mirror of
https://github.com/mandiant/capa.git
synced 2025-12-12 23:59:48 -08:00
Merge branch 'master' into function-id-flirt
This commit is contained in:
William Ballenthin
@@ -1,220 +1,220 @@
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#!/usr/bin/env python
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"""
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bulk-process
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Invoke capa recursively against a directory of samples
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and emit a JSON document mapping the file paths to their results.
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By default, this will use subprocesses for parallelism.
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Use `-n/--parallelism` to change the subprocess count from
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the default of current CPU count.
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Use `--no-mp` to use threads instead of processes,
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which is probably not useful unless you set `--parallelism=1`.
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example:
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$ python scripts/bulk-process /tmp/suspicious
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{
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"/tmp/suspicious/suspicious.dll_": {
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"rules": {
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"encode data using XOR": {
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"matches": {
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"268440358": {
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[...]
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"/tmp/suspicious/1.dll_": { ... }
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"/tmp/suspicious/2.dll_": { ... }
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}
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usage:
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usage: bulk-process.py [-h] [-r RULES] [-d] [-q] [-n PARALLELISM] [--no-mp]
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input
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detect capabilities in programs.
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positional arguments:
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input Path to directory of files to recursively analyze
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optional arguments:
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-h, --help show this help message and exit
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-r RULES, --rules RULES
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Path to rule file or directory, use embedded rules by
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default
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-d, --debug Enable debugging output on STDERR
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-q, --quiet Disable all output but errors
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-n PARALLELISM, --parallelism PARALLELISM
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parallelism factor
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--no-mp disable subprocesses
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Copyright (C) 2020 FireEye, Inc. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at: [package root]/LICENSE.txt
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Unless required by applicable law or agreed to in writing, software distributed under the License
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is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and limitations under the License.
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"""
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import sys
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import json
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import logging
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import os.path
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import argparse
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import multiprocessing
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import multiprocessing.pool
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import capa
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import capa.main
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import capa.rules
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import capa.render
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logger = logging.getLogger("capa")
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def get_capa_results(args):
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"""
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run capa against the file at the given path, using the given rules.
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args is a tuple, containing:
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rules (capa.rules.RuleSet): the rules to match
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format (str): the name of the sample file format
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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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returns an dict with two required keys:
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path (str): the file system path of the sample to process
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status (str): either "error" or "ok"
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when status == "error", then a human readable message is found in property "error".
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when status == "ok", then the capa results are found in the property "ok".
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the capa results are a dictionary with the following keys:
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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, format, path = args
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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(path, format, capa.main.BACKEND_VIV, args.signatures, disable_progress=True)
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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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# so instead, return an object with explicit success/failure status.
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#
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# if success, then status=ok, and results found in property "ok"
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# if error, then status=error, and human readable message in property "error"
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return {
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"path": path,
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"status": "error",
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"error": "input file does not appear to be a PE file: %s" % path,
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}
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except capa.main.UnsupportedRuntimeError:
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return {
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"path": path,
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"status": "error",
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"error": "unsupported runtime or Python interpreter",
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}
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except Exception as e:
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return {
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"path": path,
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"status": "error",
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"error": "unexpected error: %s" % (e),
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}
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meta = capa.main.collect_metadata("", path, "", format, 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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return {
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"path": path,
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"status": "ok",
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"ok": {
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"meta": meta,
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"capabilities": capabilities,
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},
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}
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def main(argv=None):
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if argv is 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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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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)
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parser.add_argument("--no-mp", action="store_true", help="disable subprocesses")
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args = parser.parse_args(args=argv)
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capa.main.handle_common_args(args)
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if args.rules == "(embedded rules)":
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logger.info("using default embedded rules")
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logger.debug("detected running from source")
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args.rules = os.path.join(os.path.dirname(__file__), "..", "rules")
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logger.debug("default rule path (source method): %s", args.rules)
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else:
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logger.info("using rules path: %s", args.rules)
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try:
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rules = capa.main.get_rules(args.rules)
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rules = capa.rules.RuleSet(rules)
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logger.info("successfully loaded %s rules", len(rules))
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except (IOError, capa.rules.InvalidRule, capa.rules.InvalidRuleSet) as e:
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logger.error("%s", str(e))
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return -1
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samples = []
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for (base, directories, files) in os.walk(args.input):
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for file in files:
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samples.append(os.path.join(base, file))
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def pmap(f, args, parallelism=multiprocessing.cpu_count()):
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"""apply the given function f to the given args using subprocesses"""
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return multiprocessing.Pool(parallelism).imap(f, args)
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def tmap(f, args, parallelism=multiprocessing.cpu_count()):
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"""apply the given function f to the given args using threads"""
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return multiprocessing.pool.ThreadPool(parallelism).imap(f, args)
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def map(f, args, parallelism=None):
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"""apply the given function f to the given args in the current thread"""
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for arg in args:
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yield f(arg)
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if args.no_mp:
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if args.parallelism == 1:
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logger.debug("using current thread mapper")
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mapper = map
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else:
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logger.debug("using threading mapper")
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mapper = tmap
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else:
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logger.debug("using process mapper")
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mapper = pmap
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results = {}
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for result in mapper(
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get_capa_results, [(rules, "pe", sample) for sample in samples], parallelism=args.parallelism
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):
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if result["status"] == "error":
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logger.warning(result["error"])
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elif result["status"] == "ok":
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meta = result["ok"]["meta"]
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capabilities = result["ok"]["capabilities"]
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# our renderer expects to emit a json document for a single sample
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# so we deserialize the json document, store it in a larger dict, and we'll subsequently re-encode.
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results[result["path"]] = json.loads(capa.render.render_json(meta, rules, capabilities))
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else:
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raise ValueError("unexpected status: %s" % (result["status"]))
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print(json.dumps(results))
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logger.info("done.")
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return 0
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if __name__ == "__main__":
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sys.exit(main())
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#!/usr/bin/env python
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"""
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bulk-process
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Invoke capa recursively against a directory of samples
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and emit a JSON document mapping the file paths to their results.
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|
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By default, this will use subprocesses for parallelism.
|
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Use `-n/--parallelism` to change the subprocess count from
|
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the default of current CPU count.
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Use `--no-mp` to use threads instead of processes,
|
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which is probably not useful unless you set `--parallelism=1`.
|
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example:
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$ python scripts/bulk-process /tmp/suspicious
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{
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"/tmp/suspicious/suspicious.dll_": {
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"rules": {
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"encode data using XOR": {
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"matches": {
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"268440358": {
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[...]
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"/tmp/suspicious/1.dll_": { ... }
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"/tmp/suspicious/2.dll_": { ... }
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}
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usage:
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usage: bulk-process.py [-h] [-r RULES] [-d] [-q] [-n PARALLELISM] [--no-mp]
|
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input
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|
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detect capabilities in programs.
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|
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positional arguments:
|
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input Path to directory of files to recursively analyze
|
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|
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optional arguments:
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-h, --help show this help message and exit
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-r RULES, --rules RULES
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Path to rule file or directory, use embedded rules by
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default
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-d, --debug Enable debugging output on STDERR
|
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-q, --quiet Disable all output but errors
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-n PARALLELISM, --parallelism PARALLELISM
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||||
parallelism factor
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--no-mp disable subprocesses
|
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Copyright (C) 2020 FireEye, Inc. All Rights Reserved.
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||||
Licensed under the Apache License, Version 2.0 (the "License");
|
||||
you may not use this file except in compliance with the License.
|
||||
You may obtain a copy of the License at: [package root]/LICENSE.txt
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License
|
||||
is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
See the License for the specific language governing permissions and limitations under the License.
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||||
"""
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import sys
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import json
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import logging
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import os.path
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import argparse
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import multiprocessing
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import multiprocessing.pool
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import capa
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import capa.main
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import capa.rules
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import capa.render
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logger = logging.getLogger("capa")
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def get_capa_results(args):
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"""
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run capa against the file at the given path, using the given rules.
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args is a tuple, containing:
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rules (capa.rules.RuleSet): the rules to match
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format (str): the name of the sample file format
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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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|
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returns an dict with two required keys:
|
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path (str): the file system path of the sample to process
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status (str): either "error" or "ok"
|
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|
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when status == "error", then a human readable message is found in property "error".
|
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when status == "ok", then the capa results are found in the property "ok".
|
||||
|
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the capa results are a dictionary with the following keys:
|
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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, format, path = args
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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(path, format, capa.main.BACKEND_VIV, args.signatures, disable_progress=True)
|
||||
except capa.main.UnsupportedFormatError:
|
||||
# i'm 100% sure if multiprocessing will reliably raise exceptions across process boundaries.
|
||||
# so instead, return an object with explicit success/failure status.
|
||||
#
|
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# if success, then status=ok, and results found in property "ok"
|
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# if error, then status=error, and human readable message in property "error"
|
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return {
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"path": path,
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"status": "error",
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"error": "input file does not appear to be a PE file: %s" % path,
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}
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except capa.main.UnsupportedRuntimeError:
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return {
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"path": path,
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"status": "error",
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"error": "unsupported runtime or Python interpreter",
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}
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except Exception as e:
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return {
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"path": path,
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"status": "error",
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"error": "unexpected error: %s" % (e),
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}
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meta = capa.main.collect_metadata("", path, "", format, 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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|
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return {
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"path": path,
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"status": "ok",
|
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"ok": {
|
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"meta": meta,
|
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"capabilities": capabilities,
|
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},
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}
|
||||
|
||||
|
||||
def main(argv=None):
|
||||
if argv is None:
|
||||
argv = sys.argv[1:]
|
||||
|
||||
parser = argparse.ArgumentParser(description="detect capabilities in programs.")
|
||||
capa.main.install_common_args(parser, wanted={"rules", "signatures"})
|
||||
parser.add_argument("input", type=str, help="Path to directory of files to recursively analyze")
|
||||
parser.add_argument(
|
||||
"-n", "--parallelism", type=int, default=multiprocessing.cpu_count(), help="parallelism factor"
|
||||
)
|
||||
parser.add_argument("--no-mp", action="store_true", help="disable subprocesses")
|
||||
args = parser.parse_args(args=argv)
|
||||
capa.main.handle_common_args(args)
|
||||
|
||||
if args.rules == "(embedded rules)":
|
||||
logger.info("using default embedded rules")
|
||||
logger.debug("detected running from source")
|
||||
args.rules = os.path.join(os.path.dirname(__file__), "..", "rules")
|
||||
logger.debug("default rule path (source method): %s", args.rules)
|
||||
else:
|
||||
logger.info("using rules path: %s", args.rules)
|
||||
|
||||
try:
|
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rules = capa.main.get_rules(args.rules)
|
||||
rules = capa.rules.RuleSet(rules)
|
||||
logger.info("successfully loaded %s rules", len(rules))
|
||||
except (IOError, capa.rules.InvalidRule, capa.rules.InvalidRuleSet) as e:
|
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logger.error("%s", str(e))
|
||||
return -1
|
||||
|
||||
samples = []
|
||||
for (base, directories, files) in os.walk(args.input):
|
||||
for file in files:
|
||||
samples.append(os.path.join(base, file))
|
||||
|
||||
def pmap(f, args, parallelism=multiprocessing.cpu_count()):
|
||||
"""apply the given function f to the given args using subprocesses"""
|
||||
return multiprocessing.Pool(parallelism).imap(f, args)
|
||||
|
||||
def tmap(f, args, parallelism=multiprocessing.cpu_count()):
|
||||
"""apply the given function f to the given args using threads"""
|
||||
return multiprocessing.pool.ThreadPool(parallelism).imap(f, args)
|
||||
|
||||
def map(f, args, parallelism=None):
|
||||
"""apply the given function f to the given args in the current thread"""
|
||||
for arg in args:
|
||||
yield f(arg)
|
||||
|
||||
if args.no_mp:
|
||||
if args.parallelism == 1:
|
||||
logger.debug("using current thread mapper")
|
||||
mapper = map
|
||||
else:
|
||||
logger.debug("using threading mapper")
|
||||
mapper = tmap
|
||||
else:
|
||||
logger.debug("using process mapper")
|
||||
mapper = pmap
|
||||
|
||||
results = {}
|
||||
for result in mapper(
|
||||
get_capa_results, [(rules, "pe", sample) for sample in samples], parallelism=args.parallelism
|
||||
):
|
||||
if result["status"] == "error":
|
||||
logger.warning(result["error"])
|
||||
elif result["status"] == "ok":
|
||||
meta = result["ok"]["meta"]
|
||||
capabilities = result["ok"]["capabilities"]
|
||||
# our renderer expects to emit a json document for a single sample
|
||||
# so we deserialize the json document, store it in a larger dict, and we'll subsequently re-encode.
|
||||
results[result["path"]] = json.loads(capa.render.render_json(meta, rules, capabilities))
|
||||
else:
|
||||
raise ValueError("unexpected status: %s" % (result["status"]))
|
||||
|
||||
print(json.dumps(results))
|
||||
|
||||
logger.info("done.")
|
||||
|
||||
return 0
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
sys.exit(main())
|
||||
|
||||
Reference in New Issue
Block a user