Fuzzing with Grammars

Overview

This article dives into the Fuzzing with Grammars chapter from The Fuzzing Book which builds on previous discussions, specifically those on Mutation-Based Fuzzing. By defining the legal inputs to a program through grammar specifications, we can streamline and enhance test generation, especially for complex formats. This blog post highlights the importance of structured inputs, which play a critical role in more advanced forms of fuzzing, including configuration fuzzing, API fuzzing, and GUI fuzzing. Here, we explore both the benefits and applications of grammar-based fuzzing, demonstrating how it offers a systematic approach for generating effective test cases across a range of input types. We will be exploring these practices and examining their relevance to our team’s current situation with tools like execexam, connecting them to the concepts discussed last week to highlight how grammar-based fuzzing provides a systematic and effective approach to comprehensive test case generation.

Summary

Grammars are essential tools for producing syntactically valid inputs, offering a structured approach to input generation. In this post, we will examine grammar-based fuzzing as a tool for generating complex, syntactically valid inputs. By seeding grammars in mutation-based fuzzing, we can produce varied inputs that strengthen testing efforts. The post will also highlight the benefits of incorporating character classes, operators, and helper functions to improve the efficiency and accuracy of input generation.

Input Languages

In software engineering all programs are triggered by the input, and this can be a wide range of sources. Here are some examples of inputs: data that the program reads from files, user input, or even data from interactions with other sources. All these dictate how a program will behave, so it is very helpful to think about possible input sources and get them under control and how to “systemically test them”.

For the sake of simplicity, we will assume that a program has one single input. The range of valid inputs a program can process correctly is referred to as a “language”, because they follow a specific set of rules of syntax and semantics. There are many examples, there are simple languages like comma-separated value (CSV) files and complex languages like Python programs.

For languages to be formally described, the field of formal language theory has developed a set of language specifications to describe a language. “Regular expressions” represent the simplest class of strings, for example [a-z]* represents a simple sequence of lowercase letters. Automata theory links these languages to Finite state machines, these machines help recognize patterns defined by regular expressions.

Regular expressions are good when representing a simple input but from more complex inputs they are limited. This is why we use turning machines. Turing machines can specify more complex input sets like Python. Since Python is Turing Complete it allows us to define and/or list inputs for another program but because testing has to be specific to each program it cannot be done automatically.

Grammars

Grammars are a set of rules that define the structure of a language, much like in English it allows us to properly construct sentences so our communication comes out clearly. Grammars are useful in helping understand the structure of an input and describing patterns where elements are nested within each other.

Representing Grammar in Python

from typing import List, Dict, Union, Any, Tuple

SimpleGrammar = Dict[str, List[str]]
Option = Dict[str, Any]
Expansion = Union[str, Tuple[str, Option]]
Grammar = Dict[str, List[Expansion]]

EXPR_GRAMMAR: Grammar = {
    "<start>":
        ["<expr>"],

    "<expr>":
        ["<term> + <expr>", "<term> - <expr>", "<term>"],

    "<term>":
        ["<factor> * <term>", "<factor> / <term>", "<factor>"],

    "<factor>":
        ["+<factor>",
         "-<factor>",
         "(<expr>)",
         "<integer>.<integer>",
         "<integer>"],

    "<integer>":
        ["<digit><integer>", "<digit>"],

    "<digit>":
        ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"]
}

"<identifier>" in EXPR_GRAMMAR

False

Simplifying Grammar-Based Fuzzing

Grammar-based fuzzing generates complex inputs for testing applications by systematically expanding grammar rules. The simple_grammar_fuzzer function begins with a start symbol (e.g., <start>) and iteratively expands it according to predefined grammar rules to create various expressions.

While effective, this approach is computationally expensive due to repetitive search and replace operations. Additionally, it may encounter expansion errors when it hits limits, such as reaching the maximum number of non-terminals.

Activity: Using nonterminals("<digit><integer>"), what non-terminals are extracted?

Simple Grammar Fuzzer

Let’s put the grammars to use! The simple_grammar_fuzzer is a basic tool that generates random expressions by starting with a placeholder (<start>) and expanding it using a set of grammar rules. It replaces non-terminal symbols like <expr> or <term> with random elements according to these rules until a complete expression is formed. To prevent endless expansion, the fuzzer limits the number of placeholders and retries, though it’s not perfect and sometimes encounters errors.

import random
import re
from typing import Any, Dict, List, Tuple

Grammar = Dict[str, List[str]]

def nonterminals(expansion):
    return re.compile(r'(<[^<> ]*>)').findall(expansion)

class ExpansionError(Exception):
    pass

def simple_grammar_fuzzer(grammar: Grammar, 
                          start_symbol: str = "<start>",
                          max_nonterminals: int = 10,
                          max_expansion_trials: int = 100,
                          log: bool = False) -> str:
    """Produce a string from `grammar`.
       `start_symbol`: use a start symbol other than `<start>` (default).
       `max_nonterminals`: the maximum number of non-terminals 
         still left for expansion
       `max_expansion_trials`: maximum # of attempts to produce a string
       `log`: print expansion progress if True"""

    term = start_symbol
    expansion_trials = 0

    while len(nonterminals(term)) > 0:
        symbol_to_expand = random.choice(nonterminals(term))
        expansions = grammar[symbol_to_expand]
        expansion = random.choice(expansions)
        # In later chapters, we allow expansions to be tuples,
        # with the expansion being the first element
        if isinstance(expansion, tuple):
            expansion = expansion[0]

        new_term = term.replace(symbol_to_expand, expansion, 1)

        if len(nonterminals(new_term)) < max_nonterminals:
            term = new_term
            if log:
                print("%-40s" % (symbol_to_expand + " -> " + expansion), term)
            expansion_trials = 0
        else:
            expansion_trials += 1
            if expansion_trials >= max_expansion_trials:
                raise ExpansionError("Cannot expand " + repr(term))

    return term

Now, we can fuzz with a grammar by calling the simple_grammar_fuzzer() function!

for i in range(10):
    print(simple_grammar_fuzzer(grammar=EXPR_GRAMMAR, max_nonterminals=5))

7 + 6.5 - (--0.00) * 24 - --1
+++-68
-((+6.92)) + 8 - (++5 * (-+0) - (-2) + (-+1) / 01) + -8
46 / 49 / 8.808254
5 / (+(+8) * +1) / ((+((++3 * -62) - -5 * -(-8 / -(--+89.7))))) * -+((4.00))
+1 + 784.1
2.4 / 5 / +1.29
++3 / ((2)) + (-8 + 5 / -0 / +-898.5 * +-(1.8 / +(-----+(8.7) - +6) - (-+5) / ((-(--++1)) / 5 + 8 * 5 / (-3 - 6) * (----(++(-+(+++-++-+(5)))) / (-(0 / 1) * -(+5) - -+(++1) / --((7)) * (---++-(+++-+(7)) * ((-5 / (-+(-2)) / +4 + ((-0 - 1.8) / +2 * ((+-((0)) + ++(-++((0)) + +(9)) - (+8) + 0 / ++1))) + +217)) + +3 + 9)) / 3) / -+7))) + 9.0
(7 * (62) * +5) / ----7 + +4 + +(6238.295)
(-(+5)) / +++(--(+-+(-((+-(9 - +-(4) * --+++9)))) + +1)) - +(+++(-+-1)) + ++--(--(+66.8) * 04)

While our fuzzer does the job in most cases, it has a number of drawbacks.

Activity: What drawbacks does simple_grammar_fuzzer() have?:

A. It has a large number of string search and replace operations

B. It may fail to produce a string (i.e., throw an ExpansionError)

C. It often picks some symbol to expand that does not even occur in the string

D. All of the above

Some Grammars

Here’s a grammar for cgi_decode() introduced in the Coverage chapter.

CGI_GRAMMAR: Grammar = {
    "<start>":
        ["<string>"],

    "<string>":
        ["<letter>", "<letter><string>"],

    "<letter>":
        ["<plus>", "<percent>", "<other>"],

    "<plus>":
        ["+"],

    "<percent>":
        ["%<hexdigit><hexdigit>"],

    "<hexdigit>":
        ["0", "1", "2", "3", "4", "5", "6", "7",
            "8", "9", "a", "b", "c", "d", "e", "f"],

    "<other>":  # Actually, could be _all_ letters
        ["0", "1", "2", "3", "4", "5", "a", "b", "c", "d", "e", "-", "_"],
}

for i in range(10):
    print(simple_grammar_fuzzer(grammar=CGI_GRAMMAR, max_nonterminals=10))

1b%1b+%0a
%12a
%f9
a
%4c
%f8
%74
+_
%61%c0
%040ce+

Grammar Toolbox

In your own grammar toolbox there are many different ways in which you could use grammars. It is important to note that grammars are not very effective in meeting complex constraints. The example of this that is given in the Fuzzing Book is that it would be hard to express a port range that is supposed to be between 1024 and 2048.

There are a few different reasons why this would be hard to express, one of them being that there is a limited range and thus the grammar could not allow a number larger than 2048. This means that there would have to be specific rules in place to make sure that 2048 is valid and 2049 is invalid. This can become extremely complex to express very quickly when having to set these types of parameters.

An example where grammars may be more practical would be using grammars as mutation seeds. The reason that grammars may be useful as an input is that they almost always can produce valid inputs from a syntactical standpoint. This is useful because when inputs are syntactically valid they can reveal other areas in which inputs may fail.

Another thing worth mentioning is escape characters that can be used for delimiting non-terminals in grammars. This can be useful whenever you want to manipulate the input or output to have a specific value.

simple_nonterminal_grammar: Grammar = {
   "<start>": ["<nonterminal>"],
   "<nonterminal>": ["<left-angle><identifier><right-angle>"],
   "<left-angle>": ["<"],
   "<right-angle>": [">"],
   "<identifier>": ["id"]  # for now
}

(Note that this does not work with simple_grammar_fuzzer(), but rather with the GrammarFuzzer class we’ll introduce in the next chapter.)

There are also shortcuts that can be implemented to manipulate how symbols can be used in recursion.

• <symbol>? indicates that <symbol> is optional meaning it can occur 0 or 1 times.
• <symbol>+ indicates that <symbol> can occur 1 or more times repeatedly
• <symbol>* indicates that <symbol> is completely optional.

You can also use parentheses to apply the shortcut to multiple symbols. For instance, (<symbol><char>)? would indicate that both <symbol> and <char> can optionally occur 0 or 1 times.

Checking Grammars

Grammars are introduced as strings making it somewhat easy for there to be errors. There is a solution to this. The helper function is_valid_grammar() can assist in making sure that your grammars are working correctly. This function iterates through a grammar to make sure that all symbols are defined and in use. It also identifies if all symbols are reachable from the start symbol. This helper function should be implemented whenever you are using grammars to assure that the inputs you are giving are what you expected them to be.

Key Takeaways

In this blog post, we explored the versatility of grammars in generating syntactically valid inputs and their use in mutation-based fuzzing. Grammars, when combined with character classes and operators, enhance the ease of producing more complex input patterns. How can we further leverage these extensions to streamline input generation during the software testing process for our tools?

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