| /* A fuzz test for CPython. | |
| The only exposed function is LLVMFuzzerTestOneInput, which is called by | |
| fuzzers and by the _fuzz module for smoke tests. | |
| To build exactly one fuzz test, as when running in oss-fuzz etc., | |
| build with -D _Py_FUZZ_ONE and -D _Py_FUZZ_<test_name>. e.g. to build | |
| LLVMFuzzerTestOneInput to only run "fuzz_builtin_float", build this file with | |
| -D _Py_FUZZ_ONE -D _Py_FUZZ_fuzz_builtin_float. | |
| See the source code for LLVMFuzzerTestOneInput for details. */ | |
| #include <Python.h> | |
| #include <stdlib.h> | |
| #include <inttypes.h> | |
| /* Fuzz PyFloat_FromString as a proxy for float(str). */ | |
| static int fuzz_builtin_float(const char* data, size_t size) { | |
| PyObject* s = PyBytes_FromStringAndSize(data, size); | |
| if (s == NULL) return 0; | |
| PyObject* f = PyFloat_FromString(s); | |
| if (PyErr_Occurred() && PyErr_ExceptionMatches(PyExc_ValueError)) { | |
| PyErr_Clear(); | |
| } | |
| Py_XDECREF(f); | |
| Py_DECREF(s); | |
| return 0; | |
| } | |
| #define MAX_INT_TEST_SIZE 0x10000 | |
| /* Fuzz PyLong_FromUnicodeObject as a proxy for int(str). */ | |
| static int fuzz_builtin_int(const char* data, size_t size) { | |
| /* Ignore test cases with very long ints to avoid timeouts | |
| int("9" * 1000000) is not a very interesting test caase */ | |
| if (size > MAX_INT_TEST_SIZE) { | |
| return 0; | |
| } | |
| /* Pick a random valid base. (When the fuzzed function takes extra | |
| parameters, it's somewhat normal to hash the input to generate those | |
| parameters. We want to exercise all code paths, so we do so here.) */ | |
| int base = _Py_HashBytes(data, size) % 37; | |
| if (base == 1) { | |
| // 1 is the only number between 0 and 36 that is not a valid base. | |
| base = 0; | |
| } | |
| if (base == -1) { | |
| return 0; // An error occurred, bail early. | |
| } | |
| if (base < 0) { | |
| base = -base; | |
| } | |
| PyObject* s = PyUnicode_FromStringAndSize(data, size); | |
| if (s == NULL) { | |
| if (PyErr_ExceptionMatches(PyExc_UnicodeDecodeError)) { | |
| PyErr_Clear(); | |
| } | |
| return 0; | |
| } | |
| PyObject* l = PyLong_FromUnicodeObject(s, base); | |
| if (l == NULL && PyErr_ExceptionMatches(PyExc_ValueError)) { | |
| PyErr_Clear(); | |
| } | |
| PyErr_Clear(); | |
| Py_XDECREF(l); | |
| Py_DECREF(s); | |
| return 0; | |
| } | |
| /* Fuzz PyUnicode_FromStringAndSize as a proxy for unicode(str). */ | |
| static int fuzz_builtin_unicode(const char* data, size_t size) { | |
| PyObject* s = PyUnicode_FromStringAndSize(data, size); | |
| if (s == NULL && PyErr_ExceptionMatches(PyExc_UnicodeDecodeError)) { | |
| PyErr_Clear(); | |
| } | |
| Py_XDECREF(s); | |
| return 0; | |
| } | |
| #define MAX_JSON_TEST_SIZE 0x10000 | |
| PyObject* json_loads_method = NULL; | |
| /* Called by LLVMFuzzerTestOneInput for initialization */ | |
| static int init_json_loads() { | |
| /* Import json.loads */ | |
| PyObject* json_module = PyImport_ImportModule("json"); | |
| if (json_module == NULL) { | |
| return 0; | |
| } | |
| json_loads_method = PyObject_GetAttrString(json_module, "loads"); | |
| return json_loads_method != NULL; | |
| } | |
| /* Fuzz json.loads(x) */ | |
| static int fuzz_json_loads(const char* data, size_t size) { | |
| /* Since python supports arbitrarily large ints in JSON, | |
| long inputs can lead to timeouts on boring inputs like | |
| `json.loads("9" * 100000)` */ | |
| if (size > MAX_JSON_TEST_SIZE) { | |
| return 0; | |
| } | |
| PyObject* input_bytes = PyBytes_FromStringAndSize(data, size); | |
| if (input_bytes == NULL) { | |
| return 0; | |
| } | |
| PyObject* parsed = PyObject_CallFunctionObjArgs(json_loads_method, input_bytes, NULL); | |
| if (parsed == NULL) { | |
| /* Ignore ValueError as the fuzzer will more than likely | |
| generate some invalid json and values */ | |
| if (PyErr_ExceptionMatches(PyExc_ValueError) || | |
| /* Ignore RecursionError as the fuzzer generates long sequences of | |
| arrays such as `[[[...` */ | |
| PyErr_ExceptionMatches(PyExc_RecursionError) || | |
| /* Ignore unicode errors, invalid byte sequences are common */ | |
| PyErr_ExceptionMatches(PyExc_UnicodeDecodeError) | |
| ) { | |
| PyErr_Clear(); | |
| } | |
| } | |
| Py_DECREF(input_bytes); | |
| Py_XDECREF(parsed); | |
| return 0; | |
| } | |
| #define MAX_RE_TEST_SIZE 0x10000 | |
| PyObject* sre_compile_method = NULL; | |
| PyObject* sre_error_exception = NULL; | |
| int SRE_FLAG_DEBUG = 0; | |
| /* Called by LLVMFuzzerTestOneInput for initialization */ | |
| static int init_sre_compile() { | |
| /* Import sre_compile.compile and sre.error */ | |
| PyObject* sre_compile_module = PyImport_ImportModule("sre_compile"); | |
| if (sre_compile_module == NULL) { | |
| return 0; | |
| } | |
| sre_compile_method = PyObject_GetAttrString(sre_compile_module, "compile"); | |
| if (sre_compile_method == NULL) { | |
| return 0; | |
| } | |
| PyObject* sre_constants = PyImport_ImportModule("sre_constants"); | |
| if (sre_constants == NULL) { | |
| return 0; | |
| } | |
| sre_error_exception = PyObject_GetAttrString(sre_constants, "error"); | |
| if (sre_error_exception == NULL) { | |
| return 0; | |
| } | |
| PyObject* debug_flag = PyObject_GetAttrString(sre_constants, "SRE_FLAG_DEBUG"); | |
| if (debug_flag == NULL) { | |
| return 0; | |
| } | |
| SRE_FLAG_DEBUG = PyLong_AsLong(debug_flag); | |
| return 1; | |
| } | |
| /* Fuzz _sre.compile(x) */ | |
| static int fuzz_sre_compile(const char* data, size_t size) { | |
| /* Ignore really long regex patterns that will timeout the fuzzer */ | |
| if (size > MAX_RE_TEST_SIZE) { | |
| return 0; | |
| } | |
| /* We treat the first 2 bytes of the input as a number for the flags */ | |
| if (size < 2) { | |
| return 0; | |
| } | |
| uint16_t flags = ((uint16_t*) data)[0]; | |
| /* We remove the SRE_FLAG_DEBUG if present. This is because it | |
| prints to stdout which greatly decreases fuzzing speed */ | |
| flags &= ~SRE_FLAG_DEBUG; | |
| /* Pull the pattern from the remaining bytes */ | |
| PyObject* pattern_bytes = PyBytes_FromStringAndSize(data + 2, size - 2); | |
| if (pattern_bytes == NULL) { | |
| return 0; | |
| } | |
| PyObject* flags_obj = PyLong_FromUnsignedLong(flags); | |
| if (flags_obj == NULL) { | |
| Py_DECREF(pattern_bytes); | |
| return 0; | |
| } | |
| /* compiled = _sre.compile(data[2:], data[0:2] */ | |
| PyObject* compiled = PyObject_CallFunctionObjArgs( | |
| sre_compile_method, pattern_bytes, flags_obj, NULL); | |
| /* Ignore ValueError as the fuzzer will more than likely | |
| generate some invalid combination of flags */ | |
| if (compiled == NULL && PyErr_ExceptionMatches(PyExc_ValueError)) { | |
| PyErr_Clear(); | |
| } | |
| /* Ignore some common errors thrown by sre_parse: | |
| Overflow, Assertion and Index */ | |
| if (compiled == NULL && (PyErr_ExceptionMatches(PyExc_OverflowError) || | |
| PyErr_ExceptionMatches(PyExc_AssertionError) || | |
| PyErr_ExceptionMatches(PyExc_IndexError)) | |
| ) { | |
| PyErr_Clear(); | |
| } | |
| /* Ignore re.error */ | |
| if (compiled == NULL && PyErr_ExceptionMatches(sre_error_exception)) { | |
| PyErr_Clear(); | |
| } | |
| Py_DECREF(pattern_bytes); | |
| Py_DECREF(flags_obj); | |
| Py_XDECREF(compiled); | |
| return 0; | |
| } | |
| /* Some random patterns used to test re.match. | |
| Be careful not to add catostraphically slow regexes here, we want to | |
| exercise the matching code without causing timeouts.*/ | |
| static const char* regex_patterns[] = { | |
| ".", "^", "abc", "abc|def", "^xxx$", "\\b", "()", "[a-zA-Z0-9]", | |
| "abc+", "[^A-Z]", "[x]", "(?=)", "a{z}", "a+b", "a*?", "a??", "a+?", | |
| "{}", "a{,}", "{", "}", "^\\(*\\d{3}\\)*( |-)*\\d{3}( |-)*\\d{4}$", | |
| "(?:a*)*", "a{1,2}?" | |
| }; | |
| const size_t NUM_PATTERNS = sizeof(regex_patterns) / sizeof(regex_patterns[0]); | |
| PyObject** compiled_patterns = NULL; | |
| /* Called by LLVMFuzzerTestOneInput for initialization */ | |
| static int init_sre_match() { | |
| PyObject* re_module = PyImport_ImportModule("re"); | |
| if (re_module == NULL) { | |
| return 0; | |
| } | |
| compiled_patterns = (PyObject**) PyMem_RawMalloc( | |
| sizeof(PyObject*) * NUM_PATTERNS); | |
| if (compiled_patterns == NULL) { | |
| PyErr_NoMemory(); | |
| return 0; | |
| } | |
| /* Precompile all the regex patterns on the first run for faster fuzzing */ | |
| for (size_t i = 0; i < NUM_PATTERNS; i++) { | |
| PyObject* compiled = PyObject_CallMethod( | |
| re_module, "compile", "y", regex_patterns[i]); | |
| /* Bail if any of the patterns fail to compile */ | |
| if (compiled == NULL) { | |
| return 0; | |
| } | |
| compiled_patterns[i] = compiled; | |
| } | |
| return 1; | |
| } | |
| /* Fuzz re.match(x) */ | |
| static int fuzz_sre_match(const char* data, size_t size) { | |
| if (size < 1 || size > MAX_RE_TEST_SIZE) { | |
| return 0; | |
| } | |
| /* Use the first byte as a uint8_t specifying the index of the | |
| regex to use */ | |
| unsigned char idx = (unsigned char) data[0]; | |
| idx = idx % NUM_PATTERNS; | |
| /* Pull the string to match from the remaining bytes */ | |
| PyObject* to_match = PyBytes_FromStringAndSize(data + 1, size - 1); | |
| if (to_match == NULL) { | |
| return 0; | |
| } | |
| PyObject* pattern = compiled_patterns[idx]; | |
| PyObject* match_callable = PyObject_GetAttrString(pattern, "match"); | |
| PyObject* matches = PyObject_CallFunctionObjArgs(match_callable, to_match, NULL); | |
| Py_XDECREF(matches); | |
| Py_DECREF(match_callable); | |
| Py_DECREF(to_match); | |
| return 0; | |
| } | |
| #define MAX_CSV_TEST_SIZE 0x10000 | |
| PyObject* csv_module = NULL; | |
| PyObject* csv_error = NULL; | |
| /* Called by LLVMFuzzerTestOneInput for initialization */ | |
| static int init_csv_reader() { | |
| /* Import csv and csv.Error */ | |
| csv_module = PyImport_ImportModule("csv"); | |
| if (csv_module == NULL) { | |
| return 0; | |
| } | |
| csv_error = PyObject_GetAttrString(csv_module, "Error"); | |
| return csv_error != NULL; | |
| } | |
| /* Fuzz csv.reader([x]) */ | |
| static int fuzz_csv_reader(const char* data, size_t size) { | |
| if (size < 1 || size > MAX_CSV_TEST_SIZE) { | |
| return 0; | |
| } | |
| /* Ignore non null-terminated strings since _csv can't handle | |
| embeded nulls */ | |
| if (memchr(data, '\0', size) == NULL) { | |
| return 0; | |
| } | |
| PyObject* s = PyUnicode_FromString(data); | |
| /* Ignore exceptions until we have a valid string */ | |
| if (s == NULL) { | |
| PyErr_Clear(); | |
| return 0; | |
| } | |
| /* Split on \n so we can test multiple lines */ | |
| PyObject* lines = PyObject_CallMethod(s, "split", "s", "\n"); | |
| if (lines == NULL) { | |
| Py_DECREF(s); | |
| return 0; | |
| } | |
| PyObject* reader = PyObject_CallMethod(csv_module, "reader", "N", lines); | |
| if (reader) { | |
| /* Consume all of the reader as an iterator */ | |
| PyObject* parsed_line; | |
| while ((parsed_line = PyIter_Next(reader))) { | |
| Py_DECREF(parsed_line); | |
| } | |
| } | |
| /* Ignore csv.Error because we're probably going to generate | |
| some bad files (embeded new-lines, unterminated quotes etc) */ | |
| if (PyErr_ExceptionMatches(csv_error)) { | |
| PyErr_Clear(); | |
| } | |
| Py_XDECREF(reader); | |
| Py_DECREF(s); | |
| return 0; | |
| } | |
| /* Run fuzzer and abort on failure. */ | |
| static int _run_fuzz(const uint8_t *data, size_t size, int(*fuzzer)(const char* , size_t)) { | |
| int rv = fuzzer((const char*) data, size); | |
| if (PyErr_Occurred()) { | |
| /* Fuzz tests should handle expected errors for themselves. | |
| This is last-ditch check in case they didn't. */ | |
| PyErr_Print(); | |
| abort(); | |
| } | |
| /* Someday the return value might mean something, propagate it. */ | |
| return rv; | |
| } | |
| /* CPython generates a lot of leak warnings for whatever reason. */ | |
| int __lsan_is_turned_off(void) { return 1; } | |
| int LLVMFuzzerInitialize(int *argc, char ***argv) { | |
| wchar_t* wide_program_name = Py_DecodeLocale(*argv[0], NULL); | |
| Py_SetProgramName(wide_program_name); | |
| return 0; | |
| } | |
| /* Fuzz test interface. | |
| This returns the bitwise or of all fuzz test's return values. | |
| All fuzz tests must return 0, as all nonzero return codes are reserved for | |
| future use -- we propagate the return values for that future case. | |
| (And we bitwise or when running multiple tests to verify that normally we | |
| only return 0.) */ | |
| int LLVMFuzzerTestOneInput(const uint8_t *data, size_t size) { | |
| if (!Py_IsInitialized()) { | |
| /* LLVMFuzzerTestOneInput is called repeatedly from the same process, | |
| with no separate initialization phase, sadly, so we need to | |
| initialize CPython ourselves on the first run. */ | |
| Py_InitializeEx(0); | |
| } | |
| int rv = 0; | |
| #if !defined(_Py_FUZZ_ONE) || defined(_Py_FUZZ_fuzz_builtin_float) | |
| rv |= _run_fuzz(data, size, fuzz_builtin_float); | |
| #endif | |
| #if !defined(_Py_FUZZ_ONE) || defined(_Py_FUZZ_fuzz_builtin_int) | |
| rv |= _run_fuzz(data, size, fuzz_builtin_int); | |
| #endif | |
| #if !defined(_Py_FUZZ_ONE) || defined(_Py_FUZZ_fuzz_builtin_unicode) | |
| rv |= _run_fuzz(data, size, fuzz_builtin_unicode); | |
| #endif | |
| #if !defined(_Py_FUZZ_ONE) || defined(_Py_FUZZ_fuzz_json_loads) | |
| static int JSON_LOADS_INITIALIZED = 0; | |
| if (!JSON_LOADS_INITIALIZED && !init_json_loads()) { | |
| PyErr_Print(); | |
| abort(); | |
| } else { | |
| JSON_LOADS_INITIALIZED = 1; | |
| } | |
| rv |= _run_fuzz(data, size, fuzz_json_loads); | |
| #endif | |
| #if !defined(_Py_FUZZ_ONE) || defined(_Py_FUZZ_fuzz_sre_compile) | |
| static int SRE_COMPILE_INITIALIZED = 0; | |
| if (!SRE_COMPILE_INITIALIZED && !init_sre_compile()) { | |
| PyErr_Print(); | |
| abort(); | |
| } else { | |
| SRE_COMPILE_INITIALIZED = 1; | |
| } | |
| rv |= _run_fuzz(data, size, fuzz_sre_compile); | |
| #endif | |
| #if !defined(_Py_FUZZ_ONE) || defined(_Py_FUZZ_fuzz_sre_match) | |
| static int SRE_MATCH_INITIALIZED = 0; | |
| if (!SRE_MATCH_INITIALIZED && !init_sre_match()) { | |
| PyErr_Print(); | |
| abort(); | |
| } else { | |
| SRE_MATCH_INITIALIZED = 1; | |
| } | |
| rv |= _run_fuzz(data, size, fuzz_sre_match); | |
| #endif | |
| #if !defined(_Py_FUZZ_ONE) || defined(_Py_FUZZ_fuzz_csv_reader) | |
| static int CSV_READER_INITIALIZED = 0; | |
| if (!CSV_READER_INITIALIZED && !init_csv_reader()) { | |
| PyErr_Print(); | |
| abort(); | |
| } else { | |
| CSV_READER_INITIALIZED = 1; | |
| } | |
| rv |= _run_fuzz(data, size, fuzz_csv_reader); | |
| #endif | |
| return rv; | |
| } |