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.:: Automated vulnerability auditing in machine code ::.

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Current issue : #64 | Release date : 2007-05-27 | Editor : The Circle of Lost Hackers
IntroductionThe Circle of Lost Hackers
Phrack Prophile of the new editorsThe Circle of Lost Hackers
Phrack World NewsThe Circle of Lost Hackers
A brief history of the Underground sceneDuvel
Hijacking RDS TMC traffic information signallcars & danbia
Attacking the Core: Kernel Exploitation Notestwiz & sgrakkyu
The revolution will be on YouTubegladio
Automated vulnerability auditing in machine codeTyler Durden
The use of set_head to defeat the wildernessg463
Cryptanalysis of DPA-128sysk
Mac OS X Wars - A XNU Hopenemo
Hacking deeper in the systemscythale
The art of exploitation: Autopsy of cvsxplAc1dB1tch3z
Know your enemy: Facing the copsLance
Remote blind TCP/IP spoofingklm
Hacking your brain: The projection of consciousnesskeptune
International scenesVarious
Title : Automated vulnerability auditing in machine code
Author : Tyler Durden
		  Automated vulnerability auditing in machine code

             		Tyler Durden <tyler@phrack.org> 

		              Phrack Magazine #64
                  		
		             Version of May 22 2007

					

I. Introduction
   a/ On the need of auditing automatically
   b/ What are exploitation frameworks
   c/ Why this is not an exploitation framework
   d/ Why this is not fuzzing
   e/ Machine code auditing : really harder than sources ? 

II. Preparation
   a/ A first intuition 
   b/ Static analysis vs dynamic analysis
   c/ Dependences & predicates
       - Controlflow analysis
       - Dataflow analysis
   d/ Translation to intermediate forms
   e/ Induction variables (variables in loops)     

III. Analysis
   a/ What is a vulnerability ?
   b/ Buffer overflows and numerical intervals
	- Flow-insensitive
	- Flow-sensitive
	- Accelerating the analysis by widening
   c/ Type-state checking
	- Memory leaks
	- Heap corruptions	
   d/ More problems
	- Predicate analysis
	- Alias analysis and naive solutions
	- Hints on detecting race conditions

IV. Chevarista: an analyzer of binary programs
   a/ Project modelization
   b/ Program transformation
   c/ Vulnerability checking
   d/ Vulnerable paths extraction
   e/ Future work : Refinement

V. Related Work
   a/ Model Checking
   b/ Abstract Interpretation

VI. Conclusion
VII. Greetings
VIII. References
IX. The code
      

	.::###########################################################::.


Software have bugs. That is quite a known fact.



----------------------[ I. Introduction



In this article, we will discuss the design of an engine for automated 
vulnerability analysis of binary programs. The source code of the 
Chevarista static analyzer is given at the end of this document.

The purpose of this paper is not to disclose 0day vulnerability, but
to understand how it is possible to find them without (or with
restricted) human intervention. However, we will not friendly provide
the result of our automated auditing on predefined binaries : instead
we will always take generic examples of the most common difficulties 
encountered when auditing such programs. Our goal is to enlight the 
underground community about writing your own static analyzer and not
to be profitful for security companies or any profit oriented organization.

Instead of going straight to the results of the proposed implementation, 
we may introduce the domain of program analysis, without going deeply
in the theory (which can go very formal), but taking the perspective
of a hacker who is tired of focusing on a specific exploit problem
and want to investigate until which automatic extend it is possible
to find vulnerabilities and generate an exploit code for it without
human intervention.

Chevarista hasnt reached its goal of being this completely automated
tool, however it shows the path to implement incrementally such tool
with a genericity that makes it capable of finding any definable kind 
of vulnerability.

Detecting all the vulnerabilities of a given program can be
untractable, and this for many reasons. The first reason is that
we cannot predict that a program running forever will ever have
a bug or not. The second reason is that if this program ever stop,
the number of states (as in "memory contexts") it reached and passed
through before stopping is very big, and testing all of of possible
concrete program paths would either take your whole life, or a dedicated 
big cluster of machine working on this for you during ages.

As we need more automated systems to find bugs for us, and we do not
have such computational power, we need to be clever on what has to be 
analysed, how generic can we reason about programs, so a single small 
analyzer can reason about a lot of different kinds of bugs. After all, 
if the effort is not worth the genericity, its probably better to audit 
code manually which would be more productive. However, automated systems
are not limited to vulnerability findings, but because of their tight 
relation with the analyzed program, they can find the exact conditions 
in which that bug happens, and what is the context to reach for triggering it.

But someone could interject me : "But is not Fuzzing supposed to do
that already ?". My answer would be : Yes. But static analysis is
the intelligence inside Fuzzing. Fuzzy testing programs give very
good results but any good fuzzer need to be designed with major static
analysis orientations. This article also applies somewhat to fuzzing
but the proposed implementation of the Chevarista analyzer is not 
a fuzzer. The first reason is that Chevarista does not execute the
program for analyzing it. Instead, it acts like a (de)compiler but 
perform analysis instead of translating (back) to assembly (or source) code.
It is thus much more performant than fuzzing but require a lot of
development and litterature review for managing to have a complete
automatic tool that every hacker dream to maintain.

Another lost guy will support : "Your stuff looks more or less like an
exploitation framework, its not so new". Exploitation frameworks
are indeed not very new stuffs. None of them analyze for vulnerabilities,
and actually only works if the builtin exploits are good enough. When
the framework aims at letting you trigger exploits manually, then it
is not an automated framework anymore. This is why Chevarista is not
CORE-Impact or Metasploit : its an analyzer that find bugs in programs
and tell you where they are.

One more fat guy in the end of the room will be threatening: "It is simply
not possible to find vulnerabilities in code without the source .." and
then a lot of people will stand up and declare this as a prophety, 
because its already sufficiently hard to do it on source code anyway.
I would simply measure this judgement by several remarks: for some
peoples, assembly code -is- source code, thus having the assembly is
like having the source, without a certain number of information. That
is this amount of lost information that we need to recover when writing
a decompiler. 

First, we do not have the name of variables, but naming variables in a different
way does not affect the result of a vulnerability analysis. Second, we do not have
the types, but data types in compiled C programs do not really enforce properties 
about the variables values (because of C casts or a compiler lacking strong type 
checking). The only real information that is enforced is about variable size in
memory, which is recoverable from an assembly program most of the time. This
is not as true for C++ programs (or other programs written in higher level
objects-oriented or functional languages), but in this article we will 
mostly focuss on compiled C programs.

A widely spread opinion about program analysis is that its harder to 
acheive on a low-level (imperative) language rather than a high-level 
(imperative) language. This is true and false, we need to bring more 
precision about this statement. Specifically, we want to compare the
analysis of C code and the analysis of assembly code:


 ---------------------------------------------------------------------
| Available information   |      C code         |    Assembly code    |
|---------------------------------------------------------------------|
| Original variables names|    Yes (explicit)   |         No          |
|---------------------------------------------------------------------|
|   Original types names  |    Yes (explicit)   |         No          |
|---------------------------------------------------------------------|
|  Control Sequentiality  |    Yes (explicit)   |    Yes (explicit)   |
|---------------------------------------------------------------------|
|  Structured control     |    Yes (explicit)   |    Yes (recoverable)|
|---------------------------------------------------------------------|
|    Data dependencies    |    Yes (implicit)   |    Yes (implicit)   |
|---------------------------------------------------------------------|
|    Data Types           |    Yes (explicit)   |    Yes (recoverable)|
|---------------------------------------------------------------------|
|    Register transfers   |    No               |    Yes (explicit)   |
|---------------------------------------------------------------------|
|  Selected instructions  |    No               |    Yes (explicit)   |
 ---------------------------------------------------------------------

Lets discuss those points more in details:

 - The control sequentiality is obviously kept in the assembly, else
the processor would not know how to execute the binary program.
However the binary program does not contain a clearly structured
tree of execution. Conditionals, but especially, Loops, do not appear
as such in the executable code. We need a preliminary analysis for
structuring the control flow graph. This was done already on source
and binary code using different algorithms that we do not present
in this article.

- Data dependencies are not explicit even in the source program, however
we can compute it precisely both in the source code and the binary code.
The dataflow analysis in the binary code however is slightly different,
because it contains every single load and store between registers and
the memory, not only at the level of variables, as done in the source
program. Because of this, the assembly programs contains more instructions
than source programs contain statements. This is an advantage and a
disadvantage at the same time. It is an advantage because we can track
the flow in a much more fine-grained fashion at the machine level, and
that is what is necessary especially for all kind of optimizations, 
or machine-specific bugs that relies on a certain variable being either
in the memory or in a register, etc. This is a disadvantage because we 
need more memory to analyse such bigger program listings.

- Data types are explicit in the source program. Probably the recovery
of types is the hardest information to recover from a binary code. 
However this has been done already and the approach we present in this
paper is definitely compatible with existing work on type-based
decompilation. Data types are much harder to recover when dealing with
real objects (like classes in compiled C++ programs). We will not deal
with the problem of recovering object classes in this article, as we 
focuss on memory related vulnerabilities.

- Register level anomalies can happen [DLB], which can be useful for a 
hacker to determine how to create a context of registers or memory when 
writing exploits. Binary-level code analysis has this advantage that it 
provides a tighter approach to exploit generation on real world existing 
targets.

- Instruction level information is interested again to make sure we dont
miss bugs from the compiler itself. Its very academically well respected
to code a certified compiler which prove the semantic equivalence between
source code and compiled code but for the hacker point of view, it does 
not mean so much. Concrete use in the wild means concrete code,
means assembly. Additionally, it is rarer but it has been witnessed
already some irregularities in the processor's execution of specific
patterns of instructions, so an instruction level analyzer can deal with
those, but a source level analyzer cannot. A last reason I would mention
is that the source code of a project is very verbose. If a code analyzer
is embedded into some important device, either the source code of the
software inside the device will not be available, or the device will lack
storage or communication bandwidth to keep an accessible copy of the source
code. Binary code analyzer do not have this dependencie on source code and
can thus be used in a wider scope.


To sum-up, there is a lot of information recovery work before starting to
perform the source-like level analysis. However, the only information
that is not available after recovery is not mandatory for analysing
code : the name of types and variables is not affecting the 
execution of a program. We will abstract those away from our analysis
and use our own naming scheme, as presented in the next chapter of this 
article.




-------------[ II. Preparation




We have to go on the first wishes and try to understand better what
vulnerabilities are, how we can detect them automatically, are we
really capable to generate exploits from analyzing a program that we
do not even execute ? The answer is yes and no and we need to make 
things clear about this. The answer is yes, because if you know exactly
how to caracterize a bug, and if this bug is detectable by any 
algorithm, then we can code a program that will reason only about
those known-in-advance vulnerability specificities and convert the 
raw assembly (or source) code into an intermediate form that will make
clear where the specificities happens, so that the "signature" of the
vulnerability can be found if it is present in the program. The answer
is no, because giving an unknown vulnerability, we do not know in
advance about its specificities that caracterize its signature. It
means that we somewhat have to take an approximative signature and 
check the program, but the result might be an over-approximation (a
lot of false positives) or an under-approximation (finds nothing or
few but vulnerabilities exist without being detected).

As fuzzing and black-box testing are dynamic analysis, the core of 
our analyzer is not as such, but it can find an interest to run the 
program for a different purpose than a fuzzer. Those try their 
chance on a randomly crafted input. Fuzzer does not have a *inner*
knowledge of the program they analyze. This is a major issue because
the dynamic analyzer that is a fuzzer cannot optimize or refine
its inputs depending on what are unobservable events for him. A fuzzer
can as well be coupled with a tracer [AD] or a debugger, so that fuzzing 
is guided by the debugger knowledge about internal memory states and 
variable values during the execution of the program.

Nevertheless, the real concept of a code analysis tool must be an integrated 
solution, to avoid losing even more performance when using an external 
debugger (like gdb which is awfully slow when using ptrace). Our 
technique of analysis is capable of taking decisions depending on 
internal states of a program even without executing them. However, our 
representation of a state is abstract : we do not compute the whole 
content of the real memory state at each step of execution, but consider
only the meaningful information about the behavior of the program by automatically 
letting the analyzer to annotate the code with qualifiers such as : "The next 
instruction of the will perform a memory allocation" or "Register R or memory cell 
M will contain a pointer on a dynamically allocated memory region". We will explain
in more details heap related properties checking in the type-state analysis
paragraph of Part III.

In this part of the paper, we will describe a family of intermediate forms
which bridge the gap between code analysis on a structured code, and code
analysis on an unstructured (assembly) code. Conversion to those intermediate
forms can be done from binary code (like in an analyzing decompiler) or from
source code (like in an analyzing compiler). In this article, we will
transform binary code into a program written in an intermediate form, and then
perform all the analysis on this intermediate form. All the studies properties
will be related to dataflow analysis. No structured control flow is necessary
to perform those, a simple control flow graph (or even list of basic blocks
with xrefs) can be the starting point of such analysis.

Lets be more  concrete a illustrate how we can analyze the internal states of
a program without executing it. We start with a very basic piece of code:

	
Stub 1:
-------
			 o			o  : internal state
 if (a)		        / \		
   b++;		->     o   o			/\ : control-flow splitting 
 else		        \ /			\/ : control-flow merging
   c--;	               	 o
-------                        


In this simplistic example, we represent the program as a graph whoose
nodes are states and edges are control flow dependencies. What is an internal
state ? If we want to use all the information of each line of code,	
we need to make it an object remembering which variables are used and modified 
(including status flags of the processors). Then, each of those control state
perform certains operations before jumping on another part of the code (represented
by the internal state for the if() or else() code stubs). Once the if/else 
code is finished, both paths merge into a unique state, which is the state after
having executed the conditional statement. Depending how abstract is the analysis,
the internal program states will track more or less requested information at each
computation step. For example, once must differentiate a control-flow analysis 
(like in the previous example), and a dataflow analysis.

Imagine this piece of code:


Stub 2:
-------

Code			Control-flow		  Data-flow with predicates

                                                           a
						        ---o--- 
                                                       /    \  \
                                                      /      \  \
						     /  c     \  \
c = 21;			    o		            |   o    b o  \
b = a;			    |			    |  / \    /    \ 
a = 42;			    o			     \/   ------   /
if (b != c)		   / \		             /\  |b != c| /  
  a++;			  o   o  		    /  \  ------ /
else			   \ /                     /    \ /   \ /
  a--;                      o                     |    a o   a o
c += a;                     |                      \     |    /
-------                     o                       \    |   /
			                             \   |  /
						      \	 | / 
                                                       c o 	
                                                         |
						       (...)
                            

In a dataflow  graph, the nodes are the variables, and the arrow are the
dependences between variables. The control-flow and data-flow graphs are
actually complementary informations. One only cares about the sequentiality
in the graph, the other one care about the dependences between the variables
without apparently enforcing any order of evaluation. Adding predicates
to a dataflow graph helps at determining which nodes are involved in a
condition and which instance of the successors data nodes (in our case, 
variable a in the if() or the else()) should be considered for our 
analysis.

As you can see, even a simple data-flow graph with only few variables
starts to get messy already. To clarify the reprensentation of the 
program we are working on, we need some kind of intermediate representation
that keep the sequentiality of the control-flow graph, but also provide the
dependences of the data-flow graph, so we can reason on both of them
using a single structure. We can use some kind of "program dependence graph"
that would sum it up both in a single graph. That is the graph we will consider
for the next examples of the article.

Some intermediate forms introduces special nodes in the data-flow graph, and
give a well-recognizable types to those nodes. This is the case of Phi() and
Sigma() nodes in the Static Single Assignment [SSA] and Static Single 
Information [SSI] intermediate forms and that facilitates indeed the reasoning
on the data-flow graph. Additionally, decomposing a single variable into
multiple "single assignments" (and multiple single use too, in the SSI form),
that is naming uniquely each apparition of a given variable, help at desambiguizing 
which instance of the variable we are talking about at a given point of the program:



Stub 2 in SSA form	Stub 2 in SSI form	Data-flow graph in SSI form
------------------	------------------	--------------------------

c1 = 21;		c1 = 21;			              o a1
b1 = a1;		b1 = a1;			             / \
if (b1 != c1)		(a3, a4) = Sigma(a2);  (a3, a4) = Sigma(a2) o   o b1
  a2 = a1 + 1;		if (b1 != c1)                              /|
else			  a3 = a2 + 1;                            / |
                                                                 /  | 
                                                                /   |    
                                                               /    |    o c1
  a3 = a1 - 1;		else                                   |    |    |
a4 = Phi(a2, a3)	  a4 = a2 - 1;                      a3 o    o a4 |
c2 = c1 + a4;		a5 = Phi(a3, a4);                       \   |    |
			c2 = c1 + a5;                            \  |    |
----------------        -------------------                       \ |    |
                                                                   \|    |
                                                  a5 = Phi(a3, a4)  o    |
                                                                     \  /
                                                                      o c2
                                                                      .
                                                                      .
                                                                      .

      
Note that we have not put the predicates (condition test) in that graph. In
practice, its more convenient to have additional links in the graph, for 
predicates (that ease the testing of the predicate when walking on the graph),
but we have removed it just for clarifying what is SSA/SSI about.

Those "symbolic-choice functions" Phi() and Sigma() might sound a little bit
abstract. Indeed, they dont change the meaning of a program, but they capture
the information that a given data node has multiple successors (Sigma) or
ancestors (Phi). The curious reader is invited to look at the references for
more details about how to perform the intermediate translation. We will here 
focuss on the use of such representation, especially when analyzing code 
with loops, like this one:


		Stub 3 C code		Stub 3 in Labelled SSI form        
		-------------           ---------------------------       

		int a = 42;	        int a1 = 42;
		int i = 0;              int i1 = 0;

					P1 = [i1 < a1]
                        		(<i4:Loop>, <i9:End>) = Sigma(P1,i2);
					(<a4:Loop>, <a9:End>) = Sigma(P1,a2);
			
		while (i < a)           
		{                 =>    Loop:
                       			 a3 = Phi(<BLoop:a1>, <BEnd:a5>);
					 i3 = Phi(<BLoop:i1>, <BEnd:i5>);
  		  a--;                   a5 = a4 - 1;
  		  i++;                   i5 = i4 + 1;
					 P2 = [i5 < a5]
					 (<a4:Loop>, <a9:End>) = Sigma(P2,a6);
		        		 (<i4:Loop>, <i9:End>) = Sigma(P2,i6);
		} 
                        		End:
                        		 a8 = Phi(<BLoop:a1>, <Bend:a5>);
                        		 i8 = Phi(<BLoop:i1>, <Bend:i5>);
		a += i;                  a10 = a9 + i9;
		-----------             ---------------------------------



By trying to synthetize this form a bit more (grouping the variables
under a unique Phi() or Sigma() at merge or split points of the control
flow graph), we obtain a smaller but identical program. This time,
the Sigma and Phi functions do not take a single variable list in parameter,
but a vector of list (one list per variable):


		Stub 3 in Factored & Labelled SSI form        
		--------------------------------------

		int a1 = 42;
		int i1 = 0;

		P1 = [i1 < a1]

		(<i4:Loop>, <i9:End>)            (i2)
		(		    ) = Sigma(P1,(  ));
		(<a4:Loop>, <a9:End>)            (a2)
	   
			
		Loop:

		(a3)      (<BLoop:a1>, <BEnd:a5>)
		(  ) = Phi(                     );
		(i3)      (<BLoop:i1>, <BEnd:i5>)

		a5 = a4 - 1;
		i5 = i4 + 1;

		P2 = [i5 < a5]

		(<a4:Loop>, <a9:End>)             (a6)
		(                   ) = Sigma(P2, (  ));
		(<i4:Loop>, <i9:End>)             (i6)

		End:

		(a8)      (<BLoop:a1>, <Bend:a5>)
		(  ) = Phi(                     );
		(i8)      (<BLoop:i1>, <Bend:i5>)

		a10 = a9 + i9;
		----------------------------------------



How can we add information to this intermediate form ? Now the Phi()
and Sigma() functions allows us to reason about forward dataflow
(in the normal execution order, using Sigma) and backward dataflow 
analysis (in the reverse order, using Phi). We can easily find the
inductive variables (variables that depends on themselves, like the
index or incrementing pointers in a loop), just using a simple analysis:

Lets consider the Sigma() before each Label, and try to iterate its 
arguments:


		
		(<a4:Loop>, <a9:End>)             (a6)
		(                   ) = Sigma(P2, (  ));
		(<i4:Loop>, <i9:End>)             (i6)
		

	->	(<a5:Loop>,<a10:End>)
		(                   )
		(<i5:Loop>,   _|_   )


	->      (<a6:Loop>,   _|_   )
		(                   )
		(<i6:Loop>,   _|_   )



We take _|_ ("bottom") as a notation to say that a variable
does not have any more successors after a certain iteration
of the Sigma() function.

After some iterations (in that example, 2), we notice that 
the left-hand side and the right-hand side are identical 
for variables a and i. Indeed, both side are written given
a6 and i6. In the mathematical jargon, that is what is called
a fixpoint (of a function F) : 

	F(X) = X

or in this precise example:

	a6 = Sigma(a6)

By doing that simple iteration-based analysis over our 
symbolic functions, we are capable to deduce in an automated
way which variables are inductives in loops. In our example,
both a and i are inductive. This is very useful as you can imagine, 
since those variables become of special interest for us, especially 
when looking for  buffer overflows that might happen on buffers in 
looping code.

We will now somewhat specialize this analysis in the following
part of this article, by showing how this representation can
apply to



-------------------[ III. Analysis



	The previous part of the article introduced various notions
in program analysis. We might not use all the formalism in the future
of this article, and focuss on concrete examples. However, keep in
mind that we reason from now for analysis on the intermediate form
programs. This intermediate form is suitable for both source code
and binary code, but we will keep on staying at binary level for our
examples, proposing the translation to C only for understanding
purposes. Until now, we have shown our to understand data-flow analysis
and finding inductive variables from the (source or binary) code of 
the program. 

So what are the steps to find vulnerabilities now ?

A first intuition is that there is no generic definition for a 
vulnerability. But if we can describes them as behavior that 
violates a certain precise property, we are able to state if a 
program has a vulnerability or not. Generally, the property depends
on the class of bugs you want to analyse. For instance, properties 
that express buffer overflow safety or property that express a heap 
corruption (say, a double free) are different ones. In the first case, 
we talk about the indexation of a certain memory zone which has to never
go further the limit of the allocated memory. Additionally, for
having an overflow, this must be a write access. In case we have a
read access, we could refer this as an info-leak bug, which 
may be blindly or unblindly used by an attacker, depending if the
result of the memory read can be inspected from outside the process
or not. Sometimes a read-only out of bound access can also be used
to access a part of the code that is not supposed to be executed
in such context (if the out-of-bound access is used in a predicate).
In all cases, its interesting anyway to get the information by our 
analyzer of this unsupposed behavior, because this might lead to a 
wrong behavior, and thus, a bug.

In this part of the article, we will look at different class of
bugs, and understand how we can caracterize them, by running very
simple and repetitive, easy to implement, algorithm. This algorithm
is simple only because we act on an intermediate form that already
indicates the meaningful dataflow and controlflow facts of the
program. Additionally, we will reason either forward or backward,
depending on what is the most adapted to the vulnerability.

We will start by an example of numerical interval analysis and show
how it can be useful to detect buffer overflows. We will then show
how the dataflow graph without any value information can be useful
for finding problems happening on the heap. We will enrich our 
presentation by describing a very classic problem in program analysis,
which is  the discovery of equivalence between pointers (do they point
always on the same variable ? sometimes only ? never ?), also known as
alias analysis. We will explain why this analysis is mandatory for any
serious analyzer that acts on real-world programs. Finally, we will
give some more hints about analyzing concurrency properties inside
multithread code, trying to caracterize what is a race condition.



------------[ A. Numerical intervals




	When looking for buffer overflows or integer overflows, the 
mattering information is about the values that can be taken by 
memory indexes or integer variables, which is a numerical value.

Obviously, it would not be serious to compute every single possible
value for all variables of the program, at each program path : this
would take too much time to compute and/or too much memory for the values
graph to get mapped entirely.

By using certain abstractions like intervals, we can represent the set
of all possible values of a program a certain point of the program. We
will illustrate this by an example right now. The example itself is
meaningless, but the interesting point is to understand the mecanized
way of deducing information using the dataflow information of the program
graph.


We need to start by a very introductionary example, which consists of
finding


Stub 4					Interval analysis of stub 4
-------					---------------------------

int  a, b;	

b = 0;					b = [0 to 0]
if (rand())		 
 b--;					b = [-1 to -1]
else
 b++;					b = [1 to 1]

					After if/else:

					b = [-1 to 1]

a = 1000000 / b;			a = [1000000 / -1 to 1000000 / 1] 
					    [Reported Error: b can be 0]


In this example, a flow-insensitive analyzer will merge the interval of values
at each program control flow merge. This is a seducing approach as you need to
pass a single time on the whole program to compute all intervals. However, this
approach is untractable most of the time. Why ? In this simple example, the
flow-insensitive analyzer will report a bug of potential division by 0, whereas
it is untrue that b can reach the value 0 at the division program point. This
is because 0 is in the interval [-1 to 1] that this false positive is reported
by the analyzer. How can we avoid this kind of over-conservative analysis ?

We need to introduce some flow-sensitiveness to the analysis, and differentiate
the interval for different program path of the program. If we do a complete flow 
sensitive analysis of this example, we have:


Stub 4					Interval analysis of stub 4
-------					---------------------------

int  a, b;	

b = 0;					b = [0 to 0]
if (rand())		 
 b--;					b = [-1 to -1]
else
 b++;					b = [1 to 1]

					After if/else:

					b = [-1 to -1 OR 1 to 1]

a = 1000000 / b;			a = [1000000 / -1 to 1000000 / -1] or 
					    [1000000 /  1 to 1000000 /  1] 
					  = {-1000000 or 1000000}


Then the false positive disapears. We may take care of avoiding to be flow sensitive
from the beginning. Indeed, if the flow-insensitive analysis gives no bug, then no
bugs will be reported by the flow-sensitive analysis either (at least for this example).
Additionally, computing the whole flow sensitive sets of intervals at some program point
will grow exponentially in the number of data flow merging point (that is, Phi() function
of the SSA form). 

For this reason, the best approach seems to start with a completely flow insensitive, 
and refine the analysis on demand. If the program is transforted into SSI form, then 
it becomes pretty easy to know which source intervals we need to use to compute the 
destination variable interval of values. We will use the same kind of analysis for 
detecting buffer overflows, in that case the interval analysis will be used on the 
index variables that are used for accessing memory at a certain offset from a given 
base address.

Before doing this, we might want to do a remark on the choice of an interval abstraction
itself. This abstraction does not work well when bit swapping is involved into the 
operations. Indeed, the intervals will generally have meaningless values when bits are
moved inside the variable. If a cryptographic operation used bit shift that introduces 0 
for replacing shifted bits, that would not be a a problem, but swapping bits inside a given 
word is a problem, since the output interval is then meaningless.


 ex:
        c = a | b		(with A, B, and C integers)
	c = a ^ b
	c = not(c)


Giving the interval of A and B, what can we deduce for the intervals of C ? Its less trivial
than a simple numerical change in the variable. Interval analysis is not very well adapted
for analyzing this kind of code, mostly found in cryptographic routines.

We will now analyze an example that involves a buffer overflow on the heap. Before
doing the interval analysis, we will do a first pass to inform us about the statement
related to memory allocation and disallocation. Knowing where memory is allocated
and disallocated is a pre-requirement for any further bound checking analysis.


Stub 5					Interval analysis with alloc annotations
------					----------------------------------------

char *buf;				buf = _|_ (uninitialized)
int   n = rand();			n   = [-Inf, +Inf]
buf = malloc(n)				buf = initialized of size [-Inf to Inf]
i   = 0;				i   = [0,0], [0,1] ... [0,N]

while (i <= n)				      
{		
  assert(i < N)			    
  buf[i] = 0x00;			
  	
  i++;					i   = [0,1], [0,2] ... [0,N]
					     (iter1  iter2 ... iterN)
}
return (i);


Lets first explain that the assert() is a logical representation in the intermediate
form, and is not an assert() like in C program. Again, we never do any dynamic analysis
but only static analysis without any execution. In the static analysis of the intermediate
form program, a some point the control flow will reach a node containing the assert statement.
In the intermediate (abstract) word, reaching an assert() means performing a check on the
abstract value of the predicate inside the assert (i < N). In other words, the analyzer
will check if the assert can be false using interval analysis of variables, and will print
a bug report if it can. We can also let the assert() implicits, but representing them
explicitely make the analysis more generic, modular, and adaptable to the user.

As you can see, there is a one-byte-overflow in this example. It is pretty trivial
to spot it manually, however we want to develop an automatic routine  for doing
it. If we deploy the analysis that we have done in the previous example, the assert()
that was automatically inserted by the analyzer after each memory access of the program 
will fail after N iterations. This is because arrays in the C language start with index 0 and 
finish with an index inferior of 1 to their allocated size. Whatever kind of 
code will be inserted between those lines (except, of course, bit swapping as 
previously mentioned), we will always be able to propagate the intervals and find
that memory access are done beyond the allocated limit, then finding a clear
memory leak or memory overwrite vulnerability in the program.

However, this specific example brings 2 more questions:

	- We do not know the actual value of N. Is it a problem ? If we 
	manage to see that the constraint over the index of buf is actually
	the same variable (or have the same value than) the size of the
	allocated buffer, then it is not a problem. We will develop this in 
	the alias analysis part of this article when this appears to be a
	difficulty. 

	- Whatever the value of N, and provided we managed to identify N
	all definitions and use of the variable N, the analyzer will require N
	iteration over the loop to detect the vulnerability. This is not
	acceptable, especially if N is very big, which in that case many
	minuts will be necessary for analysing this loop, when we actually
	want an answer in the next seconds.

The answer for this optimization problem is a technique called Widening, gathered
from the theory of abstract interpretation. Instead of executing the loop N
times until the loop condition is false, we will directly in 1 iteration go to
the last possible value in a certain interval, and this as soon as we detect a
monotonic increase of the interval. The previous example would then compute
like in:

Stub 5					Interval analysis with Widening
------					-------------------------------

char *buf;				buf = _|_ (uninitialized)
int   n = rand();			n   = [-Inf, +Inf]
buf = malloc(n)				buf = initialized of size [-Inf to Inf]
i   = 0;				i = [0,0]

while (i <= n)
{
  assert(i < N); 			    iter1  iter2 iter3 iter4  ASSERT!
  buf[i] = 0x00;			i = [0,0], [0,1] [0,2] [0,N] 	
  i++;					i = [0,1], [0,2] [0,3] [0,N] 
}
return (i);


Using this test, we can directly go to the biggest possible interval in only 
a few iterations, thus reducing drastically the requested time for finding
the vulnerability. However this optimization might introduce additional
difficulties when conditional statement is inside the loop:


Stub 6					Interval analysis with Widening
------					-------------------------------

char *buf;				buf = _|_ (uninitialized)
int   n = rand() + 2;			n   = [-Inf, +Inf]
buf = malloc(n)				buf = initialized of size [-Inf to Inf]
i   = 0;				i = [0,0]

while (i <= n)				i = [0,0] [0,1] [0,2] [0,N] [0,N+1]
{
  if (i < n - 2)		        i = <same than previously for all iterations>
  {
    assert(i < N - 1)			[Never triggered !]
    buf[i] = 0x00;  			i = [0,0] [0,1] [0,2] [0,N] <False positive>    
  }			
  i++;					i = [0,1] [0,2] [0,3] [0,N] [0,N+1]	
}
return (i);


In this example, we cannot assume that the interval of i will be the same everywhere
in the loop (as we might be tempted to do as a first hint for handling intervals in
a loop). Indeed, in the middle of the loop stands a condition (with predicate being 
i < n - 2) which forbids the interval to grow in some part of the code. This is problematic 
especially if we decide to use widening until the loop breaking condition. We will miss
this more subtle repartition of values in the variables of the loop. The solution for this
is to use widening with thresholds. Instead of applying widening in a single time over the
entire loop, we will define a sequel of values which corresponds to "strategic points" of
the code, so that we can decide to increase precisely using a small-step values iteration.

The strategic points can be the list of values on which a condition is applied. In our case
we would apply widening until n = N - 2 and not until n = N. This way, we will not trigger
a false positive anymore because of an overapproximation of the intervals over the entire
loop. When each step is realized, that allows to annotate which program location is the subject
of the widening in the future (in our case: the loop code before and after the "if" statement).

Note that, when we reach a threshold during widening, we might need to apply a small-step
iteration more than once before widening again until the next threshold. For instance, 
when predicates such as (a != immed_value) are met, they will forbid the inner code of 
the condition to have their interval propagated. However, they will forbid this just one 
iteration (provided a is an inductive variable, so its state will change at next iteration) 
or multiple iterations (if a is not an inductive variable and will be modified only at another 
moment in the loop iterative abstract execution). In the first case, we need only 2 small-step
abstract iterations to find out that the interval continues to grow after a certain iteration.
In the second case, we will need multiple iteration until some condition inside the loop is
reached. We then simply needs to make sure that the threshold list includes the variable value
used at this predicate (which heads the code where the variable a will change). This way, we
can apply only 2 small-step iterations between those "bounded widening" steps, and avoid
generating false positives using a very optimized but precise abstract evaluation sequence.


In our example, we took only an easy example: the threshold list is only made of 2 elements (n
and (n - 2)). But what if a condition is realized using 2 variables and not a variable and 
an immediate value ? in that case we have 3 cases:

CASE1 - The 2 variables are inductive variables: in that case, the threshold list of the two variables 
must be fused, so widening do not step over a condition that would make it lose precision. This
seem to be a reasonable condition when one variable is the subject of a constraint that involve
a constant and the second variable is the subject of a constraint that involve the first variable:


Stub 7:						Threshold discovery
-------						-------------------

int a = MIN_LOWERBOUND;
int b = MAX_UPPERBOUND;
int i = 0;
int n = MAXSIZE;

while (i < n)					Found threshold n
{
  if (a < i < b)				Found predicate involving a and b
    (...)
  if (a > sizeof(something))			Found threshold for a
    i = b;
  else if (b + 1 < sizeof(buffer))		Found threshold for b
    i = a;
}


In that case, we can define the threshold of this loop being a list of 2 values,
one being sizeof(something), the other one being sizeof(buffer) or sizeof(buffer) - 1
in case the analyzer is a bit more clever (and if the assembly code makes it clear
that the condition applyes on sizeof(buffer) - 1).


CASE2 - One of the variable is inductive and the other one is not. 


So we have 2 subcases:

 - The inductive variable is involved in a predicate that leads to modification
   of the non-inductive variable. It is not possible without the 2 variables 
   being inductives !Thus we fall into the case 1 again.


 - The non-inductive variable is involved in a predicate that leads to
   modification of the inductive variable. In that case, the non-inductive
   variable would be invariant over the loop, which mean that a test between 
   its domain of values (its interval) and the domain of the inductive
   variable is required as a condition to enter the code stubs headed by the
   analyzed predicate. Again, we have 2 sub-subcases:

	* Either the predicate is a test == or !=. In that case, we must compute
	the intesection of both variables intervals. If  the intersection is void,
	the test will never true, so its dead code. If the intersection is itself
	an interval (which will be the case most of the time), it means that the
	test will be true over this inductive variable intervals of value, and 
	false over the remaining domain of values. In that case, we need to put
	the bounds of the non-inductive variable interval into the threshold list for 
	the widening of inductive variables that depends on this non-inductive 
	variable.
	

	* Or the predicate is a comparison : a < b (where a or b is an inductive
	variable). Same remarks holds : we compute the intersection interval 
	between a and b. If it is void, the test will always be true or false and
	we know this before entering the loop. If the interval is not void, we 
	need to put the bounds of the intersection interval in the widening threshold
	of the inductive variable.


CASE3 - None of the variables are inductive variables

In that case, the predicate that they define has a single value over the
entire loop, and can be computed before the loop takes place. We then can
turn the conditional code into an unconditional one and apply widening
like if the condition was not existing. Or if the condition is always
false, we would simply remove this code from the loop as the content of
the conditional statement will never be reached.

As you can see, we need to be very careful in how we perform the widening. If
the widening is done without thresholds, the abstract numerical values will
be overapproximative, and our analysis will generate a lot of false positives.
By introducing thresholds, we sacrify very few performance and gain a lot of 
precision over the looping code analysis. Widening is a convergence accelerator
for detecting problems like buffer overflow. Some overflow problem can happen
after millions of loop iteration and widening brings a nice solution for
getting immediate answers even on those constructs.

I have not detailed how to find the size of buffers in this paragraph. Wether
the buffers are stack or heap allocated, they need to have a fixed size at 
some point and the stack pointer must be substracted somewhere (or malloc
needs to be called, etc) which gives us the information of allocation 
alltogether with its size, from which we can apply our analysis. 

We will now switch to the last big part of this article, by explaining how
to check for another class of vulnerability.




------------[ B. Type state checking (aka double free, memory leaks, etc)



There are some other types of vulnerabilities that are slightly different to
check. In the previous part we explained how to reason about intervals of 
values to find buffer overflows in program. We presented an optimization
technique called Widening and we have studied how to weaken it for gaining
precision, by generating a threshold list from a set of predicates. Note that
we havent explicitely used what is called the "predicate abstraction", which
may lead to improving the efficiency of the analysis again. The interested
reader will for sure find resources about predicate abstraction on any good
research oriented search engine. Again, this article is not intended to give
all solutions of the problem of the world, but introduce the novice hacker
to the concrete problematic of program analysis.

In this part of the article, we will study how to detect memory leaks and
heap corruptions. The basic technique to find them is not linked with interval
analysis, but interval analysis can be used to make type state checking more
accurate (reducing the number of false positives). 

Lets take an example of memory leak to be concrete:


Stub 8:
-------

1. u_int off  = 0;
2. u_int ret  = MAXBUF;
3. char  *buf = malloc(ret);

4. do {
5.     off += read(sock, buf + off, ret - off);
6.     if (off == 0)
7.       return (-ERR);
8.     else if (ret == off)
9.       buf = realloc(buf, ret * 2);
10.} while (ret);

11. printf("Received %s \n", buf);
12. free(buf);
13. return;



In that case, there is no overflow but if some condition appears after the read, an error
is returned without freeing the buffer. This is not a vulnerability as it, but it can
help a lot for managing the memory layout of the heap while trying to exploit a heap
overflow vulnerability. Thus, we are also interested in detecting memory leak that
turns some particular exploits into powerful weapons.

Using the graphical representation of control flow and data flow, we can easily
find out that the code is wrong:


Graph analysis of Stub 8
------------------------


	o A				A: Allocation
	|				
	|
	o<----
        |     \  
        o      \
       / \      \
      /   \      \			R: Return
   R o     o REA /			REA: Realloc
      \   /     /
       \ /     /
        o     /
        |    /
        |   /
        |  /
        | /
        |/
        o
        |				F: Free
      F o
        | 
      R o				R: Return



Note that this representation is not a data flow graph but a
control-flow graph annotated with data allocation information for
the BUF variable. This allows us to reason about existing control 
paths and sequence of memory related events. Another way of doing 
this would have been to reason about data dependences together with
the predicates, as done in the first part of this article with the 
Labelled SSI form. We are not dogmatic towards one or another 
intermediate form, and the reader is invited to ponder by himself 
which representation fits better to his understanding. I invite
you to think twice about the SSI form which is really a condensed
view of lots of different information. For pedagogical purpose, we
switch here to a more intuitive intermediate form that express a 
similar class of problems.


Stub 8:
-------


0. #define PACKET_HEADER_SIZE 20

1. int   off  = 0;
2. u_int ret  = 10;
3. char  *buf = malloc(ret);				M

4. do {
5.     off += read(sock, buf + off, ret - off);
6.     if (off <= 0)
7.       return (-ERR);					R
8.     else if (ret == off)
9.       buf = realloc(buf, (ret = ret * 2));		REA
10.} while (off != PACKET_HEADER_SIZE);

11. printf("Received %s \n", buf);
12. free(buf);						F
13. return;						R


Using simple DFS (Depth-First Search) over the graph representing Stub 8, 
we are capable of extracting sequences like:


1,2,(3 M),4,5,6,8,10,11,(12 F),(12 R)		M...F...R	-noleak-

1,2,(3 M),4,(5,6,8,10)*,11,(12 F),(12 R)	M(...)*F...R	-noleak-

1,2,(3 M),4,5,6,8,10,5,6,(7 R)			M...R		-leak-

1,2,(3 M),(4,5,6,8,10)*,5,6,(7 R)		M(...)*R	-leak-

1,2,(3 M),4,5,6,8,(9 REA),10,5,6,(7 R)		M...REA...R	-leak-

1,2,(3 M),4,5,6,(7 R)				M...R		-leak-

etc

More generally, we can represent the set of all possible traces for
this example :


		1,2,3,(5,6,(7 | 8(9 | Nop)) 10)*,(11,12,13)*


with | meaning choice and * meaning potential looping over the events
placed between (). As the program might loop more than once or twice,
a lot of different traces are potentially vulnerable to the memory leak
(not only the few we have given), but all can be expressed using this
global generic regular expression over events of the loop, with respect
to this regular expression:


			.*(M)[^F]*(R)


that represent traces containing a malloc followed by a return without 
an intermediate free, which corresponds in our program to:


			.*(3)[^12]*(7)

		  =	.*(3).*(7)	 # because 12 is not between 3 and 7 in any cycle


In other words, if we can extract a trace that leads to a return after passing
by an allocation not followed by a free (with an undetermined number of states
between those 2 steps), we found a memory leak bug.

We can then compute the intersection of the global regular expression trace
and the vulnerable traces regular expression to extract all potential 
vulnerable path from a language of traces. In practice, we will not generate
all vulnerable traces but simply emit a few of them, until we find one that
we can indeed trigger. 

Clearly, the first two trace have a void intersection (they dont contain 7). So
those traces are not vulnerable. However, the next traces expressions match
the pattern, thus are potential vulnerable paths for this vulnerability.

We could use the exact same system for detecting double free, except that
our trace pattern would be :

			
			.*(F)[^A]*(F)


that is : a free followed by a second free on the same dataflow, not passing
through an allocation between those. A simple trace-based analyzer can detect
many cases of vulnerabilities using a single engine ! That superclass of 
vulnerability is made of so called type-state vulnerabilities, following the idea that
if the type of a variable does not change during the program, its state does,
thus the standard type checking approach is not sufficient to detect this kind of 
vulnerabilities.
		

As the careful reader might have noticed, this algorithm does not take predicates
in account, which means that if such a vulnerable trace is emitted, we have no 
garantee if the real conditions of the program will ever execute it. Indeed, we 
might extract a path of the program that "cross" on multiple predicates, some
being incompatible with others, thus generating infeasible paths using our
technique.

For example in our Stub 8 translated to assembly code, a predicate-insensitive 
analysis might generate the trace:

		1,2,3,4,5,6,8,9,10,11,12,13

which is impossible to execute because predicates holding at states 8 and 10 
cannot be respectively true and false after just one iteration of the loop. Thus 
such a trace cannot exist in the real world. 
 

We will not go further this topic for this article, but in the next part, we will
discuss various improvements of what should be a good analysis engine to avoid
generating too much false positives.



------------[ C. How to improve


	In this part, we will review various methods quickly to determine how exactly
it is possible to make the analysis more accurate and efficient. Current researchers
in program analysis used to call this a "counter-example guided" verification. Various
techniques taken from the world of Model Checking or Abstract Interpretation can then
be used, but we will not enter such theoretical concerns. Simply, we will discuss the
ideas of those techniques without entering details. The proposed chevarista analyzer
in appendix of this article only perform basic alias analysis, no predicate analysis,
and no thread scheduling analysis (as would be useful for detecting race conditions).
I will give the name of few analyzer that implement this analysis and quote which
techniques they are using.


----------------------[ a. Predicate analysis and the predicate lattice


Predicate abstraction [PA] is about collecting all the predicates in a program, and
constructing a mathematic object from this list called a lattice [LAT]. A lattice is
a set of objects on which a certain (partial) order is defined between elements
of this set. A lattice has various theoretical properties that makes it different
than a partial order, but we will not give such details in this article. We will
discuss about the order itself and the types of objects we are talking about:

	- The order can be defined as the union of objects 

				(P < Q iif P is included in Q)

	- The objects can be predicates


	- The conjunction (AND) of predicate can be the least upper bound of N
	predicates. Predicates (a > 42) and (b < 2) have as upper bound:

				(a > 42) && (b < 2)

	- The disjunction (OR) of predicates can be the greatest lower bound of
	N predicates. Predicates (a > 42) and (b < 2) would have as lower
	bound:

				(a > 42) || (b < 2)

	So the lattice would look like:


				(a > 42) && (b < 2)
					/  \
				       /    \
				      /      \
				(a > 42)     (b < 2)
				      \      /
                                       \    /
                                        \  /
	                        (a > 42) || (b < 2)


Now imagine we have a program that have N predicates. If all predicates
can be true at the same time, the number of combinations between predicates
will be 2 at the power of N. THis is without counting the lattice elements
which are disjunctions between predicates. The total number of combinations 
will then be then 2*2pow(N) - N : We have to substract N because the predicates
made of a single atomic predicates are shared between the set of conjunctives
and the set of disjunctive predicates, which both have 2pow(N) number of 
elements including the atomic predicates, which is the base case for a conjunction
(pred && true) or a disjunction (pred || false). 

We may also need to consider the other values of predicates : false, and unknown.
False would simply be the negation of a predicate, and unknown would inform about
the unknown truth value for a predicate (either false or true, but we dont know).
In that case, the number of possible combinations between predicates is to count
on the number of possible combinations of N predicates, each of them being potentially
true, false, or unknown. That makes up to 3pow(N) possibilities. This approach is called
three-valued logic [TVLA].

In other words, we have a exponential worse case space complexity for constructing 
the lattice of predicates that correspond to an analyzed program. Very often, the 
lattice will be smaller, as many predicates cannot be true at the same time. However, 
there is a big limitation in such a lattice: it is not capable to analyze predicates 
that mix AND and OR. It means that if we analyze a program that can be reached using 
many different set of predicates (say, by executing many different possible paths, 
which is the case for reusable functions), this lattice will not be capable to give 
the most precise "full" abstract representation for it, as it may introduce some 
flow-insensitivity in the analysis (e.g. a single predicate combinations will represent 
multiple different paths). As this might generate false positives, it looks like a good 
trade-off between precision and complexity. Of course, this lattice is just provided as 
an example and the reader should feel free to adapt it to its precise needs and depending 
on the size of the code to be verified. It is a good hint for a given abstraction
but we will see that other information than predicates are important for program
analysis.


	
---------------------[ b. Alias analysis is hard


	A problem that arises in both source code but even more in binary code
automated auditing is the alias analysis between pointers. When do pointers
points on the same variables ? This is important in order to propagate the
infered allocation size (when talking about a buffer), and to share a 
type-state (such as when a pointer is freed or allocated : you could miss 
double free or double-something bugs if you dont know that 2 variables are 
actually the same).

There are multiple techniques to achieve alias analysis. Some of them works
inside a single function (so-called intraprocedural [DDA]). Other works across
the boundaries of a function. Generally, the more precise is your alias
analysis, the smaller program you will be capable to analyze. It seems
quite difficult to scale to millions of lines of code if tracking every
single location for all possible pointers in a naive way. In addition
to the problem that each variable might have a very big amount of aliases
(especially when involving aliases over arrays), a program translated to
a single-assignment or single-information form has a very big amount of
variables too. However the live range of those variables is very limited,
so their number of aliases too. It is necessary to define aliasing relations
between variables so that we can proceed our analysis using some extra checks:

	- no_alias(a,b)   : Pointers a and b definitely points on different sets
			   of variables

	- must_alias(a,b) : Pointers a and b definitely points on the same set
			   of variables

	- may_alias(a,b)  : The "point-to" sets for variables a and b share some
			    elements (non-null intersection) but are not equal.

NoAliasing and MustAliasing are quite intuitive. The big job is definitely
the MayAliasing. For instance, 2 pointers might point on the same variable
when executing some program path, but on different variables when executing
from another path. An analysis that is capable to make those differences is
called a path-sensitive analysis. Also, for a single program location manipulating
a given variable, the point-to set of the variable can be different depending
on the context (for example : the set of predicates that are true at this moment 
of abstract program interpretation). An analysis that can reason on those
differences is called context-sensitive.

Its an open problem in research to find better alias analysis algorithms that scale
to big programs (e.g. few computation cost) and that are capable to keep
sufficiently precision to prove security properties. Generally, you can have one,
but not the other. Some analysis are very precise but only works in the boundaries
of a function. Others work in a pure flow-insensitive manner, thus scale to big
programs but are very imprecise. My example analyzer Chevarista implements only
a simple alias analysis, that is very precise but does not scale well to big
programs. For each pointer, it will try to compute its point-to set in the concrete
world by somewhat simulating the computation of pointer arithmetics and looking at 
its results from within the analyzer. It is just provided as an example but is
in no way a definitive answer to this problem.



--------------------[ c. Hints on detecting race conditions


	Another class of vulnerability that we are interested to detect
automatically are race conditions. Those vulnerability requires a different
analysis to be discovered, as they relates to a scheduling property : is
it possible that 2 thread get interleaved (a,b,a,b) executions over their
critical sections where they share some variables ? If the variables are
all well locked, interleaved execution wont be a problem anyway. But if 
locking is badly handled (as it can happens in very big programs such
as Operating Systems), then a scheduling analysis might uncover the 
problem.

Which data structure can we use to perform such analysis ? The approach
of JavaPathFinder [JPF] that is developed at NASA is to use a scheduling graph.
The scheduling graph is a non-cyclic (without loop) graph, where nodes
represents states of the program and and edges represents scheduling
events that preempt the execution of one thread for executing another.

As this approach seems interesting to detect any potential scheduling
path (using again a Depth First Search over the scheduling graph) that
fails to lock properly a variable that is used in multiple different
threads, it seems to be more delicate to apply it when we deal with
more than 2 threads. Each potential node will have as much edges as
there are threads, thus the scheduling graph will grow exponentially
at each scheduling step. We could use a technique called partial
order reduction to represent by a single node a big piece of code
for which all instructions share the same scheduling property (like:
it cannot be interrupted) or a same dataflow property (like: it uses
the same set of variables) thus reducing the scheduling graph to make
it more abstract.

Again, the chevarista analyzer does not deal with race conditions, but
other analyzers do and techniques exist to make it possible. Consider
reading the references for more about this topic.




-----------[ IV. Chevarista: an analyzer of binary programs

	
   Chevarista is a project for analyzing binary code. In this article, most of
   the examples have been given in C or assembly, but Chevarista only analyze
   the binary code without any information from the source. Everything it
   needs is an entry point to start the analysis, which you can always get
   without troubles, for any (working ? ;) binary format like ELF, PE, etc.

   Chevarista is a simplier analyzer than everything that was presented in
   this article, however it aims at following this model, driven by the succesful
   results that were obtained using the current tool. In particular, the
   intermediate form of Chevarista at the moment is a graph that contains
   both data-flow and control-flow information, but with sigma and phi 
   functions let implicit.

   For simplicity, we have chosen to work on SPARC [SRM] binary code, but after
   reading that article, you might understand that the representations
   used are sufficiently abstract to be used on any architecture. One could
   argue that SPARC instruction set is RISC, and supporting CISC architecture 
   like INTEL or ARM where most of the instruction are conditional, would be
   a problem. You are right to object on this because  these architectures
   requires specific features of the architecture-dependant backend of
   the decompiler-analyzer. Currently, only the SPARc backend is coded and there 
   is an empty skeleton for the INTEL architecture [IRM].

   What are, in the detail, the difference between such architectures ?

   They are essentially grouped into a single architecture-dependant component :
	
				The Backend

   On INTEL 32bits processors, each instruction can perform multiple operations. 
   It is also the case for SPARC, but only when conditional flags are affected 
   by the result of the operation executed by the instruction. For instance,
   a push instruction write in memory, modify the stack pointer, and potentially
   modify the status flags (eflags register on INTEL), which make it very hard to
   analyze. Many instructions do more than a single operation, thus we need to
   translate into intermediate forms that make those operations more explicit. If
   we limit the number of syntactic constructs in that intermediate form, we are
   capable of performing architecture independant analysis much easier with
   all operations made explicit. The low-level intermediate form of Chevarista
   has around 10 "abstract operations" in its IR : Branch, Call, Ternop (that
   has an additional field in the structure indicating which arithmetic or 
   logic operation is performed), Cmp, Ret, Test, Interrupt, and Stop. Additionally
   you have purely abstract operations (FMI: Flag Modifying Instruction), CFI
   (Control Flow Instruction), and Invoke (external functions calls) which allow to 
   make the analysis further even more generic. Invoke is a kind of statement that
   inform the analyzer that it should not try to analyze inside the function being
   invoked, but consider those internals as an abstraction. For instance, types
   Alloc, Free, Close are child classes of the Invoke abstract class, which model
   the fact that malloc(), free(), or close() are called and the analyzer should
   not try to handle the called code, but consider it as a blackbox. Indeed, finding
   allocation bugs does not require to go analyzing inside malloc() or free(). This
   would be necessary for automated exploit generation tho, but we do not cover this
   here.


   We make use the Visitor Design Pattern for architecturing the analysis, as presented 
   in the following paragraph.
   


--------------------[ B. Program transformation & modeling



	The project is organized using the Visitor Design Pattern [DP]. To sum-up,
  the Visitor Design Pattern allows to walk on a graph (that is: the intermediate
  form representation inside the analyzer) and transform the nodes (that contains
  either basic blocs for control flow analysis, or operands for dataflow analysis:
  indeed the control or data flow links in the graph represents the ancestors /
  successors relations between (control flow) blocs or (data flow) variables.


  The project is furnished as it:


  visitor: The default visitor. When the graph contains node which
  type are not handled by the current visitor, its this visitor that
  perform the operation. THe default visitor is the root class of 
  the Visitor classes hierarchy.

  arch	      : the architecture backend. Currently SPARC32/64 is fully
	      provided and the INTEL backend is just a skeleton. The
	      whole proof of concept was written on SPARC for simplicity. This
	      part also includes the generic code for dataflow and control flow 
	      computations.

  graph	      : It contains all the API for constructing graphs directly into
	      into the intermediate language. It also defines all the abstract
	      instructions (and the "more" abstract instruction as presented
	      previously)

  gate	      : This is the interprocedural analysis visitor. Dataflow and
	      Control flow links are propagated interprocedurally in that visitor. 
	      Additionally, a new type "Continuation" abstracts different kind of 
	      control transfer (Branch, Call, Ret, etc) which make the analysis even
	      easier to perform after this transformation.

  alias	      : Perform a basic point-to analysis to determine obvious aliases 
	      between variables before checking for vulnerabilities. THis analysis is 
	      exact and thus does not scale to big programs. There are many hours of
	      good reading and hacking to improve this visitor that would make the whole
	      analyzer much more interesting in practice on big programs.

  heap	      : This visitor does not perform a real transformation, but simplistic graph 
	      walking to detect anomalies on the data flow graph. Double frees, Memory
	      leaks, and such, are implemented in that Visitor.

  print	      : The Print Visitor, simply prints the intermediate forms after each
	      transformation in a text file.

  printdot    : Print in a visual manner (dot/graphviz) the internal representation. This
	      can also be called after each transformation but we currently calls it 
	      just at this end of the analysis.


Additionally, another transformation have been started but is still work in progress:



 symbolic     : Perform translation towards a more symbolic intermediate forms (such as
	      SSA and SSI) and  (fails to) structure the control flow graphs into a graph 
	      of zones. This visitor is work in progress but it is made part of this 
	      release as Chevarista will be discontinued in its current work, for being
	      implemented in the ERESI [RSI] language instead of C++.



	      ---------------      -----------      -----------      ----------   
	     |               |    |           |    |           |    |          |
   RAW       | Architecture  |    |   Gate    |    |   Alias   |    |   Heap   |
       ----> |               | -> |           | -> |           | -> |          | -> Results
   ASM       |   Backend     |    |  Visitor  |    |  Visitor  |    |  Visitor |
             |               |    |           |    |           |    |          |
              ---------------      -----------      -----------      ----------



--------------------[ C. Vulnerability checking


   
   Chevarista is used as follow in this demo framework. A certain big testsuits of binary
   files is provided in the package and the analysis is performed. In only a couple of
   seconds, all the analysis is finished:

   
   # We execute chevarista on testsuite binary 34

   $ autonomous/chevarista ../testsuite/34.elf
   
                  .:/\  Chevarista standalone version /\:.  
			
   [...]

   => chevarista 
Detected SPARC
Chevarista IS STARTING
Calling sparc64_IDG
Created IDG
SPARC IDG : New bloc at addr 0000000000100A34 
SPARC IDG : New bloc at addr 00000000002010A0 
[!] Reached Invoke at addr 00000000002010A4 
SPARC IDG : New bloc at addr 0000000000100A44 
Cflow reference to : 00100A50 
Cflow reference from : 00100A48 
Cflow reference from : 00100C20 
SPARC IDG : New bloc at addr 0000000000100A4C 
SPARC IDG : New bloc at addr 0000000000100A58 
SPARC IDG : New bloc at addr 0000000000201080 
[!] Reached Invoke at addr 0000000000201084 
SPARC IDG : New bloc at addr 0000000000100A80 
SPARC IDG : New bloc at addr 0000000000100AA4 
SPARC IDG : New bloc at addr 0000000000100AD0 
SPARC IDG : New bloc at addr 0000000000100AF4 
SPARC IDG : New bloc at addr 0000000000100B10 
SPARC IDG : New bloc at addr 0000000000100B70 
SPARC IDG : New bloc at addr 0000000000100954 
Cflow reference to : 00100970 
Cflow reference from : 00100968 
Cflow reference from : 00100A1C 
SPARC IDG : New bloc at addr 000000000010096C 
SPARC IDG : New bloc at addr 0000000000100A24 
Cflow reference to : 00100A2C 
Cflow reference from : 00100A24 
Cflow reference from : 00100A08 
SPARC IDG : New bloc at addr 0000000000100A28 
SPARC IDG : New bloc at addr 0000000000100980 
SPARC IDG : New bloc at addr 0000000000100A10 
SPARC IDG : New bloc at addr 00000000001009C4 
SPARC IDG : New bloc at addr 0000000000100B88 
SPARC IDG : New bloc at addr 0000000000100BA8 
SPARC IDG : New bloc at addr 0000000000100BC0 
SPARC IDG : New bloc at addr 0000000000100BE0 
SPARC IDG : New bloc at addr 0000000000100BF8 
SPARC IDG : New bloc at addr 0000000000100C14 
SPARC IDG : New bloc at addr 00000000002010C0 
[!] Reached Invoke at addr 00000000002010C4 
SPARC IDG : New bloc at addr 0000000000100C20 
SPARC IDG : New bloc at addr 0000000000100C04 
SPARC IDG : New bloc at addr 0000000000100910 
SPARC IDG : New bloc at addr 0000000000201100 
[!] Reached Invoke at addr 0000000000201104 
SPARC IDG : New bloc at addr 0000000000100928 
SPARC IDG : New bloc at addr 000000000010093C 
SPARC IDG : New bloc at addr 0000000000100BCC 
SPARC IDG : New bloc at addr 00000000001008E0 
SPARC IDG : New bloc at addr 00000000001008F4 
SPARC IDG : New bloc at addr 0000000000100900 
SPARC IDG : New bloc at addr 0000000000100BD8 
SPARC IDG : New bloc at addr 0000000000100B94 
SPARC IDG : New bloc at addr 00000000001008BC 
SPARC IDG : New bloc at addr 00000000001008D0 
SPARC IDG : New bloc at addr 0000000000100BA0 
SPARC IDG : New bloc at addr 0000000000100B34 
SPARC IDG : New bloc at addr 0000000000100B58 
Cflow reference to : 00100B74 
Cflow reference from : 00100B6C 
Cflow reference from : 00100B2C 
Cflow reference from : 00100B50 
SPARC IDG : New bloc at addr 0000000000100B04 
SPARC IDG : New bloc at addr 00000000002010E0 
SPARC IDG : New bloc at addr 0000000000100AE8 
SPARC IDG : New bloc at addr 0000000000100A98 
Intraprocedural Dependance Graph has been built succesfully! 
A number of 47 blocs has been statically traced for flow-types
[+] IDG built

Scalar parameter REPLACED with name = %o0 (addr= 00000000002010A4)
Backward dataflow analysis VAR        %o0, instr addr 00000000002010A4 
Scalar parameter REPLACED with name = %o0 (addr= 00000000002010A4)
Backward dataflow analysis VAR        %o0, instr addr 00000000002010A4 
Scalar parameter REPLACED with name = %o0 (addr= 00000000002010A4)
Backward dataflow analysis VAR        %o0, instr addr 00000000002010A4 
Backward dataflow analysis VAR        %fp, instr addr 0000000000100A48 
Return-Value REPLACED with name = %i0 (addr= 0000000000100A44) 
Backward dataflow analysis VAR        %i0, instr addr 0000000000100A44 
Backward dataflow analysis VAR        %fp, instr addr 0000000000100A5C 
Return-Value REPLACED with name = %i0 (addr= 0000000000100A58) 
Backward dataflow analysis VAR        %i0, instr addr 0000000000100A58 
Backward dataflow analysis VAR [%fp + 7e7], instr addr 0000000000100A6C 
Scalar parameter REPLACED with name = %o0 (addr= 0000000000201084)
Backward dataflow analysis VAR        %o0, instr addr 0000000000201084 
Scalar parameter REPLACED with name = %o0 (addr= 0000000000201084)
Backward dataflow analysis VAR        %o0, instr addr 0000000000201084 
Scalar parameter REPLACED with name = %o1 (addr= 0000000000201084)
Backward dataflow analysis VAR        %o1, instr addr 0000000000201084 
Scalar parameter REPLACED with name = %o1 (addr= 0000000000201084)
Backward dataflow analysis VAR        %o1, instr addr 0000000000201084 
Scalar parameter REPLACED with name = %o2 (addr= 0000000000201084)
Backward dataflow analysis VAR        %o2, instr addr 0000000000201084 
Scalar parameter REPLACED with name = %o2 (addr= 0000000000201084)
Backward dataflow analysis VAR        %o2, instr addr 0000000000201084 
Backward dataflow analysis VAR        %fp, instr addr 0000000000100A84 
Return-Value REPLACED with name = %i0 (addr= 0000000000100A80) 
Backward dataflow analysis VAR        %i0, instr addr 0000000000100A80 
Backward dataflow analysis VAR [%fp + 7d3], instr addr 0000000000100AA4 
Backward dataflow analysis VAR [%fp + 7df], instr addr 0000000000100ABC 
Backward dataflow analysis VAR [%fp + 7e7], instr addr 0000000000100AAC 
Backward dataflow analysis VAR        %fp, instr addr 0000000000100AD4 
Return-Value REPLACED with name = %i0 (addr= 0000000000100AD0) 
Backward dataflow analysis VAR        %i0, instr addr 0000000000100AD0 
Backward dataflow analysis VAR [%fp + 7d3], instr addr 0000000000100AF4 
Backward dataflow analysis VAR [%fp + 7d3], instr addr 0000000000100B24 
Backward dataflow analysis VAR [%fp + 7df], instr addr 0000000000100B18 
Backward dataflow analysis VAR [%fp + 7e7], instr addr 0000000000100B70 
Backward dataflow analysis VAR [%fp + 7e7], instr addr 0000000000100B70 
Backward dataflow analysis VAR [%fp + 7e7], instr addr 0000000000100B70 
Backward dataflow analysis VAR [%fp + 7e7], instr addr 0000000000100B38 
Backward dataflow analysis VAR        %fp, instr addr 0000000000100964 
Backward dataflow analysis VAR        %fp, instr addr 0000000000100964 
Backward dataflow analysis VAR        %fp, instr addr 0000000000100964 
Scalar parameter REPLACED with name = %o0 (addr= 0000000000100958)
Backward dataflow analysis VAR        %o0, instr addr 0000000000100958 
Scalar parameter REPLACED with name = %o0 (addr= 0000000000100958)
[....]
Backward dataflow analysis VAR        %fp, instr addr 0000000000100B6C 
Backward dataflow analysis VAR [%fp + 7df], instr addr 0000000000100B60 
Backward dataflow analysis VAR [%fp + 7e7], instr addr 0000000000100B58 
[+] GateVisitor finished

[+] AliasVisitor finished

+ Entered Node Splitting for Node id 24 
+ Entered Node Splitting for Node id 194 
+ Entered Node Splitting for Node id 722 
+ Entered Node Splitting for Node id 794 
+ Entered Node Splitting for Node id 1514 
+ Entered Node Splitting for Node id 1536 
+ Entered Node Splitting for Node id 1642 
[+] SymbolicVisitor finished

Entering DotVisitor
+ SESE visited
+ SESE visited
* SESE already visited
* SESE already visited
+ SESE visited
+ SESE visited
* SESE already visited
* SESE already visited
* SESE already visited
! Node pointed by (nil) is NOT a SESE
+ SESE visited
* SESE already visited
* SESE already visited
* SESE already visited
[+] Print*Visitors finished

Starting HeapVisitor
Double Free found
Double Free found
Double malloc
[+] Heap visitor finished

[+] Chevarista has finished


    The run was performed in less than 2 seconds and multiple vulnerabilities have
    been found in the binary file (2 double free and one memory leak as indicated
    by the latest output). Its pretty useless without more information, which brings
    us to the results.



-------------------------[ D. Vulnerable paths extraction




      Once the analysis has been performed, we can simply check what the vulnerable
      paths were:

      ~/IDA/sdk/plugins/chevarista/src $ ls tmp/
 
      cflow.png  chevarista.alias  chevarista.buchi  chevarista.dflow.dot  \
      chevarista.dot  chevarista.gate  chevarista.heap  chevarista.lir     \
      chevarista.symbolic  dflow.png


      Each visitor (transformation) outputs the complete program in each intermediate
      form. The most interesting thing is the output of the heap visitor that give
      us exactly the vulnerable paths:

      ~/IDA/sdk/plugins/chevarista/src $ cat tmp/chevarista.heap 

      [%fp + 7e7]

      [%fp + 7df]

      [%l0]

      ***********************************
      *                                 *
      * Multiple free of same variables *
      *                                 *
      ***********************************

      ******************
      path to free : 1
      ******************
      @0x2010a4 (0) {S} 32: inparam_%i0 = Alloc(inparam_%i0)      
      @0x100a44 (4) {S} 46: %g1 = outparam_%o0                    
      @0x100a48 (8) {S} 60: local_%fp$0x7e7 = %g1                 
      @0x100bcc (8) {S} 1770: outparam_%o0 = local_%fp$0x7e7      
      @0x1008e4 (8) {S} 1792: local_%fp$0x87f = inparam_%i0       
      @0x1008f4 (8) {S} 1828: outparam_%o0 = local_%fp$0x87f      
      @0x2010c4 (0) {S} 1544: inparam_%i0 = Free(inparam_%i0)     

      ******************
      path to free : 2
      ******************
      @0x2010a4 (0) {S} 32: inparam_%i0 = Alloc(inparam_%i0)      
      @0x100a44 (4) {S} 46: %g1 = outparam_%o0                    
      @0x100a48 (8) {S} 60: local_%fp$0x7e7 = %g1                 
      @0x100b58 (8) {S} 2090: %g1 = local_%fp$0x7e7               
      @0x100b5c (8) {S} 2104: local_%fp$0x7d7 = %g1               
      @0x100b68 (8) {S} 2146: %g1 = local_%fp$0x7d7               
      @0x100b6c (8) {S} 2160: local_%fp$0x7df = %g1               
      @0x100c14 (8) {S} 1524: outparam_%o0 = local_%fp$0x7df      
      @0x2010c4 (0) {S} 1544: inparam_%i0 = Free(inparam_%i0)     

      ******************
      path to free : 3
      ******************
      @0x2010a4 (0) {S} 32: inparam_%i0 = Alloc(inparam_%i0)      
      @0x100a58 (4) {S} 96: %g1 = outparam_%o0                    
      @0x100a5c (8) {S} 110: local_%fp$0x7df = %g1                
      @0x100c14 (8) {S} 1524: outparam_%o0 = local_%fp$0x7df      
      @0x2010c4 (0) {S} 1544: inparam_%i0 = Free(inparam_%i0)     

      ******************
      path to free : 4
      ******************
      @0x2010a4 (0) {S} 32: inparam_%i0 = Alloc(inparam_%i0)      
      @0x100a58 (4) {S} 96: %g1 = outparam_%o0                    
      @0x100a5c (8) {S} 110: local_%fp$0x7df = %g1                
      @0x100b60 (8) {S} 2118: %g1 = local_%fp$0x7df               
      @0x100b64 (8) {S} 2132: local_%fp$0x7e7 = %g1               
      @0x100bcc (8) {S} 1770: outparam_%o0 = local_%fp$0x7e7      
      @0x1008e4 (8) {S} 1792: local_%fp$0x87f = inparam_%i0       
      @0x1008f4 (8) {S} 1828: outparam_%o0 = local_%fp$0x87f      
      @0x2010c4 (0) {S} 1544: inparam_%i0 = Free(inparam_%i0)     
      
      ~/IDA/sdk/plugins/chevarista/src $ 
      

As you can see, we now have the complete vulnerable paths where multiple
frees are done in sequence over the same variables. In this example, 2
double frees were found and one memory leak, for which the path to free
is not given, since there is no (its a memory leak :).

A very useful trick was also to give more refined types to operands. For
instance, local variables can be identified pretty easily if they are
accessed throught the stack pointer. Function parameters and results
can also be found easily by inspecting the use of %i and %o registers
(for the SPARC architecture only).




----------------[ E. Future work : Refinement



	
	The final step of the analysis is refinement [CEGF]. Once you have analyzed
   a program for vulnerabilities and we have extracted the path of the program
   that looks like leading to a corruption, we need to recreate the real conditions
   of triggering the bug in the reality, and not in an abstract description of the
   program, as we did in that article. For this, we need to execute for real (this
   time) the program, and try to feed it with data that are deduced from the 
   conditional predicates that are on the abstract path of the program that leads to
   the potential vulnerability. The input values that we would give to the program
   must pass all the tests that are on the way of reaching the bug in the real world.

   Not a lot of projects use this technique. It is quite recent research to determine
   exactly how to be the most precise and still scaling to very big programs. The
   answer is that the precision can be requested on demand, using an iterative procedure
   as done in the BLAST [BMC] model checker. Even advanced abstract interpretation
   framework [ASA] do not have refinement in their framework yet : some would argue
   its too computationally expensive to refine abstractions and its better to couple
   weaker abstractions together than tring to refine a single "perfect" one.



 

---------------[ V. Related Work



	Almost no project about this topic has been initiated by the underground. The
	work of Nergal on finding integer overflow into Win32 binaries is the first
	notable attempt to mix research knowledge and reverse engineering knowledge,
	using a decompiler and a model checker. The work from Halvar Flake in the framework 
	of BinDiff/BinNavi [BN] is interesting but serves until now a different purpose than 
	finding vulnerabilities in binary code.

	On a more theoretical point of view, the interested reader is invited to look
	at the reference for findings a lot of major readings in the field of program
	analysis. Automated reverse engineering, or decompiling, has been studied in
	the last 10 years only and the gap is still not completely filled between those
	2 worlds. This article tried to go into that direction by introducing formal
	techniques using a completely informal view.

	Mostly 2 different theories can be studied : Model Checking [MC] and Abstract 
	Interpretation [AI] . Model Checking generally involves temporal logic properties
	expressed in languages such as LTL, CTL, or CTL* or [TL]. Those properties are then
	translated to automata. Traces are then used as words and having the automata
	not recognizing a given trace will mean breaking a property. In practice, the
	formula is negated, so that the resulting automata will only recognize the trace
	leading to vulnerabilities, which sounds a more natural approach for detecting
	vulnerabilities. 

	Abstract interpretation [ASA] is about finding the most adequate system representation 
	for allowing the checking to be computable in a reasonable time (else we might 
	end up doing an "exhaustive bruteforce checking" if we try to check all the potential
 	behavior of the program, which can btw be infinite). By reasoning into an abstract
	domain, we make the state-space to be finite (or at least reduced, compared to the 
	real state space) which turn our analysis to be tractable. The strongest the
	abstractions are, the fastest and imprecise our analysis will be. All the job
	consist in finding the best (when possible) or an approximative abstraction that
	is precise enough and strong enough to give results in seconds or minuts.

	In this article, we have presented some abstractions without quoting them explicitely
	(interval abstraction, trace abstraction, predicate abstraction ..). You can also
	design product domains, where multiple abstractions are considered at the same time,
	which gives the best results, but for which automated procedures requires more work
	to be defined.


------[ VI. Conclusion


	I Hope to have encouraged the underground community to think about using more
	formal techniques for the discovery of bugs in programs. I do not include this
	dream automated tool, but a simplier one that shows this approach as rewarding,
	and I look forward seing more automated tools from the reverse engineering 
	community in the future. The chevarista analyzer will not be continued as it, 
	but is being reimplemented into a different analysis environment, on top of a
	dedicated language for reverse engineering and decompilation of machine code.
	Feel free to hack inside the code, you dont have to send me patches as I do not
	use this tool anymore for my own vulnerability auditing. I do not wish to encourage
	script kiddies into using such tools, as they will not know how to exploit the
	results anyway (no, this does not give you a root shell).


------[ VII. Greetings

	
	Why should every single Phrack article have greetings ? 

	The persons who enjoyed Chevarista know who they are.


------[ VIII. References


 [TVLA] Three-Valued Logic
	http://en.wikipedia.org/wiki/Ternary_logic
  
 [AI] Abstract Interpretation
      http://www.di.ens.fr/~cousot/

 [MC] Model Checking
      http://en.wikipedia.org/wiki/Model_checking

 [CEGF] Counterexample-guided abstraction refinement 
        E Clarke - Temporal Representation and Reasoning

 [BN] Sabre-security BinDiff & BinNavi
      http://www.sabre-security.com/

 [JPF] NASA JavaPathFinder
       http://javapathfinder.sourceforge.net/

 [UNG] UQBT-ng : a tool that finds integer overflow in Win32 binaries
	events.ccc.de

 [SSA] Efficiently computing static single assignment form
       R Cytron, J Ferrante, BK Rosen, MN Wegman
       ACM Transactions on Programming Languages and SystemsFK 
 
 [SSI] Static Single Information (SSI)
       CS Ananian - 1999 - lcs.mit.edu       

 [MCI] Modern Compiler Implementation (Book)
       Andrew Appel
 
 [BMC] The BLAST Model Checker
       http://mtc.epfl.ch/software-tools/blast/

 [AD] 22C3 - Autodafe : an act of software torture
      events.ccc.de/congress/2005/fahrplan/events/606.en.html

 [TL] Linear Temporal logic
      http://en.wikipedia.org/wiki/Linear_Temporal_Logic

 [ASA] The ASTREE static analyzer
       www.astree.ens.fr

 [DLB] Dvorak LKML select bug
       Somewhere lost on lkml.org

 [RSI] ERESI (Reverse Engineering Software Interface)
       http://eresi.asgardlabs.org

 [PA] Automatic Predicate Abstraction of C Programs
      T Ball, R Majumdar, T Millstein, SK Rajamani 
      ACM SIGPLAN Notices 2001

 [IRM] INTEL reference manual
       http://www.intel.com/design/pentium4/documentation.htm

 [SRM] SPARC reference manual
       http://www.sparc.org/standards/

 [LAT] Wikipedia : lattice
       http://en.wikipedia.org/wiki/Lattice_%28order%29
 
 [DDA] Data Dependence Analysis of Assembly Code
       ftp://ftp.inria.fr/INRIA/publication/publi-pdf/RR/RR-3764.pdf

 [DP] Design Patterns : Elements of Reusable Object-Oriented Software
      Erich Gamma, Richard Helm, Ralph Johnson & John Vlissides
      


------[ IX. The code    

Feel free to contact me for getting the code. It is not included
in that article but I will provide it on request if you show
an interest. 
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