Replication package for Mining Input Grammars From Dynamic Control Flow

IMPORTANT This complete source code of this artifact is hosted in this Github repository.

Our submission is a tool that implements the algorithm given in the paper Mining Input Grammars From Dynamic Control Flow. We provide the virtual machine mimid which contains the complete artifacts necessary to reproduce our experiments. We describe the process of invoking the virtual machine below.

We also note that if you are unable to download the vagrant box (It is 3 GB) You can also take a look at the complete worked out example of how to derive grammar for an example program at src/PymimidBook.ipynb. The src/PymimidBook.ipynb notebook also contains all the Python experiments in the paper. It can be viewed either by using the virtual box as explained below, or can be directly viewed using any of the Jupyter notebook viewers such as VSCode. A non-interactive version hosted at Github is accessible from this link (If the load fails, click reload until you can view it).

Rebuilding the vagrant box

If you want to recreate the virtual box, simply execute make box-create in the root directory.

$ make box-create

Overview

This paper presents a novel general algorithm for mining the input grammar of a given program. Our algorithm mimid takes a program and a small set of inputs, and automatically infers a readable (upto) context-free grammar that captures the input language of the program. Our progarm relies only on having access to access patterns in the initial input buffer from different locations in the parser. Our technique works on all stack based recursive descent input parsers including parser combinators, and works without program specific heursitics.

We evaluate the grammars obtained by mimid on a variety of subjects.

Prerequisites

RAM

All experiments done on a base system with 15 GB RAM, and the VM was allocated 10 GB RAM.

Setup

First, please make sure that the port 8888 is not in use. Our VM forwards its local port 8888 to the host machine.

Download

Next, please download the vagrant box from the following link:

https://doi.org/10.5281/zenodo.3876969

This produces a file called mimid.box which is 2.6 GB in size (the commands in the host system are indicated by leading $ and the other lines indicate the expected output),

$ du -ksh mimid.box
2.6G  mimid.box

and should have the following md5 checksum.

$ md5sum mimid.box
431f6ded243e91dcd5077b00ae2aa9b3 mimid.box

Importing the box

The vagrant box can be imported as follows:

$ vagrant box add mimid ./mimid.box
==> box: Box file was not detected as metadata. Adding it directly...
==> box: Adding box 'mimid' (v0) for provider:
    box: Unpacking necessary files from: file:///path/to/mimid/mimid.box
==> box: Successfully added box 'mimid' (v0) for 'virtualbox'!

$ vagrant init mimid
A `Vagrantfile` has been placed in this directory. You are now
ready to `vagrant up` your first virtual environment! Please read
the comments in the Vagrantfile as well as documentation on
`vagrantup.com` for more information on using Vagrant.

$ vagrant up

Bringing machine 'default' up with 'virtualbox' provider...
==> default: Importing base box 'mimid'...
==> default: Matching MAC address for NAT networking...
==> default: Setting the name of the VM: vtest_default_1591177746029_82328
==> default: Fixed port collision for 22 => 2222. Now on port 2200.
==> default: Clearing any previously set network interfaces...
==> default: Preparing network interfaces based on configuration...
    default: Adapter 1: nat
==> default: Forwarding ports...
    default: 8888 (guest) => 8888 (host) (adapter 1)
    default: 22 (guest) => 2200 (host) (adapter 1)
==> default: Running 'pre-boot' VM customizations...
==> default: Booting VM...
==> default: Waiting for machine to boot. This may take a few minutes...
    default: SSH address: 127.0.0.1:2200
    default: SSH username: vagrant
    default: SSH auth method: private key
==> default: Machine booted and ready!
==> default: Checking for guest additions in VM...
==> default: Mounting shared folders...
    default: /vagrant => /path/to/mimid

Verify Box Import

The commands inside the guest system are indicated by a vm$ prefix. Anytime vm$ is used, it means to either ssh into the vagrant box as below, or if you are already in the VM, use the shell inside VM.

$ vagrant ssh

vm$ free -g
              total        used        free      shared  buff/cache   available
Mem:              9           0           9           0           0           9
Swap:             1           0           1

A complete example

vm$ pwd
/home/vagrant
vm$ ls
mimid
c_tables.sh
py_tables.sh
start_c_tests.sh
start_py_tests.sh
startjupyter.sh
taints
toolchains

The following are the important files

File/Directory	Description
startjupyter.sh	The script to start Jupyter notebook to view examples.
start_py_tests.sh	Start the Python experiments.
py_tables.sh	CLI for viewing the results from C experiments.
start_c_tests.sh	Start the C experiments.
c_tables.sh	CLI for viewing the results from Python experiments.
mimid/src/	The main mimid algorithm implementation.
mimid/Cmimid	The modularized mimid implementation (in Python) experiments in C.
mimid/src/PymimidBook.ipynb	The detailed mimid notebook which also contains experiments in Python.
toolchains/	The original LLVM and Clang tool chain.
taints/	The module to instrument C files.

The most important file here is mimid/src/PymimidBook.ipynb which is the Jupyter notebook that contains the complete algorithm explained and worked out over two examples: one simple, and the other more complex. It can be interactively explored using any of the Jupyter notebook viewers including VSCode or directly using a browser as explained below.

It can also be viewed (read only) directly using the github link here

Viewing the Jupyter notebook

From within your VM shell, do the following:

vm$ ./startjupyter.sh
...
     or http://127.0.0.1:8888/?token=ba5e5c480fe4a03d56c358b4a10d7172d2b19ff4537be55e

Copy and paste the last line in the host browser. The port 8888 is forwarded to the host. Click the src link from the browser and within that folder, click the PymimidBook.ipynb link. This will let you see the complete set of examples as well as the Python experiments in an already executed form.

You can either interactively explore the notebook by first clearing the current values Kernel>Restart & Clear Output and executing each cell one at a time, or by running all the experiments at once by Kernel>Restart & Run All. Other options are also available from menu.

Starting the experiments

There are two sets of experiments: The Python experiments (calc.py, mathexpr.py, cgidecode.py, urlparse.py, microjson.py, parseclisp.py) and the C experiments (json.c, tiny.c, mjs.c). We explain the Python part first.

Python experiments

IMPORTANT The system is very memory intensive. Hence, do not run the experiments in parallel (e.g. concurrently from a different shell). If you do that, you might get a memory error.

The following are the Python programs

Program	Input Language Kind	Description
`calc.py`	Context Free	A simple calculator program.
`mathexpr.py`	Context Free	A more complex math expression program which supports functions.
`cgidecode.py`	Regular	A program to decode CGI encoded strings.
`urlparse.py`	Regular	A program to parse URLs.
`microjson.py`	Context Free	A program to parse JSON strings.
`parseclisp.py`	Context Free	A program to parse s-expressions.

There are two main ways to run the Python experiments. Either through the Jupyter notebook as we explained earlier, or if you are on a headless system, using the command line, which is explained below.

To start the Python experiments, execute the shell command below:

vm$ ./start_py_tests.sh

This will execute the Python experiments embedded in the Jupyter notebook without requiring a browser, and produce an HTML file PymimidBook.html in the /home/vagrant folder which may be viewed offline.

Result analysis for Python (CLI)

After running the python experiments using command line, the results can be inspected using the following command line. Note that running it through Jupyter notebook interface will not produce the PymimidBook.html file which is required for using py_tables.sh. In that case, please view the results directly in the notebook Results section.

vm$ ./py_tables.sh

Precision (Table 1)     Autogram        Mimid
-----------------
calculator.py,  37.5%,  100.0%
mathexpr.py,    26.9%,  97.8%
urlparse.py,    100.0%, 100.0%
cgidecode.py,   47.6%,  100.0%
microjson.py,   47.5%,  98.6%

Recall (Table 2)        Autogram        Mimid
-----------------
calculator.py,  1.6%,   100.0%
mathexpr.py,    0.2%,   93.4%
urlparse.py,    100.0%, 95.0%
cgidecode.py,   34.7%,  100.0%
microjson.py,   0.0%,   93.0%

Here, we compare the results from Autogram to the results from mimid. What this means is that the grammar inferred by mimid for microjson (for example) when used with a grammar based random fuzzer, generated inputs such that 98.6% of such inputs were accepted by microjson.py when compared to the grammar inferred by Autogram that could only produce (using a grammar fuzzer) 47.5% valid inputs (Table 1). Similarly, when valid Javascript strings were randomly generated using a fuzzer and the golden grammar for Javascript, 93.0% of such inputs would be parsed correctly by a parser that uses the grammar mined by mimid from microjson when compared to a parser using a grammar mined using Autogram from microjson which accepts 0.0% of the inputs (Table 2).

C experiments

The following are the C programs

Program	Input Language Kind	Description
mjs.c	Context Free	The embedded Javascript interpreter.
tiny.c	Context Free	The `tinyC` C compiler.
json.c	Context Free	A JSON parser.

The C experiments are not accessible from the Jupyter notebook as it requires instrumenting the C programs. Further, the set of C programs is intended as a demonstration of how to use the mimid algorithm in a standalone fashion.

The C experiments are in the directory mimid/Cmimid, and the modularized mimid implementation (in Python) is available under mimid/Cmimid/src.

To start the C experiments, execute the shell command below:

vm$ ./start_c_tests.sh

This will execute all the C experiments, and produce results which can be analyzed as below:

Result analysis for C

Note that for C there is no Autogram implementation available, so we have nothing to compare against. Hence, the precision and recall is provided as is. The following command line produces the results (table names are in correspondence with the paper).

vm$ ./c_tables.sh
Precision (Table 1)     Mimid
-----------------
mjs     97.5%
tiny    92.8%
json    83.8%

Recall (Table 2)        Mimid
-----------------
mjs     90.5%
json    100.0%
tiny    100.0%

As before, what this means is that the grammar inferred by mimid for mjs (for example), when used with a random grammar fuzzer, generated inputs such that 97.5% of such inputs were accepted by mjs (Table 1). Similarly, when valid Javascript strings were generated randomly using a golden grammar, 90.5% of such inputs were parsed correctly by a parser that uses the grammar mined by mimid from mjs (Table 2).

How is the algorithm organized

The Jupyter notebook provided has complete documentation of the entire algorithm. Each method is thoroughly documented, and executions of methods can be performed to verify their behavior. Use the Jupyter notebook src/PymimidBook.ipynb as the main guide, and for interactive exploration of the algorithms.

The algorithm consists of the following parts. The part in the paper is given as a heading, and the corresponding parts in Jupyter notebook are noted below.

Paper: Section 3 Tracking control flow and comparisons

Instrumenting the source code to track access and control flow.

Section 1.5 Rewriting the source code in the Jupyter notebook.
Running the inputs, and generating access traces.

Section 1.6 Generating traces in the Jupyter notebook.

Paper: Section 4 From traces to parse trees

Extracting the traces to a parse tree.

Section 1.7 Mining Traces Generated in the Jupyter notebook.
Active learning of labelling (Section 4.1, 4.2 in paper)

Section 1.8 Generalize Nodes in the Jupyter notebook Section 4.1 in the paper corresponds to Section 1.8.1 in Jupyter notebook Section 4.2 in the paper corresponds to Section 1.8.2 in Jupyter notebook

Paper: Section 5 Grammar Inferrence

Generating the grammar from the collected parse trees.

Section 1.9 Generating a grammar in the Jupyter notebook.

Paper: Section 6 Evaluation

Section 2 Evaluation in the Jupyter notebook

How to add a new subject

We discuss how to add Python and C subjects below.

Adding a new Python program

To add a new Python program, one needs to know what kind of a parer it has. That is, if it is the traditional recursive descent, or a parser combinator implementation. Both are discussed below.

Adding a traditional recursive descent Python program

IMPORTANT: We assume that you have the program as well as the sample inputs. Both are required for grammar mining. In particular, we can only mine the features that are exercised by the given set of samples. Instead of the sample inputs, one can also provide a golden grammar which can be used instead to generate inputs (that may or may not be accepted by the program --- i.e the grammar implemented need not be in sync with the golden grammar, but rather, we can use the golden grammar to explore the implementation --- We only use accepted inputs for mining).

Ensure that the program has no external calls during parsing such as shlex or other lexers. For ease of explanation, we assume that you have a single source file program called ex.py. Copy and paste the contents of ex.py in the Jupyter notebook in a cell in the Our subject programs section with the following format:

%%var ex_src
# [(
...
# )]

where the ellipsis (...) is replaced by the contents of the file ex.py.

Next, register it in program_src variable as below:

program_src = {
  ...
  'ex.py': VARS['ex_src'],
}

Execute all the modified cells so that the variables are correctly populated.

Also, make sure to execute the following fragment in Generate Transformed Sources section

# [(
for file_name in program_src:
    print(file_name)
    with open("subjects/%s" % file_name, 'wb+') as f:
        f.write(program_src[file_name].encode('utf-8'))
    with open("build/%s" % file_name, 'w+') as f:
        f.write(rewrite(program_src[file_name], file_name))
# )]

which will produce the original file mimid/src/subjects/ex.py and the instrumented file mimid/src/build/ex.py.)

Next, fill in the sample inputs in an array as follows (preferably under the Evaluation/Subjects/ section under an Ex heading):

ex_samples = [ . . . ]

Again, fill in the ellipsis with the correct values.

Now, grammar mining is as simple as executing the following command

ex_grammar = accio_grammar('ex.py', VARS['ex_src'], ex_samples, cache=False)

The ex_grammar variable will hold the mined grammar. If there are any errors, restart and re-execute the Jupyter notebook completely (with the added example). This will clear away any interfering global state, and execute the example correctly.

Adding a parser combinator example

Adding a new parser combinator is more involved as it requires modification of the library. In particular, there is no information to be gathered from the method names as they are generic names such as curried and parse. The actual readable names are provided as the variable names of parser objects.

E.g. the actual readable information is present in the variable name digit rather than the method name any_of in the below example.

digit = any_of(string.digits)

Unfortunately, Python provides no easy way for us to recover the name of the variable that holds an object from within the object. Hence, we require that each object that contains a recognizable parser such as digit also defines a .tag member variable which provides the name associated as a string.

(While it can be technically done using an AST rewrite automatically, or there might be other clever ways of recovering the variable name, we have not done that at this point.)

digit = any_of(string.digits)
digit.tag = 'digit'

Hence, one can either reuse the existing parser combinator library in section The parsec library, or use your own combinator library with the tag information for each parser object as given above.

Since adding a new parser combinator library is fairly complex, and may require additional customization of the library, we do not explore that here. Instead we assume that you use the parser combinator library given in the Jupyter notebook itself.

To make the addition simple, please add your example program under the A parsec lisp parser section, by adding the program pcex in the end under a new cell. Use %%var pcex_src line as the first line in that cell with your parser combinator definitions. Make sure to provide the .tag string to each named parser. Finally, make sure that you define a main function that takes in the string to be parsed as an argument.

E.g.

%%var pcex_src
import string
import json
import sys
import myparsec as pyparsec

ap = pyparsec.char('a')
ap.tag = 'a'
bp = pyparsec.char('b')
bp.tag = 'b'

abparser = ap >> bp
abparser.tag = 'abparser'

def main(arg):
    v = abparser.parse(arg)
    if isinstance(v, pyparsec.Left):
        raise Exception('parse failed')
    return v

Next, provide your own samples in the variable pcex_samples as we showed earlier.

Finally, the grammar can be extracted as

pc_grammar = accio_grammar('pcex.py', VARS['pcex_src'], pcex_samples)

where the variable pc_grammar holds the grammar mined.

Adding a new C program

First, move into /home/vagrant/mimid/Cmimid/. This is the base directory for C examples.

vm$ cd ~/mimid/Cmimid

Check the targets supported by the Makefile as below:

vm$ make help

For subject specific help, use help-subject

E.g.

vm$ make help-tiny

To add a new C program, we assume that the program you have is a single C file called ex.c. The ex.c is assumed to read inputs both from stdin as well as as an argument to the program, which is then processed by the parsing function in your program.

Assuming that your parser entry point is a function parse_ex(char* input, char* result), copy and paste the following into your program. (The second parameter result is only an example, and is not needed. All you need is some means to return the parse failure or success).

void strip_input(char *my_string) {
  int read = strlen(my_string);
  if (my_string[read - 1] == '\n') {
    my_string[read - 1] = '\0';
  }
}

int main(int argc, char *argv[]) {
  char my_string[10240];
  char result[10240];
  init_hex_values();
  int ret = -1;
  if (argc == 1) {
    char *v = fgets(my_string, 10240, stdin);
    if (!v) {
      exit(1);
    }
    strip_input(my_string);
  } else {
    strcpy(my_string, argv[1]);
    strip_input(my_string);
  }
  printf("val: <%s>\n", my_string);
  ret = parse_ex(my_string, &result);
  return ret;
}

Place the program under examples/. Further, provide the golden grammar of your program as ex.grammar under the same directory.

E.g:

vm$ ls examples/ex.*
examples/ex.c examples/ex.grammar

The golden grammar examples/ex.grammar is used to generate inputs.

One can also provide sample inputs, in which case, they can be used instead. In that case, place the program under examples/ as before. Then, provide the sample inputs to your program as ex.input.1, ex.input.2 etc. under the same directory (please follow the *.input.<n> naming convention. It is required for the make).

Note that the original abstract target is the following, where KIND is set to generate from grammar.

build/%.inputs.done: build/%.inputs.done.$(KIND)
    touch $@

Since we are providing a set of inputs, we only need to copy. Hence edit the Makefile and add a new target for this file as below.

build/ex.inputs.done: build/ex.inputs.done.copy
    touch $@

IMPORTANT: Note that the KIND part of the receipt is set to copy.

Next, check whether your program has a lexer or a tokenizer. A lexer reads the input, and splits it into predefined tokens before it is passed into the program. For an example of a program that uses a lexer, see examples/mjs.c where the lexer is static int pnext(struct pstate *p). For an example of a program that does not use a lexer, see examples/cgi_decode.c where each character is processed directly by the parser.

If it has a lexer, then edit the Makefile, and define a target build/ex.events as below. Note that the Makefile syntax requires a tab i.e. (\t) before the recipe.

build/ex.events: build/ex.json.done
  $(PYTHON) ./src/tokenevents.py build/ex/ > $@_
  mv $@_ $@

If it does not use a lexer, then the default build/%.events target will work without modification.

Once this is done, one can mine the grammar and extract the grammar with the following commands:

vm$ make build/ex.grammar
vm$ cat build/ex.grammar

The precision of the grammar can be extracted with:

vm$ make build/ex.precision
vm$ cat build/ex.precision

Similarly, the recall can be extracted with:

vm$ make build/ex.fuzz
vm$ cat build/ex.fuzz

Note that one can also proceed step by step,

First compiling and instrumenting the program:

vm$ make build/tiny.x
vm$ make build/tiny.d

Then generating inputs using the provided golden grammar (If copying the sample inputs, edit the Makefile and add a new receipt for the inputs as described previously, with the KIND set to copy for this particular target.)

vm$ make build/tiny.inputs.done

Running the instrumented program to collect the traces

vm$ make build/tiny.json.done

Extract the mimid buffer access events with control flow annotation

vm$ make build/tiny.events

Extract the parse trees from the collected events.

vm$ make build/tiny.tree

After this, the following command can be used to view the parse trees generated using ASCII art.

vm$ make build/tiny.showtree

Extract the generalized grammar from the parse trees

vm$ make build/tiny.mgrammar

Convert the generalized grammar to a compact EBNF form

vm$ make build/tiny.grammar

After this, the following command can be used to inspect the grammar generated in a colorized form:

vm$ make build/tiny.showg

Convert the grammar to the fuzzingbook canonical representation for parsing

vm$ make build/tiny.pgrammar

At this point, one can evaluate the precision and recall as follows:

Precision

vm$ make build/tiny.precision

Recall

vm$ make build/tiny.fuzz

We also note that each command line invoked by the make can also be invoked directly. All command lines take a flat -h which provides information on its argument, and how it is used. We have opted to keep the documentation in the Jupyter notebook, and simply generate the modules in accordance with the DRY principle.

E.g Viewing the trees at different stages of generalization.

vm$ python3 ./src/ftree.py
ftree.py <json file> <tree numbers>*
    Display the selected trees from <json file>
    if <tree numbers> is empty, all trees are shown if not, only selected trees are shown

Using it (the first line is the string that was parsed.):

vm $ python3 ./src/ftree.py build/tiny.tree 1 2 | less -r
o )if ( a < a ); else ; else { ; }}else ; '
'<START>'
+-- '<_real_program_main(1).0>'
    +-- '<parse_expr.0>'
            +-- '<program.0>'
                        |-- '<statement.0>'
...

The parse trees in in-between stages can also be inspected:

vm$ ls build/tiny-*tree*
build/tiny-loop_trees.json  build/tiny-method_trees.json  build/tiny-trees.json

For example, the initial parse tree is in build/tiny-trees.json. The first tree before generalization can be viewed as

vm $ python3 ./src/ftree.py build/tiny-trees.json 0 | less -r

The next generalization stage is methods. The result can be seen as

vm $ python3 ./src/ftree.py build/tiny-method_trees.json 0 | less -r

The final generalization stage (copied to build/tiny.tree) is build/tiny-loop_trees.json which can be viewed as

vm $ python3 ./src/ftree.py build/tiny-loop_trees.json 0 | less -r

Similarly, any of the in-between grammars can be inspected. For example, the non-generalized mined grammar is build/tiny-mined_g.json which can be viewed as:

vm$ cat build/tiny-mined_g.json | jq . -C | less -r
{
  "[start]": "<START>",
    "[grammar]": {
    . . .
  },
  "[command]": "build/tiny.x"
}

where [start] is the start symbol.

Grammar Format

The [grammar] is the grammar in the Fuzzingbook canonical JSON format, where the nonterminals are represented as keys of a python dict, and each nonterminal is associated with a definition represented by an list of rules, and each rule is again a list of tokens, and each token can either be a nonterminal or a terminal symbol represented as a string.

The [command] is the un-instrumented program that accepts the grammar (The instrumented program from which the grammar was mined can be obtained by substituting .x with .d.)

After tokens are generalized, it becomes build/tiny-general_tokens.json (before compaction), which can be viewed as

vm$ cat build/tiny-general_tokens.json | jq . -C | less -r

The defined top level keys are:

vm$ cat build/tiny.grammar | jq '. | keys'
[
  "[command]",
  "[grammar]",
  "[start]"
]

Note that the tree formats are also fixed.

vm$ cat build/tiny.tree | jq '.[0] | keys'
[
  "arg",
  "original",
  "tree"
]

For example, one can access the input file that generated a particular tree (0) using:

vm$ cat build/tiny.tree | jq '.[0].arg' -C

The .original contains the (un-instrumented) program from which grammar was mined.

vm$ cat build/tiny.tree | jq '.[0].original' -C | less -r

Finally, tree itself is

vm$ cat build/tiny.tree | jq '.[0].tree' -C | less -r

Derivation Tree Format

The tree format is again from the Fuzzingbook, where the derivation tree is represented as a recursive structure with each node represented by a tuple or pair list with the first element, the nonterminal that corresponds to that node, and the second element the children of that node, and remaining elements contain any meta information associated with the node. The terminal symbols are represented by nodes with empty children.

Notes

The complete run (below command line) can take up to 10 hours to complete.

vm$ ./start_c_tests.sh && ./start_py_tests.sh

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README.md

README.md

Replication package for Mining Input Grammars From Dynamic Control Flow

Rebuilding the vagrant box

Overview

Prerequisites

RAM

Setup

Download

Importing the box

Verify Box Import

A complete example

Viewing the Jupyter notebook

Starting the experiments

Python experiments

Result analysis for Python (CLI)

C experiments

Result analysis for C

How is the algorithm organized

Paper: Section 3 Tracking control flow and comparisons

Paper: Section 4 From traces to parse trees

Paper: Section 5 Grammar Inferrence

Paper: Section 6 Evaluation

How to add a new subject

Adding a new Python program

Adding a traditional recursive descent Python program

Adding a parser combinator example

Adding a new C program

Notes

Files

README.md

Latest commit

History

README.md

File metadata and controls

Replication package for Mining Input Grammars From Dynamic Control Flow

Rebuilding the vagrant box

Overview

Prerequisites

RAM

Setup

Download

Importing the box

Verify Box Import

A complete example

Viewing the Jupyter notebook

Starting the experiments

Python experiments

Result analysis for Python (CLI)

C experiments

Result analysis for C

How is the algorithm organized

Paper: Section 3 Tracking control flow and comparisons

Paper: Section 4 From traces to parse trees

Paper: Section 5 Grammar Inferrence

Paper: Section 6 Evaluation

How to add a new subject

Adding a new Python program

Adding a traditional recursive descent Python program

Adding a parser combinator example

Adding a new C program

Notes