bap VS klee

BAP (https://github.com/binaryanalysisplatform/bap), angr (https://angr.io/) and others already do what you're asking for as more purpose-built solutions for dynamic analysis. Angr specifically in python.

I find this pretty good https://www.cs.cornell.edu/courses/cs3110/2019sp/textbook/intro/ocaml.html. Fun projects include compilers (pattern matching and static types are why Ocaml is usually selected), binary analysis stuff https://github.com/BinaryAnalysisPlatform/bap, stuff that requires async so you can try out nomadic async stuff, or really anything you desire.

I also want to use something like KLEE: https://klee.github.io/

I think that KLEE was quite good at that. See https://klee.github.io/

I think the most critical part the flow is the integer overflow bug, and it is totally avoidable. I am a software engine at Microsoft. Half my time was spent on security and compliance. We have the right tool, right policy to avoid such things happen. However, I'm not saying Microsoft software is free of integer overflow bugs. I don't intend to advertise Microsoft C/C++ development tools here, but they are what I know most.

Let's go to the technical part: If you are asked to implement the binary algorithm with your favorite programming language, how do you verify your code is correct? Unit-tests. How many test cases you may need? More than 10. As long as you have enough tests, your don't need to worry too much. But how much test coverage is enough? Please remember JDK had a integer overflow bug in their binary search in early 2000s. So, people know the algorithm, but normally people don't know how to test their code, therefore most people can't write bug-free binary search code. And any non-trivial C/C++ function may need tens of thousands test cases. Simply you can't write the tests by hand.

You need the right tools: fuzzing and static analysis.

At Microsoft, every file parser should go through fuzzing, which basically is you generate some random input, then you run your tests with the random inputs. Not very fantastic. But there is another kind of fuzzing: symbolic execution, which tries to find all the possible execution paths of your code. If you run symbolic execution with your binary search code, you can get 100% test coverage. And it is guaranteed bug-free. It is like a math proof. Please note the advantage is based on human just had surprising great advancement on SAT solvers in the last 20 years. And often you need to make some compromises between your business goal and security. Most functions can't reach 100% test coverage. You need to simplify them. See https://github.com/klee/klee to get a quickstart. Though C/C++ is often considered unsafe, they have the best fuzzer.

Then it is about SAL annotation and static analyzer. In C, whenever you pass a pointer of an array to another function, you should also pass its length with it. And in the callee function you should check the length. If you forgot it, your static code analyzer will give you a warning. In such a sense, if you didn't allocate enough memory, it will only result an error code being returned instead of undefined behavior.

The last thing: Use safeint wrapping your malloc function. https://docs.microsoft.com/en-us/cpp/safeint/safeint-library...

When we move off the binary search toy example to a real code base, clearly you can see how much extra effort is needed to make the code safe. Please pardon me, most OSS libraries don't have the resource. Many famous OSS projects are "Mom-and-pop" shops. They don't have any compliance rule. They invest very little on fuzzing. So the big companies really should help them. Now you see an integer overflow bug was found in Apple's image render, but was the code written by Apple? Not necessarily. Now we all see the importance of the Open Source movement. It's time to think how to harden their security. For example, even I want to spend my free time on adding SAL annotations to an OSS project I love, would the maintainers accept it?