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https://github.com/mandiant/capa.git
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mypy
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@@ -81,6 +81,7 @@ def get_capa_results(args):
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rules (capa.rules.RuleSet): the rules to match
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signatures (List[str]): list of file system paths to signature files
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format (str): the name of the sample file format
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os (str): the name of the operating system
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path (str): the file system path to the sample to process
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args is a tuple because i'm not quite sure how to unpack multiple arguments using `map`.
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@@ -96,12 +97,12 @@ def get_capa_results(args):
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meta (dict): the meta analysis results
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capabilities (dict): the matched capabilities and their result objects
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"""
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rules, sigpaths, format, path = args
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rules, sigpaths, format, os, path = args
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should_save_workspace = os.environ.get("CAPA_SAVE_WORKSPACE") not in ("0", "no", "NO", "n", None)
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logger.info("computing capa results for: %s", path)
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try:
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extractor = capa.main.get_extractor(
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path, format, capa.main.BACKEND_VIV, sigpaths, should_save_workspace, disable_progress=True
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path, format, os, capa.main.BACKEND_VIV, sigpaths, should_save_workspace, disable_progress=True
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)
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except capa.main.UnsupportedFormatError:
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# i'm 100% sure if multiprocessing will reliably raise exceptions across process boundaries.
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@@ -127,7 +128,7 @@ def get_capa_results(args):
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"error": f"unexpected error: {e}",
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}
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meta = capa.main.collect_metadata([], path, [], extractor)
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meta = capa.main.collect_metadata([], path, format, os, [], extractor)
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capabilities, counts = capa.main.find_capabilities(rules, extractor, disable_progress=True)
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meta["analysis"].update(counts)
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meta["analysis"]["layout"] = capa.main.compute_layout(rules, extractor, capabilities)
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@@ -142,7 +143,7 @@ def main(argv=None):
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argv = sys.argv[1:]
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parser = argparse.ArgumentParser(description="detect capabilities in programs.")
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capa.main.install_common_args(parser, wanted={"rules", "signatures"})
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capa.main.install_common_args(parser, wanted={"rules", "signatures", "format", "os"})
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parser.add_argument("input", type=str, help="Path to directory of files to recursively analyze")
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parser.add_argument(
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"-n", "--parallelism", type=int, default=multiprocessing.cpu_count(), help="parallelism factor"
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