Extending to new vulnerability classes

One of the things I really want to support is automatic generation of exploits for modern heap based vulnerabilities. At the moment I have other features to implement so this has gotten pushed back for the time being. In the meantime, I wanted to see how hard it was to extend my current functionality from stack overflows that trash the EIP to other vulnerability/exploit combinations. So, yesterday I went and found an example from an wargame I used play. It contains a pretty silly vulnerability, as follows:

(The aim here was to see how hard it was to extend my current code, not to see how complicated I could make a test case. The array pointed to by input is controllable by the user and 256 bytes in size. The function some_func is some benign function that just exits the program)

void func_ptr_smash(char *input)
    void (*func_ptr)(int);
    char buffer[248];
    func_ptr = &some_func;
    strcpy(buffer, input);

    (*func_ptr) (z);

Extending the tool turned out to be pretty simple. In a stack smash that overwrites the stored EIP we attempt to generate a query that expresses the constraint memLocation == trampolineAddr, where memLocation is the value in ESP at the ret instruction, and trampolineAddr is the address of some usable trampoline.

Modifying this to handle a function pointer overwrite can be done in a couple of ways, depending on what parts of the address space are randomised and how generic we want the solution to be. The most straight forward solution is simply to treat a function pointer overwrite like a slight twist on the previous situation. Essentially, instead of a ret instruction popping a tainted value into the EIP, which we can then redirect to a trampoline, we have a call instruction where the argument is tainted and can be sused to redirect to a trampoline. So, instead of generating a constraint on the value of ESP we have to express the constraint on whatever register/memory location the call instruction uses as an argument.

Another potential approach is usable if some non-randomised data locations exist that we can use as a shellcode buffer. Once again the same data flow analysis can be used to find the location of a suitable home for shellcode in these areas. In this case we avoid the requirement for a register to contain the address of one of these buffers and can just jump right into it by generating a constraint that specifies the operand to the call is equal to the static memory address of our shellcode.

Here is an example run of the tool in which we use the latter method. I tested it on Ubuntu 8.04 which has a non-randomised heap by default and thus we can hardcode the address of shellcode buffers on the heap. The vulnerable program was compiled with -O0 -fno-stack-protector -D_FORTIFY_SOURCE=0, otherwise gcc would have repositioned the overflow buffer and optimised the call so that the address is calculated at compile time.

In conclusion, my current approach was easily extendible in this case due to the similarity between a function pointer overwrite and smashing the stored EIP. Both cases essentially only differ at the point where the tainted memory location or register is moved to the EIP. They are detectable in exactly the same way and have the same symptom; namely, the attempted movement of tainted data into the EIP register. I would hypothesise that any vulnerability* with the same symptom can be dealt with in a similar way. So then we must ask, what other symtoms are there? Well, what can cause a program to crash? We have seen the case where the program attempts to execute data at an unmapped memory location, so that leaves invalid reads and writes.

Exploiting vulnerabilities detected via an invalid read/write

Old glibc heap exploits are a simple example of those that would be detectable as a result of an invalid write. I won’t go into the details of the method but the unlink macro essentially has the following effect, where next->fd and next->bk are under our control:

 *( next->fd + 12 ) = next->bk
 *( next->bk + 8 ) = next->fd

In this case the vulnerability will probably be discovered when the application tries the first write. This is a crucial difference between the earlier vulnerabilities and this one when considering how to automatically generate an exploit. Detecting the potential for an exploit is still simple – when an instruction writes memory we can simply check the destination operand to see if it is tainted**. The problem now becomes ‘how do we automatically determine a useful location to write?’. Assuming all data sources are randomised our options are limited somewhat. Depending on the protections in place the DTORS/GOT might be usable. Other than that (and depending on what the value we are corrupting actually points to) we could potentially just change the lower bytes of the location being written and attempt to modify some useful program variable on the heap***. This has the disadvantage of requiring some sort of input from the user, as determining what is a useful variable would seem to be mostly application specific.

If the location being written is on the stack we could potentially modify the lower bytes to change a stored EIP/EBP and then proceed in the same fashion as before. To automate this we could note the value of the ESP when a call instruction occurs and calculate the difference between this value and the location of the variable being written.

To sum these options up we have two potential output types. The first is a direct exploit; It writes the DTORS/GOT or changes the lower bytes of a stack variable to modify a stored EIP and may or may not be possible depending on the protections in place and the location being written. The second is a new program input that may lead to another crash at a different location. It is basically another fuzz input except with potentially some program variables corrupted. In this case, the result is that the program can continue execution and we hope that it hits another exploitable crash further on****.

For a read from an invalid address we also have a couple of options. If the variable being read into is later used for something such as memory allocation or as a bound on a loop/function involved in moving memory we might attempt to control this value. To do this automatically would require some form of static analysis to determine if the variable was ever used in such a context (or perhaps multiple runs of dynamic analysis). Once again, as in the write case, we also have the option to just manipulate the destination read in such a way that it is any valid address and hope that another vulnerability is later triggered. Unlike the write case this is pretty trivial though as we can just read from any address in the .text section.

* Obviously this doesn’t apply to certain vulnerability classes. It applies to most I can think of that we will exploit by somehow changing the execution pointer. Not trust problems, many race conditions, other design flaws etc.
** Some slightly more complicated analysis might be required here as sometimes a destination operand in a write can be legitimately tainted but restricted to the bounds of some safe chunk of data
*** We could also change the metadata of some other chunks on the heap but right now (3am in the morning) I can’t think of an obvious way to leverage this for code execution
**** In some cases the address written might be constrained in such a way that a heap spray is required to ensure it is mapped. Same idea applies for reads. Another potential problem is that all writable data stores might be randomised. In this case heap spraying could again be useful.

Gathering constraints from conditional branches

Dataflow analysis without considering the effects of conditional branching is of limited use when trying to generate an exploit or find a vulnerability. For lightweight analysis the inaccuracies introduced may be acceptable, but when creating an input that should drive the program to a specific point we must consider all conditional branches affected by user input and represent the inherent restrictions in the path condition.

Consider the example:

cmp eax, ebx

In this case, if either eax or ebx are tainted then our choice of input can influence the condition. We would therefore like to have this condition represented in the formula we will generate to represent the path condition. We can achieve this be separating intructions into two categories; the set A, those that write the EFLAGS, and the set B, those that read the EFLAGS. Associated with every entry in A and B is the list of EFLAGS it writes or reads respectively. We represent the EFLAGS register using vector E <e_0, e_1, ... , e_31>. Each entry in this vector stores a list containing the operands of the last instruction to write this particular eflag. If the last instruction to write the eflag was not tainted by user input then the operand list will be empty.

(In practice each entry in the vector E is an object containing 1 to N vectors, where N is the number of operands of the instruction that wrote e. Each operand vector is of size 1 to DWORD_SIZE, with each entry representing the taint information for a single byte of that operand. The cost of instrumenting at the byte level is slower runtimes due to the extra overhead, but it means we gain accuracy and expressiveness in our formulae)

Using this infrastructure we can then begin to record the operands involved in instructions that write the EFLAGS register at runtime. Our instrumentation routine for every instruction ins looks something like this:

if ins in A
    operandList = ins.operandList;
    if (operandList.tainted)
        for eflag in ins.eflagsWritten:
            eflag.operandList = operandList;
        for eflag in ins.eflagsWritten:
            eflag.operandList = [];

Once this algorithm is in place we can then process instructions from set B, those that read the EFLAGS register. For now I will just consider conditional jumps and exclude other instructions like cmpxchg, adc etc. The majority of eflag write/read combinations can be easily expressed by simply taking any of the eflags read by a given instruction, extracting the operand list (if there is one), and directly encoding the semantics of the condition on the operands. e.g. if the instruction is cmp x, y and the condition is jg, and the jump is taken, then we can express this as (> x y). To determine whether the jump has been taken or not, and hence whether the condition should be negated, we take an approach similar to that of Catchconv/Smartfuzz. That is, on the taken branch we simply insert a callback to our analysis routine with the condition and on the fallthrough path we insert a callback with the negated condition. Once this callback is triggered we basically have all the components required to store the condition for later processing – the operands involved, the conditional jump and whether the condition evaluated to true or false.

When we decide to generate an input it becomes necessary to convert these conditions to an SMT-LIB compliant formula. We first filter out the relevant conditions. Basically we only need include information on conditions if our changes to the original input might result in a different outcome. This is relatively simple to determine. For every condition we can extract all TaintByte objects that represent the taint information and dataflow history of a given byte. We can trace this back to a specific byte from user input and if we then decide to change this byte, the conditions it occur in will be included in the formula. SMT-LIB format has support for concatenation of bit-vectors so we generate our formula by concatenating the information on single bytes into words and double words where necessary. For example, if some of our input was treated as a 4 byte integer and compared to 0 we would end up with conditions like (not (= (concat n1 n2 n3 n4) bv0[32])), which expresses the concatenation of 4 bytes (which would earlier be declared as being of size 8 ) and the condition that these 4 bytes should not equal 0 when concatenated together. SMT-LIB bitvector arithmethic is modulo-X, where X is the size of the bit-vectors involved, and so it accurately models integer overflows/underflows.

(I mentioned that this approach works for most instructions. One case where some hackery is required is the test instruction. Typically something like test eax, eax is followed by a jz/je instruction. Obviously the intention here is that we are checking if eax is 0, not checking if eax == eax . This situation is easily detected by simply comparing the operands that set the zero flag on a jz/je for equality)

To demonstrate how these conditions can be gathered and used I have put together an annotated run of my prototype against a simple vulnerable program. It runs user input through a filter to ensure it is alphanumeric and if so a strcpy vulnerability is triggered. It can be found here. To get an idea of the kind of SMT constraints this generated I’ve also uploaded the entire formula here. The constraints that exist specifying every byte cannot be 0 are gathered from strcpy.

Morphing shellcode using CFGs and SAT

A friend of mine is working on a project that involves building a metamorphic engine for x86 assembly. One of the core parts of such a project is a context free grammar describing valid mutations for a given instruction. To use one of his examples, an instruction to put v in r can be modelled in a BNF style grammar as follows:

put_v_in_r(v1,r1) := pushl_v(v1) popl_r(r1) | movl_v_r(v1,r1)

This could be quite useful in the process of automatic exploit generation. I expect the user to provide shellcode expressing what they want the exploit to do, but what if that shellcode is constrained by conditionals in the program? e.g. the shellcode is \x41\x42\x43\x44 but there is a conditional in the program that causes it to abort if the byte \x41 is detected in the input stream.

One solution in this situation, is to simply ask the user for different shellcode, but in the presence of complex modifications and conditions it quickly becomes impossible to provide any kind of meaningful feedback on what they should do to pass the filters. How about an automated solution? We have the conditions in propositional form and, due to the other project mentioned, we also have access to a grammar of potential shellcode mutations. If we can combine the two into a new propositional formula, describing the effect of mutating the shellcode using this grammar and the conditions from the program, we might be able to use the power of SMT/SAT solvers to brute force a solution! If a solution is found then we can preserve the effects of the original shellcode, as well as passing the filters.

The main difficulty in implementing this solution comes from encoding the grammar in propositional form. The typical solution to encoding a transition relation as a propositional formula involves iteratively applying the transition relation to existing states until a fixed point is reached. That is, starting with our original shellcode we would expand the first non-terminal to one or more potential shellcodes. We then expand these in the same fashion, and so on until no further new states are added; we then have the set of all potential shellcodes given the starting shellcode and the provided grammar. Obviously, there can be problems storing the states if the number grows exponentially on each expansion, but this is a common verification problem and there are a number of approaches to resolve it, varying from “hope it doesn’t happen” to using a compact storage mechanism like binary decision diagrams. We can then extract the states that contain no non-terminals, i.e. they are a valid x86 instruction stream, conjoin this with the path condition and ask our SAT/SMT solver to enumerate the shellcodes that satisfy these conditions.

There is a bit of a snag with this fixed point approach though. What if our grammar contains an expansion A ->* aB , which states that A expands to, some terminal, a and, some non-terminal, B in one or more steps, and also the rule B ->* A? In this case we are no longer guaranteed to reach a fixed point, as we can enter an infinite loop. The obvious solution is to transform our grammar into one that doesn’t contain such rules. We can do this by setting an upper bound, k, on the number of such recursive iterations allowed and including these expansions as new non-terminals. This new grammar has lost some of the potential outcomes of the original but it can now be processed using our fixed point approach. It should be noted that this upper bound on the number of self-recursions isn’t quite as artificial or as limiting as it may seem. There will always be some upper bound on the size of the buffer used for shellcode and thus it may be practical, in some cases, to use quite a low value of k.

(In fact, for a real world implementation it is well worth considering how to involve the length of the buffer we intend to use as we perform the fixed point computation. There is no reliable way to favour shorter shellcodes that also satisfy the program constraints, but one optimisation would be to exclude any shellcodes that have already exceeded the length limit from future expansions.)

Fun uses for an SMT solver

An SMT solver (such as Z3, Yices or STP) is a decision procedure that can handle various types of arithmetic and other decidable theories. They make use of procedures specific to the theories they handle, such as linear arithmetic, in combination with the brute force power of a SAT solver. At a high level, proceed by iteratively by replacing the sub-expressions in a formula like (x + y < 10 || x > 9) && (y + z < 5) with propositional variables, to give something like (A || B) && (C). At this point we can apply a SAT solver to look for a solution. If none exists then we need not bother with analysing the abstracted expressions, as the formula is unsatisfiable. If the SAT solver finds a satisfiable solution we then restore the original expressions and pass the conjunction of this off to the core decision procedure which can deal with the semantics of the given theory. This process then proceeds in the standard DPLL method of iteration, involving finding UNSAT cores and backtracking. One of the best collection of resources on how they actually work is Leonardo De Moura’s MSR research page.

So, theory aside, how can we use this to entertain ourselves/do useful things? Well, an advantage of an SMT solver over a regular SAT solver is that we can quite easily express the sort of operations that tend to go on during program execution. We can model conditions, arithmetic and arrays. Using a theory that can model operations of this kind we can represent a path through a program (or several different paths in fact). By appending extra constraints we can then use an SMT solver to generate inputs that will take different conditional branches (useful for guiding a fuzzer), ensure memory locations have a specific value (useful for avoiding shellcode filters) and/or model conditions we want to check for, such as integer overflows.

A practical example may help illuminate why you might want to use an SMT solver. Consider the problem of a program containing an exploitable vulnerability where we have located a suitable buffer for shellcode but we now need to know what input to provide such that the desired shellcode is in that buffer when we jump to it. One solution is to use dynamic analysis to gather the entire path condition over user influenced data and then append the condition that the buffer we are interested in contains the shellcode. To do this we will gather all data movement and conditional instructions on tainted data (I won’t discuss how to do this here). At the vulnerability point we can then express this trace as the input to an SMT solver. Most solvers have their own API but they should also all accept a SMT-LIB formatted text file input, which is what I use so as not to tie myself to a single solver. You can read more about the format here, but essentially our input will have 4 sections.

Suppose, for the sake of having a tractable example, that our vulnerable program just takes two bytes of input and moves them once into a new location, and we want to determine what input to provide such that these new locations have the values 0x41 and 0x42. Our specification to the solver would then proceed as follows:

We begin with a pretty standard header that is basically static unless you want to use a different logic. I use quantified bitvector logic because it is easier to model mod32 arithmetic and data movement/conditionals at the byte and bit level than it would be with linear arithmetic or another logic.

(benchmark exploitSample
:status unknown
:logic QF_BV

Following this we then specify the name and type of all variables that we intend to use in the formula itself. The format of valid names is discussed in a document linked from the SMT-LIB website, but approximately follows the same rules as C.

Here I declare 4 variables, <i0, i1, n256, n257> (names unimportant), and I declare them to be bitvectors of site 8 e.g. they model a single byte

:extrafuns ((n256 BitVec[8])(i0 BitVec[8])(n257 BitVec[8])(i1 BitVec[8]))

Next is the assumptions section. Here we can specify the path condition, i.e. all data movement and conditionals that occured during the dynamic trace. We could just as easily express these in the next section but for ease of comprehension I use the assumptions section (according to the docs some parsers might also work slightly faster this way).

The format of this section should be familiar with anyone that has dabbled in programming languages that are list orientated. It is pretty much (operator operand1 operand2 ....)

This assumption is basically the expression of the conjuction (n256 := i0) AND (n257 := i1)

:assumption (and (= n256 i0)(= n257 i1)

Finally, we have our formula. This is of the same format as the assumption section, but I usually use it to express the part of the problem I want to solve. e.g. I encode the shellcode here.

(The form for a bitvector constant is bvDECIMAL_VAL[SIZE_OF_VECTOR])

:formula (and (= n256 bv65[8])(= n257 bv66[8]))

Obviously, this is a trivial example and we can quite easily spot the solution, but in a situation where there are 30+ movements of every byte of input, as well as conditionals and arithmetic, it quickly becomes impossible to solve by hand. I’ve used this technique to produce satisfying inputs for formulae over hundreds of input variables in a matter of seconds. As for the actual running, we can concatenate the above sections into a text file (probably best to use a .smt extension as some solvers seem to look for it) and invoke our solver to get something like the following:

nnp@test:~/Desktop/yices-1.0.21/bin/$ yices -smt -e < constraints.smt 
(= i0 0b01000001)
(= i1 0b01000010)

Which can be parsed back to a C/Python/etc array using a relatively simple script.

This approach is exactly what I’m doing to auto-generate my exploits. In the assumptions, I specify everything I’ve traced during program execution, and in the formula I specify the shellcode and the desired location I want to use it in (determined by the method described in a previous post of mine), as well as the address of a shellcode trampoline. By also including the information on what the original user input was, the SMT solver can produce an output with the exploit information at the desired indexes and valid input at all others. The finished product is currently something that looks like this:

exploitArray = ['\x99\x31\xc0\x52\x68\x6e\x2f\x73\x68\x68\x2f\x2f\x62\x69\x89\

oFile = open('pyExploit.in', 'w')

Which was produced from an input file containing only ‘B’s, the required shellcode and a set of potential trampoline addresses. The tool has determined that at the vulnerability point the ESP register points to a buffer containing data from the start of the user input, and thus has filled in the start of the input with the shellcode. It has also found the correct bytes that overwrite the EIP and replaced them with the address of a jmp %esp (0x08048439)