Carsten Wiecher

16 papers A 1B 1Journal 8Unranked 5
YearRankTypeTitle / Venue / Authors
2024 J jnl
Syst. Eng.
Carsten Wiecher, Constantin Mandel, Matthias Günther, Jannik Fischbach, Joel Greenyer, Matthias Greinert, Carsten Wolff, Roman Dumitrescu, Daniel Méndez, Albert Albers
2024
Carsten Wiecher
2023 J jnl
J. Syst. Softw.
Jannik Fischbach, Julian Frattini, Andreas Vogelsang, Daniel Méndez, Michael Unterkalmsteiner, Andreas Wehrle, Pablo Restrepo Henao, Parisa Yousefi, Tedi Juricic, Jeannette Radduenz, Carsten Wiecher
2022 J jnl
CoRR
Jannik Fischbach, Julian Frattini, Andreas Vogelsang, Daniel Méndez, Michael Unterkalmsteiner, Andreas Wehrle, Pablo Restrepo Henao, Parisa Yousefi, Tedi Juricic, Jeannette Radduenz, Carsten Wiecher
2022 J jnl
CoRR
Carsten Wiecher, Constantin Mandel, Matthias Günther, Jannik Fischbach, Joel Greenyer, Matthias Greinert, Carsten Wolff, Roman Dumitrescu, Daniel Méndez, Albert Albers
2022 J jnl
CoRR
Carsten Wiecher, Philipp Tendyra, Carsten Wolff
2021 conf
IDAACS
Carsten Wolff, Philipp Tendyra, Carsten Wiecher
2021 conf
REFSQ Workshops
Carsten Wiecher, Joel Greenyer
2021 A conf
MoDELS
Carsten Wiecher, Jannik Fischbach, Joel Greenyer, Andreas Vogelsang, Carsten Wolff, Roman Dumitrescu
2021 J jnl
CoRR
Carsten Wiecher, Jannik Fischbach, Joel Greenyer, Andreas Vogelsang, Carsten Wolff, Roman Dumitrescu
2021 B conf
REFSQ
Carsten Wiecher, Joel Greenyer, Carsten Wolff, Harald Anacker, Roman Dumitrescu
2021 J jnl
CoRR
Carsten Wiecher, Joel Greenyer, Carsten Wolff, Harald Anacker, Roman Dumitrescu
2021 J jnl
CoRR
Carsten Wiecher, Carsten Wolff, Harald Anacker, Roman Dumitrescu
2020 conf
MoDELS (Companion)
Carsten Wiecher
2020 conf
MoDELS (Companion)
Carsten Wiecher, Sergej Japs, Lydia Kaiser, Joel Greenyer, Roman Dumitrescu, Carsten Wolff
2019 conf
MoDELS (Companion)
Carsten Wiecher, Joel Greenyer, Jan Korte
redb/extractors/js_extractors/js_suspicious_apis.py
← Index redb/extractors/js_extractors/js_suspicious_apis.py python
import inspect
from datetime import datetime, timezone
from typing import Any

from redb.extractors.enum import Tag
from redb.extractors.js_extractor import JSExtractor
from redb.extractors.js_extractors.js_patterns import CATEGORIES, PATTERNS


# Backwards-compatible export: `{category: [(raw_pattern_string, api_name), ...]}`
# in canonical PATTERNS insertion order (code_execution, network, filesystem,
# process, registry, crypto_encoding, dom_manipulation). Kept so external
# callers (notably JSDeobfuscationExtractor pre-cleanup) keep working until
# they are migrated to PATTERNS directly.
SUSPICIOUS_APIS: "dict[str, list[tuple[str, str]]]" = {}
for _name, _compiled in PATTERNS.items():
    SUSPICIOUS_APIS.setdefault(CATEGORIES[_name], []).append((_compiled.pattern, _name))


class JSSuspiciousAPIsExtractor(JSExtractor):

    def __init__(
        self, filepath, log, exporters=None, index_prefix=None,
        known_benign=False, known_malicious=False, source=None, context=None,
    ):
        super().__init__(
            filepath, log, exporters, index_prefix,
            known_benign, known_malicious, source, context=context,
        )
        self.api_findings = None
        self.log.debug(inspect.currentframe().f_code.co_name)

    def tag(self):
        return Tag.JS_SUSPICIOUS_APIS.value

    def _get_context_snippet(self, line, max_len=200):
        """Get a truncated context snippet around a match."""
        line = line.strip()
        if len(line) > max_len:
            return line[:max_len] + "..."
        return line

    def extract(self):
        src = self.js_source
        if not src:
            return None

        # Pass 1: shared per-sample scan over the raw source. The dict contains
        # entries for both PATTERNS and FEATURE_PATTERNS; the loop below only
        # consults PATTERNS keys, so feature-only entries are ignored.
        raw_scan = self._context.scan or {}
        raw_lines = self.lines

        # Pass 2: same patterns over the deobfuscated text, when the
        # deobfuscator produced something meaningfully different. APIs hidden
        # behind one obfuscation layer (Vjw0rm-style array.join + eval,
        # Dean-Edwards packers, jjencode, ...) only surface here. The scan is
        # cached on JSContext so JSDeobfuscationExtractor (which computes the
        # new_apis_found diff) reuses the same result.
        deobf_scan = self._context.scan_deobfuscated
        if deobf_scan:
            deobf_text, _ = self._context.deobfuscated
            deobf_lines = deobf_text.splitlines()
        else:
            deobf_lines = []

        findings = []
        # Iterate PATTERNS in canonical order so output is deterministic and
        # matches the historical category/pattern ordering. For each api_name,
        # raw findings take precedence; if an API is found only in the
        # deobfuscated text, we surface it as a row tagged revealed_by_deobf=1
        # with line numbers / snippets pulled from the deobfuscated source.
        for api_name in PATTERNS:
            raw_info = raw_scan.get(api_name)
            if raw_info:
                line_numbers = raw_info["lines"]
                lines_for_snippets = raw_lines
                revealed_by_deobf = 0
            else:
                deobf_info = deobf_scan.get(api_name)
                if not deobf_info:
                    continue
                line_numbers = deobf_info["lines"]
                lines_for_snippets = deobf_lines
                revealed_by_deobf = 1

            snippets = [
                self._get_context_snippet(lines_for_snippets[ln - 1])
                for ln in line_numbers[:3]
                if 0 < ln <= len(lines_for_snippets)
            ]
            findings.append({
                "api_name": api_name,
                "api_category": CATEGORIES[api_name],
                # Historical semantics: count = number of unique lines with a
                # match, not total in-source match count.
                "call_count": len(line_numbers),
                "line_numbers": line_numbers,
                "context_snippet": " | ".join(snippets),
                "revealed_by_deobf": revealed_by_deobf,
            })

        if not findings:
            return None

        self.api_findings = findings
        return findings

    def prepare_export_data(self, exporter_type: str) -> Any:
        if exporter_type == "ClickHouseExporter":
            if not self.api_findings:
                return None

            current_time = datetime.now(timezone.utc)
            data = []
            for f in self.api_findings:
                data.append([
                    self.sha256,
                    f['api_name'],
                    f['api_category'],
                    f['call_count'],
                    f['line_numbers'],
                    f['context_snippet'],
                    f['revealed_by_deobf'],
                    current_time,
                ])

            column_names = [
                "sha256", "api_name", "api_category",
                "call_count", "line_numbers", "context_snippet",
                "revealed_by_deobf",
                "analysis_date",
            ]

            column_type_names = [
                "FixedString(64)", "String", "LowCardinality(String)",
                "UInt32", "Array(UInt32)", "String",
                "UInt8",
                "DateTime64(3, 'UTC')",
            ]

            return (data, column_names, column_type_names)

    def get_clickhouse_table(self) -> str:
        return "redb_js_suspicious_apis"