PPT-TaintScope : A Checksum-Aware Directed Fuzzing Tool for Automatic Software

Vulnerability Detection Tielei Wang1 2 Tao Wei1 2 Guofei Gu3 Wei Zou1 2 1Key Laboratory of Network and Software Security Assurance Peking University Ministry of Education Beijing 100871 China. 1 Copyright Turret Punches. Siemens 840Dsi CNC Control. Up to Six . Indexable. Multi-Tool Stations. Remote Diagnostic via Ethernet. Network via Ethernet. Automatic Clamp Positioning. Automatic Tool Lubrication. Cows. The “No Bull” Talk on Fuzzing. Security B-Sides Ottawa. November 13, 2010. Mike Sues (Rigel Kent). Karim Nathoo (Inverse Labs). Objectives. We can’t cover fuzzing in-depth in 50 minutes. Raise awareness of fuzzing as an option in higher assurance/product evaluations/more focused assessments. Tielei. Wang. 1. , Tao Wei. 1. , . Guofei. Gu. 2. , Wei Zou. 1. 1. Peking University, China. 2. Texas A&M University, US. 31st IEEE Symposium on Security & Privacy. Outline. Introduction. Background . A Checksum-Aware Directed fuzzing Tool for Automatic Software Vulnerability Detection. Tielei. Wang. 1. , Tao Wei. 1. , . Guofei. Gu. 2. , Wei Zou. 1. 1. Peking University, China. 2. Texas A&M University, US.  . 0368-3500. Nurit. . Dor. Shir. Landau-. Feibish. Noam Rinetzky. Preliminaries. Students will group in teams of 2-3 students. . Each group will do one of the projects presented.. Administration. By. . Nikolaj . Tolka. čio. v. Agenda. What is web application fuzz testing. Introduction to “Fuzzing Machine”. What results it produces. Youtube. setup in “Fuzzing Machine” . How it can be used in other projects. Tielei. Wang. 1. , Tao Wei. 1. , . Guofei. Gu. 2. , Wei Zou. 1. 1. Peking University, China. 2. Texas A&M University, US. 31st IEEE Symposium on Security & Privacy. Outline. Introduction. Background .  . 0368-3500. Nurit. . Dor. Shir. Landau-. Feibish. Noam Rinetzky. Preliminaries. Students will group in teams of 2-3 students. . Each group will do one of the projects presented.. Administration. Dawn Song, . Kostya. . Serebryany. ,. Peter . Collingbourne. . Techniques for bug finding. Automatic test case generation. Lower coverage . Lower false positives . Higher false negatives. Fuzzing . Dawn Song, . Kostya. . Serebryany. ,. Peter . Collingbourne. . Techniques for bug finding. Automatic test case generation. Lower coverage . Lower false positives . Higher false negatives. Fuzzing . John . Heasman. Stanford University, April 2009. Agenda. Introductions. What is . fuzzing. ?. What data can be fuzzed?. What does fuzzed data look like?. When (not) to fuzz?. Two approaches and a basic methodology. Software Vulnerability Detection. . Tielei . Wang. 1,2. , Tao Wei. 1,2. , Guofei Gu. 3. , Wei . Zou. 1,2. 1. Key Laboratory of Network and Software Security Assurance . (. Peking University), . Ministry . Briefing pack. Contents. Executive summary. Overview of the software. Insight into the technology and key features. Implementation timeline and how to get involved. Key contacts and more information.