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The Future of Software Security. David Wagner U.C. Berkeley. The Scale of the Problem. Viruses and worms prevalent — and especially nasty ones can cost > $1 billion per occurrence Phishing, pharming, botnets, rootkits, privacy spills
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The Future of Software Security David Wagner U.C. Berkeley
The Scale of the Problem • Viruses and worms prevalent — and especially nasty ones can cost > $1 billion per occurrence • Phishing, pharming, botnets, rootkits, privacy spills • Cybercrime profits for 2004 ($105 billion) claimed to exceed those of drug crime [Treasury Dept.] • 80% of home users infected with spyware [NCSA] • MTTI (Mean Time To Infection) for WinXP out-of-box: 15 minutes [SANS] • Why? (1) Because we haven’t made security usable. (2) Because our software is riddled with defects.
Where is Software Security Heading? • Traditional approaches aren’t adequate • operating system security — a good OS can’t make up for a buggy app • virus detectors, IDS — a kludge on its last legs • patch-and-pray — can’t keep up with the blackhats • working around buggy end hosts in the network — a nice idea, killed by HTTP, SOAP, … • Software security is a software quality problem • Software bugs = security holes = bad • We’ve got an application security problem
Application Security Matters “unsafe” = remotely exploitable hole is publicly known and no patch available; “safe” = anything else. Sources: scanit, Secunia • Application quality can make a measurable difference
Complexity: No End in Sight • Complexity is the enemy of security — yet is on the rise
Approaches • What can we do about it? • Try harder • Hardened languages and execution environments • Tools to find security bugs in legacy code • Languages, programming disciplines, tools to eliminate security bugs in new code
MOPS: A C Model Checker • Experiment: Analyze an entire Linux distribution • Redhat 9, all C packages (732 pkgs, ~ 50 MLOC) • Examined 5 sample security rules • Team of 4 manually examined 900+ warnings • Laborious: multiple person-months of effort • Found 108 new security holes in Linux apps • Lesson: Security bugfinding tools can be very effective
Input Validation char *name = gethostbyaddr(...)->h_name; printf(name); // BUG! • Idea: static taint tracking • Inputs from untrusted sources are “tainted” • Prevent the flow of tainted data to trusting sinks • Crude implementation — abuse the C type system: struct taintedstr { char *p; }; struct taintedstr name = mktaintedstr(gethostbyaddr(...)->h_name); printf(name); // TYPE ERROR! • Cheesy hack — augmented (Apps) Hungarian: char *tszName = gethostbyaddr(…)->h_name; printf(tszName); // BUG!
Taint Analysis Through Type Inference • A better way: type qualifier inference (CQual) • New types: $tainted int, $untainted int, etc. • Tool infers these qualifiers, where needed, so you don’t have to • Application: Detection of format string vulnerabilities • Partial results: 3 vulnerabilities in 200K LOC • In progress: analysis of an entire Linux distribution (260M LOC, 16K packages) • Application: Detection of user/kernel vulnerabilities in the Linux kernel • Linux 2.4.20: 11 exploitable security holes • Linux 2.4.23: 10 exploitable security holes
User/Kernel Security Holes • Experiment: Can CQual verify absence of u/k bugs? • Sound whole-kernel analysis • Found 10 exploitable holes in Linux 2.4.23 core • Sparse: missed all 10 bugs; 7000 annotations; many FPs • MECA: missed 6/8 bugs; 75 annotations; very few FPs • Lesson: Verifying absence of (certain) bugs is within reach • Lesson: Verification tools find more bugs
Parting Thoughts • Security (quality) is a whole-systems problem • Affects all elements of the system • Pervades SW lifecycle: design, implementation, QA • Security (quality) is a people problem • Training, user testing, processes, ... • Program analysis tools can help with the gruntwork • Bottom line: Better tools and better languages can help, but there’s no silver bullet • If we haven’t solved the software quality problem yet, we probably won’t solve the software security problem any time soon, either