def import_annotation(binary: Binary, type_: FileType,
path: Path) -> Annotation:
"""Imports functions from annotation file and creates Annotation object.
Args:
binary: The Binary to which to import functions.
Returns:
the Annotation, with newly created functions.
"""
if not path.is_file():
raise FileNotFoundError
logger.info(f"Importing {type_.name} file from {path}")
ann_cls, fnc_gen = FileTypeMapping[type_]
binary.partition()
annotation = ann_cls(binary=binary, path=str(path))
meta = fnc_gen(path)
for name, addr, size, mode in meta:
if not binary.range_is_valid(addr, addr + size):
logger.debug(
f"{name} @{addr:#08X}-{addr+size:#08X} not in valid range")
continue
if len(name) > 0 and binary.range_is_valid(addr, addr + size):
fnc = Function.get(binary=binary, addr=addr)
if not fnc:
fnc = binary.functions.create(addr=addr,
size=size,
name=name,
mode=mode)
annotation.functions.add(fnc)
binary.annotations.add(annotation)
return annotation
def get_or_create_binary(path: Path, make_target=False) -> Binary:
"""import_binary creates a Binary and stores it in database
Args:
binary_path: the file path of the binary
Returns: Binary
"""
logger.info(f"retrieving binary for path {path}")
binary = Binary.get(filepath=str(path))
if not binary:
if not path.is_file():
raise FileNotFoundError
binary = Binary(filepath=str(path), name=path.name)
elif make_target:
binary.is_target = True
binary.partition()
return binary
def match_matchers_against(target: Binary,
graph=None,
parallelize=True) -> Iterable[Match]:
"""match_candidates_against matches all candidates against the given
Binary.
Args:
target: the target binary to match the candidates against
"""
settings = SettingsStorage.get_settings()
parallelize = parallelize or settings.get("matcher_parallelization", False)
if graph is None:
graph = makeGraph()
bin_ = target.read()
partitions = target.partition()
if parallelize:
matches = parallel_prepartioned_graph_match(graph, bin_, partitions)
else:
matches = prepartioned_graph_match(graph, bin_, partitions)
match_results = []
for matchers, size, end in matches:
start = end - size
match, new = Match.deduplicate(target, start, size,
dict(certainty=1 / len(matchers)))
for matcher in matchers:
match.matched_by.add(matcher)
if new:
target.matches.add(match)
match_results.append(match)
orm.commit()
if settings.get("find_fnc_starts", False):
starts = find_starts(target,
start_cut=settings.get("fnc_start_size", 8))
match_results.extend(starts)
orm.commit()
return match_results