240 lines
9.4 KiB
Plaintext
240 lines
9.4 KiB
Plaintext
# Specification for the fuzz testing tool
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#
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# Copyright (C) 2014 Maria Kustova <maria.k@catit.be>
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#
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# This program is free software: you can redistribute it and/or modify
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# it under the terms of the GNU General Public License as published by
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# the Free Software Foundation, either version 2 of the License, or
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# (at your option) any later version.
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#
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# This program is distributed in the hope that it will be useful,
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# but WITHOUT ANY WARRANTY; without even the implied warranty of
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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# GNU General Public License for more details.
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#
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# You should have received a copy of the GNU General Public License
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# along with this program. If not, see <http://www.gnu.org/licenses/>.
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Image fuzzer
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============
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Description
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-----------
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The goal of the image fuzzer is to catch crashes of qemu-io/qemu-img
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by providing to them randomly corrupted images.
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Test images are generated from scratch and have valid inner structure with some
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elements, e.g. L1/L2 tables, having random invalid values.
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Test runner
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-----------
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The test runner generates test images, executes tests utilizing generated
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images, indicates their results and collects all test related artifacts (logs,
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core dumps, test images, backing files).
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The test means execution of all available commands under test with the same
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generated test image.
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By default, the test runner generates new tests and executes them until
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keyboard interruption. But if a test seed is specified via the '--seed' runner
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parameter, then only one test with this seed will be executed, after its finish
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the runner will exit.
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The runner uses an external image fuzzer to generate test images. An image
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generator should be specified as a mandatory parameter of the test runner.
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Details about interactions between the runner and fuzzers see "Module
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interfaces".
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The runner activates generation of core dumps during test executions, but it
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assumes that core dumps will be generated in the current working directory.
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For comprehensive test results, please, set up your test environment
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properly.
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Paths to binaries under test (SUTs) qemu-img and qemu-io are retrieved from
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environment variables. If the environment check fails the runner will
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use SUTs installed in system paths.
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qemu-img is required for creation of backing files, so it's mandatory to set
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the related environment variable if it's not installed in the system path.
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For details about environment variables see qemu-iotests/check.
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The runner accepts a JSON array of fields expected to be fuzzed via the
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'--config' argument, e.g.
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'[["feature_name_table"], ["header", "l1_table_offset"]]'
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Each sublist can have one or two strings defining image structure elements.
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In the latter case a parent element should be placed on the first position,
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and a field name on the second one.
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The runner accepts a list of commands under test as a JSON array via
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the '--command' argument. Each command is a list containing a SUT and all its
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arguments, e.g.
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runner.py -c '[["qemu-io", "$test_img", "-c", "write $off $len"]]'
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/tmp/test ../qcow2
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For variable arguments next aliases can be used:
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- $test_img for a fuzzed img
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- $off for an offset in the fuzzed image
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- $len for a data size
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Values for last two aliases will be generated based on a size of a virtual
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disk of the generated image.
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In case when no commands are specified the runner will execute commands from
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the default list:
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- qemu-img check
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- qemu-img info
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- qemu-img convert
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- qemu-io -c read
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- qemu-io -c write
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- qemu-io -c aio_read
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- qemu-io -c aio_write
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- qemu-io -c flush
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- qemu-io -c discard
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- qemu-io -c truncate
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Qcow2 image generator
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---------------------
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The 'qcow2' generator is a Python package providing 'create_image' method as
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a single public API. See details in 'Test runner/image fuzzer' chapter of
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'Module interfaces'.
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Qcow2 contains two submodules: fuzz.py and layout.py.
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'fuzz.py' contains all fuzzing functions, one per image field. It's assumed
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that after code analysis every field will have own constraints for its value.
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For now only universal potentially dangerous values are used, e.g. type limits
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for integers or unsafe symbols as '%s' for strings. For bitmasks random amount
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of bits are set to ones. All fuzzed values are checked on non-equality to the
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current valid value of the field. In case of equality the value will be
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regenerated.
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'layout.py' creates a random valid image, fuzzes a random subset of the image
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fields by 'fuzz.py' module and writes a fuzzed image to the file specified.
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If a fuzzer configuration is specified, then it has the next interpretation:
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1. If a list contains a parent image element only, then some random portion
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of fields of this element will be fuzzed every test.
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The same behavior is applied for the entire image if no configuration is
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used. This case is useful for the test specialization.
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2. If a list contains a parent element and a field name, then a field
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will be always fuzzed for every test. This case is useful for regression
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testing.
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The generator can create header fields, header extensions, L1/L2 tables and
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refcount table and blocks.
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Module interfaces
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-----------------
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* Test runner/image fuzzer
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The runner calls an image generator specifying the path to a test image file,
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path to a backing file and its format and a fuzzer configuration.
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An image generator is expected to provide a
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'create_image(test_img_path, backing_file_path=None,
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backing_file_format=None, fuzz_config=None)'
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method that creates a test image, writes it to the specified file and returns
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the size of the virtual disk.
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The file should be created if it doesn't exist or overwritten otherwise.
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fuzz_config has a form of a list of lists. Every sublist can have one
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or two elements: first element is a name of a parent image element, second one
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if exists is a name of a field in this element.
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Example,
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[['header', 'l1_table_offset'],
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['header', 'nb_snapshots'],
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['feature_name_table']]
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Random seed is set by the runner at every test execution for the regression
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purpose, so an image generator is not recommended to modify it internally.
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Overall fuzzer requirements
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===========================
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Input data:
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----------
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- image template (generator)
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- work directory
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- action vector (optional)
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- seed (optional)
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- SUT and its arguments (optional)
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Fuzzer requirements:
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-------------------
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1. Should be able to inject random data
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2. Should be able to select a random value from the manually pregenerated
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vector (boundary values, e.g. max/min cluster size)
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3. Image template should describe a general structure invariant for all
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test images (image format description)
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4. Image template should be autonomous and other fuzzer parts should not
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rely on it
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5. Image template should contain reference rules (not only block+size
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description)
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6. Should generate the test image with the correct structure based on an image
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template
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7. Should accept a seed as an argument (for regression purpose)
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8. Should generate a seed if it is not specified as an input parameter.
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9. The same seed should generate the same image for the same action vector,
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specified or generated.
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10. Should accept a vector of actions as an argument (for test reproducing and
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for test case specification, e.g. group of tests for header structure,
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group of test for snapshots, etc)
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11. Action vector should be randomly generated from the pool of available
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actions, if it is not specified as an input parameter
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12. Pool of actions should be defined automatically based on an image template
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13. Should accept a SUT and its call parameters as an argument or select them
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randomly otherwise. As far as it's expected to be rarely changed, the list
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of all possible test commands can be available in the test runner
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internally.
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14. Should support an external cancellation of a test run
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15. Seed should be logged (for regression purpose)
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16. All files related to a test result should be collected: a test image,
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SUT logs, fuzzer logs and crash dumps
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17. Should be compatible with python version 2.4-2.7
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18. Usage of external libraries should be limited as much as possible.
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Image formats:
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-------------
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Main target image format is qcow2, but support of image templates should
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provide an ability to add any other image format.
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Effectiveness:
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-------------
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The fuzzer can be controlled via template, seed and action vector;
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it makes the fuzzer itself invariant to an image format and test logic.
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It should be able to perform rather complex and precise tests, that can be
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specified via an action vector. Otherwise, knowledge about an image structure
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allows the fuzzer to generate the pool of all available areas can be fuzzed
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and randomly select some of them and so compose its own action vector.
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Also complexity of a template defines complexity of the fuzzer, so its
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functionality can be varied from simple model-independent fuzzing to smart
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model-based one.
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Glossary:
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--------
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Action vector is a sequence of structure elements retrieved from an image
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format, each of them will be fuzzed for the test image. It's a subset of
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elements of the action pool. Example: header, refcount table, etc.
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Action pool is all available elements of an image structure that generated
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automatically from an image template.
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Image template is a formal description of an image structure and relations
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between image blocks.
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Test image is an output image of the fuzzer defined by the current seed and
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action vector.
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