That said, Binary Ninja’s implementation is by no means complete. For example, all tested tools fail to consistently reconstruct 16-bit division with small immediate divisors when compiled for 圆4 using clang: Like most heuristic approaches, this is not a foolproof mechanism! But no other tool is perfectly accurate either. This is the route we’ve chosen to go for our division/modulus deoptimization feature. Unfortunately the performance of this implementation at scale and across compilers and architectures is not only slow, but it actually fails at least as often as simpler heuristics.įinally, there are several heuristics based on detecting particular patterns (asm or IL based) that can be used to identify patterns. One example as demonstrated by the Division Deoptimization plugin is to use a solver such as z3 to try to recover the simplification. There are several ways to try to recover the original division which is far more intuitive and easer to reason about. Instead of implementing them with the native CPU instructions, they will use shifts and multiplications with magic constants that when operating on a fixed integer size has the same effect as a native division instruction. One of the many things compilers do that can make reverse engineering harder is use a variety of algorithmic optimizations, in particular for modulus and division calculations. With a slew of decompilation improvements, Binary Ninja 3.5 (Coruscant) has completed its jump from hyperspace dev with even more improvements to the decompilation quality and many other quality of life improvements across the UI, API, documentation, debugger, and more! Here’s a list of the biggest changes, but don’t forget to check out the full list of changes with even more fixes and features.
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