proxy Trojan Architecture Web Application Client Machine
- 23. Proxy Trojan Architecture Drop Server Inject Fake Transaction Extract Data Tamper Page Web Application Client Machine Tamper Request
- 25. A Typical Change by a Trojan Clean Infected Observation: Trojan likes to tamper with plain traffic
- 26. Observing Typical Changes by Trojans Encoding of • Enforces use of traffic that is easily tampered by the Trojan Related Headers • Avoids HTTP/1.1 connections, and compressed data Client Type • Ensures identification by the drop server and other Identification attacker controlled components Additional • Extra data provided by an unfortunate victim • Could represent client identification for attacker Parameters controlled components Parameter Order • A consequence of fake transactions
- 27. Shape Based Tests Step 1: • The application (or a device protecting the application) inspects the shape of incoming messages for changes typical to Trojans Step 2: • If a Trojan pattern is detected mark the client (IP address / session / request) as “infected”
- 28. Shape Based Tests in Action Drop Server Apply Shape Tests Inject Fake Transaction Extract Data Tamper Page Web Application Client Machine Apply Shape Tests Tamper Request
- 29. Challenges – Tracking Trojan Discrepancies • Each Trojan may • Need to keep display a different track of Trojans change • Create a • Changes may be framework for reflected in shape based rules specific request • Create a types framework for constructing shape tests
- 30. Challenges – Avoiding False Positives Challenge: • Some real client devices do not support (or choose not to support) HTTP/1.1 or compressed data Solution: • Engage the browser in a challenge response protocol
- 31. Challenges – Avoiding False Positives HTTP/1.1 200 OK . . . Content-Encoding: gzip Refresh: 2;url=infection_test.html?infected=no <html> ...........V*//W...Qzi...I...z...J:`.......T$......d.y.%@.^f.R,...(..y.:.J....9.V......%%...JV.J~.a...!.. ~@.Dqbkc...%6.... <head> <script>window.navigate('infection_test.html?infected=yes')</script> </head> <body></body> </html>
- 33. Observing Content-Tampering Trojans Observation: • Current malware tampers HTML at the network layer (before it is interpreted by browser) • This is due to simplicity and robustness considerations Solution: • Use client side code to verify integrity of HTML page content in coordination with the server
- 34. OK Solution: Altering the Trojan Behavior Naïve Solution • Step 1: “Provoke” the MitB into making changes • Step 2: Compare the HTML content to known Trojan behaviors Challenges • MitB can be configured to avoid this type of manipulation • Solution requires constant chase after MitB configuration files Requires constructing an up-to-date database of “known behaviors”
- 35. Better Solution: Content-Based Tests Step 1: • Server computes a digest of the delivered HTML page Random (invisible) elements are injected into the page before computation Step 2: • Server appends a page digest computation function to the HTML page Computation function code includes a random salt Step 3: • When page is loaded into the browser, the computation function is invoked, computes the digest and sends it to the server for verification Step 4: • If the browser does not send back a digest then infection is assumed
- 36. Content Based Tests in Action Drop Server Compute Digest and Inject Digest Computation Function Inject Fake Transaction Extract Data Tamper Page Web Application Client Machine Compare Digests Tamper Request Compute Digest
- 37. Content-Based Tests: Strengths 1) Digest cannot be pre-computed by malware due to the random HTML elements 2) Digest cannot be computed by malware without executing the digest computation function + Requires malware to implement / invoke Javascript engine 3) Computation function can be extended to explicitly reference the randomly injected HTML elements through DOM functions + Requires the malware to implement / fake DOM 4) Malware cannot dismiss test
- 38. Content-Based Tests: Strengths 5) Does not depend on specific MitB configuration and the expected changes + Only depends on protected application page + Some configuration options should be available to restrict the parts of the page that are digested – Avoid elements produced by client side code 6) Breaking the tie with attackers + Complexity of the computation process can be increased with small effort + Resulting changes to malware code are complex and painful, increasing its footprint
- 40. Look at the Complete Picture Apply shape based … And content-based tests… tests
- 41. Interact with Infected Clients Provide clear visual warnings Contact customer offline Apply business access policies • Example 1: Allow data extraction but deny transaction • Example 2: Limit transaction size Automatically employ extra validation through side channels • Adaptive authentication Keep a more comprehensive audit trail for the user / session
- 42. MitB is Only Part of the Landscape Identifying account takeover • Server side fraud detection • Device profiling and reputation • Advanced authentication Defeat phishing campaigns • Detect and takedown campaigns • Detect victims in real time
- 43. Requires a Flexible Deployment Framework Cannot change application code whenever capabilities change or threats morph Be able to protect legacy applications Create consistency across all applications and flexibility in choosing vendors
- 44. Summary
- 45. Summary Threat to consumer is constantly growing and is past the point where we can expect most of our consumers to avoid infection Consumer infection has become a business problem While providers should urge consumers to be prudent they MUST learn how to interact with infected Some car safety mechanisms are consumers and create a safe business environment for them regardless of the general threat already regulated. We can expect the same from business IT security
- 46. Summary Enterprise IT is failing to properly tackle client based attacks within enterprise The growing number of so called “APT” attacks on organizations demonstrate the effect of “compromised insider” Failures stem from the same reason: try to avoid infection rather than learn to interact with infected clients
- 48. SecureSphere 9.0 - Fraud Prevention Services SecureSphere integrates with Trusteer to detect users infected with malware like SpyEye, Zeus, Gozi & Silon 1. User accesses Website 2. SecureSphere redirects browser to Trusteer 3. Browser downloads, runs malware check 4. Result reaches WAF for analysis Is this endpoint safe? Pass / Block
- 49. Use Case: Man in the Browser – Fraud Malware Challenge + Fraud malware performing activities on behalf of customers, causing money losses & customers dissatisfaction + FFIEC compliance requirements Solution + Detect infected end-devices + Block sensitive areas in the application from infected devices + Report on users connected from infected end-devices 49
- 50. ThreatRadar Fraud Prevention Stopping MitB SecureSphere provides full event detail to analyze Man in the Browser (MitB) attacks
- 51. Centrally Manage Fraud and Web Security Known Attack Sources User Infected with Malware Geolocation SecureSphere Policy Engine User Name Browser and Agent Web Attack Detection Bot Detection Combining Web fraud with WAF policies enhances accuracy of fraud detection
- 52. Webinar Materials Get LinkedIn to Imperva Data Security Direct for… Answers to Post-Webinar Attendee Discussions Questions Webinar Webinar Slides Recording Link