![]() Hence any value greater than maximum supported value of unsigned char leads to integer overflow. strlen()’s return type is size_t (unsigned int) which gets stored in unsigned char data type. ![]() Line of the above vulnerable program shows us that an integer overflow bug exists. $gcc -g -fno-stack-protector -z execstack -o vuln vuln.c If(passwd_len >= 4 & passwd_len /proc/sys/kernel/randomize_va_space Unsigned char passwd_len = strlen(passwd) /* */ Here I will be talking only about integer overflow, but the procedure remains same for underflows too!! For example when we try to store -2147483649 to signed int data type, its gets wrapped around and stored as 21471483647. Similarly storing a value lesser than the minimum supported value is called integer underflow. This is called integer overflow and this overflow could lead to arbitrary code execution!! For example when we try to store 2147483648 to signed int data type, its gets wrapped around and stored as -21471483648. When we try to store a value greater than maximum supported value, our value gets wrapped around. In this post I will be talking ONLY about integer overflow leading to stack overflow, integer overflow leading to heap overflow will be covered up later in a separate post. Integer overflow on its own doesnt lead to arbitrary code execution, but an integer overflow might lead to stack overflow or heap overflow which could result in arbitrary code execution. At a common MachinePrecision of 15.9546 digits, 0.1 + 0.2 actually has a of 0.30000000000000004, but is printed as 0.3.Īrguably, your example is hitting a different failure mode (integer overflow) than just floating point imprecision, so I guess that’s a point to be made? For that though, SaferIntegers.jl has been suggested and would show you the problem right away.Storing a value greater than maximum supported value is called integer overflow. See here for Matlab and here for Mathematica:īy default, the inputs 0.1 and 0.2 in the example are taken to have MachinePrecision. For example, both Matlab and Mathematica lie to you in the same way. Interestingly, I believe the majority of languages you’ve quoted do actually have the same “problem” - they just mask it by showing fewer decimals than would be required to accurately represent the true number. There is not one single language intended for numerical analysis that I know of (SAS, R, S, Matlab, Mathematica, Maxima, Octave, SPSS,… a few others), not one, that allows for incorrect arithmetic of the kind I showed you above. I am using R language as an example because R is widely utilized in Pharma when regulations matter and this is one language they use to have their models approved. They might ask the following question and rightly so, “ wait a minute, you are telling me that my model can be mathematically correct but that I cannot expect mathematically correct results?” If now we mention Julia speed they only thing they are going to hear is how fast Julia fails. These people need to have their models approved by regulatory bodies, how do you think they will react when they find out that Julia, by design, accepts 1 + 1 = 2.12 ? julia> 1/(1-10^49/10^63) + 1/(1-10^49/10^63) # ~1 + ~1 Well, now you have found the first one, I work for Big Pharma and in some of my past projects closely so to the Pharmacokinetics and Pharmacodynamics crowd. While they have many concerns, I can’t say that anyone in any of those industries has ever expressed concern about integer overflow as a regulatory issue. At this point I’ve been involved in a lot of discussions with customers using Julia in a variety of heavily regulated industries-finance, pharma, medicine, insurance, aviation, aerospace, etc.
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