Maksym Gaiduk

47 papers B 25Journal 3Unranked 19
YearRankTypeTitle / Venue / Authors
2025 B conf
KES
Wilhelm Daniel Scherz, Maksym Gaiduk, Jorge Ávila-Campos, Ralf Seepold, Natividad Martínez Madrid, Paula Herrera, Julián David Echeverry
2025 B conf
KES
Maksym Gaiduk, Ángel Serrano Alarcón, Natividad Martínez Madrid, Ralf Seepold
2025 B conf
KES
Ralf Seepold, Natividad Martínez Madrid, Aaron Raymond See, Tsung-Lu Michael Lee, Maksym Gaiduk, Wilhelm Daniel Scherz, Daniel Vélez Gutiérrez
2025 B conf
KES
Wilhelm Daniel Scherz, Ángel Serrano Alarcón, Maksym Gaiduk, Andrei Boiko, Rodion Kraft, Ann Nosseir, Natividad Martínez Madrid, Juan Antonio Ortega, Ralf Seepold
2024 B conf
KES
Akhmadbek Asadov, Maksym Gaiduk, Juan Antonio Ortega, Natividad Martínez Madrid, Ralf Seepold
2024 B conf
KES
Ángel Serrano Alarcón, Maksym Gaiduk, Natividad Martínez Madrid, Ralf Seepold, Juan Antonio Ortega
2024 B conf
KES
Ángel Serrano Alarcón, Maksym Gaiduk, Natividad Martínez Madrid, Ralf Seepold, Juan Antonio Ortega
2024 B conf
KES
Raúl Martín Starke, William Wannemacher, Mohammed Ali Rajeh, Ralf Seepold, Maksym Gaiduk
2024 B conf
KES
Joaquín Martín Acuña, Claudia Trancón Jiménez, Carlos Baquero Villena, Juan Antonio Ortega, Ralf Seepold, Maksym Gaiduk
2024 B conf
KES
Wilhelm Daniel Scherz, Dennis Grewe, Maksym Gaiduk, Ralf Seepold, Juan Antonio Ortega
2024 B conf
KES
Daniel Vélez, Maksym Gaiduk, Mostafa Haghi, Juan Antonio Ortega, Natividad Martínez Madrid, Ralf Seepold
2023 B conf
KES
Ángel Serrano Alarcón, Maksym Gaiduk, Natividad Martínez Madrid, Ralf Seepold
2023 B conf
KES
Andrei Boiko, Maksym Gaiduk, Ralf Seepold, Natividad Martínez Madrid
2023 J jnl
Sensors
Maksym Gaiduk, Ralf Seepold, Natividad Martínez Madrid, Juan Antonio Ortega
2023 B conf
KES
Maksym Gaiduk, Ralf Seepold, Natividad Martínez Madrid
2023 B conf
KES
Maksym Gaiduk, Ángel Serrano Alarcón, Ralf Seepold, Natividad Martínez Madrid
2023 conf
ApplePies
Andrei Boiko, Maksym Gaiduk, Natividad Martínez Madrid, Ralf Seepold
2023 J jnl
Sensors
Andrei Boiko, Maksym Gaiduk, Wilhelm Daniel Scherz, Andrea Gentili, Massimo Conti, Simone Orcioni, Natividad Martínez Madrid, Ralf Seepold
2022 B conf
KES
Petra Friedrich, Maksym Gaiduk, Ángel Serrano Alarcón, Wilhelm Daniel Scherz, Natividad Martínez Madrid, Ralf Seepold, Matthias Gaßner, Dominik Fuchs
2022 J jnl
IEEE J. Biomed. Health Informatics
Maksym Gaiduk, Juan José Perea, Ralf Seepold, Natividad Martínez Madrid, Thomas Penzel, Martin Glos, Juan Antonio Ortega
2022 B conf
KES
Akhmadbek Asadov, Andrei Boiko, Maksym Gaiduk, Wilhelm Daniel Scherz, Ralf Seepold, Natividad Martínez Madrid
2022 conf
KES-IDT
Sebastian Rätzer, Maksym Gaiduk, Ralf Seepold
2022 B conf
KES
Maksym Gaiduk, Ralf Seepold, Natividad Martínez Madrid, Simone Orcioni, Massimo Conti, Juan Antonio Ortega
2021 B conf
KES
Maksym Gaiduk, Lucas Weber, Ángel Serrano Alarcón, Ralf Seepold, Natividad Martínez Madrid, Simone Orcioni, Massimo Conti
2021 conf
EMBC
Lucas Weber, Maksym Gaiduk, Ralf Seepold, Natividad Martínez Madrid, Martin Glos, Thomas Penzel
2021 conf
ApplePies
Maksym Gaiduk, Ralf Seepold, Natividad Martínez Madrid, Thomas Penzel, Lucas Weber, Massimo Conti, Simone Orcioni, Juan Antonio Ortega
2021 B conf
KES
Simone Orcioni, Francesco Di Nardo, Sandro Fioretti, Massimo Conti, Ralf Seepold, Maksym Gaiduk, Natividad Martínez Madrid
2020 conf
ApplePies
Maksym Gaiduk, Ralf Seepold, Natividad Martínez Madrid, Juan Antonio Ortega, Massimo Conti, Simone Orcioni, Thomas Penzel, Wilhelm Daniel Scherz, Juan José Perea, Ángel Serrano Alarcón, Gerald Weiss
2020 B conf
KES
Klara Stuburic, Maksym Gaiduk, Ralf Seepold
2020 B conf
KES
Yurii Shkilniuk, Ángel Serrano Alarcón, Maksym Gaiduk, Ralf Seepold, Natividad Martínez Madrid
2020 conf
CinC
Lucas Weber, Maksym Gaiduk, Wilhelm Daniel Scherz, Ralf Seepold
2020 B conf
KES
Maksym Gaiduk, Ralf Seepold, Juan Antonio Ortega, Natividad Martínez Madrid
2020 conf
EMBC
Maksym Gaiduk, Simone Orcioni, Massimo Conti, Ralf Seepold, Thomas Penzel, Natividad Martínez Madrid, Juan Antonio Ortega
2020 conf
EMBC
Massimo Conti, Carlo Aironi, Simone Orcioni, Ralf Seepold, Maksym Gaiduk, Natividad Martínez Madrid
2020 B conf
KES
Maksym Gaiduk, Dennis Wehrle, Ralf Seepold, Juan Antonio Ortega
2020 conf
HCI (9)
Hans-Peter Hutter, Alireza Darvishy, Stephan Roth, Susanne Gäumann, Heidi Kaspar, Tatjana Thimm, Maksym Gaiduk, Sandra Evans, Martin Rosenberg
2020 conf
ApplePies
Yurii Shkilniuk, Maksym Gaiduk, Ralf Seepold
2019 conf
KES-IDT (2)
Ralf Seepold, Maksym Gaiduk, Juan Antonio Ortega, Massimo Conti, Simone Orcioni, Natividad Martínez Madrid
2019 conf
IWBBIO (1)
Mislav Juric, Maksym Gaiduk, Ralf Seepold
2019 conf
KES-IDT (2)
Maksim Sandybekov, Clemens Grabow, Maksym Gaiduk, Ralf Seepold
2019 conf
EMBC
Maksym Gaiduk, Ralf Seepold, Thomas Penzel, Juan Antonio Ortega, Martin Glos, Natividad Martínez Madrid
2019 conf
ApplePies
Maksym Gaiduk, Ralf Seepold, Natividad Martínez Madrid, Simone Orcioni, Massimo Conti
2018 conf
IWBBIO (2)
Massimo Conti, Simone Orcioni, Natividad Martínez Madrid, Maksym Gaiduk, Ralf Seepold
2018 conf
ApplePies
Eva Rodríguez de Trujillo, Ralf Seepold, Maksym Gaiduk, Natividad Martínez Madrid, Simone Orcioni, Massimo Conti
2018 B conf
KES
Eva Rodríguez de Trujillo, Ralf Seepold, Maksym Gaiduk
2018 conf
IWBBIO (2)
Maksym Gaiduk, Bruno Vunderl, Ralf Seepold, Juan Antonio Ortega, Thomas Penzel
2017 conf
IWBBIO (2)
Maksym Gaiduk, Ina Kuhn, Ralf Seepold, Juan Antonio Ortega, Natividad Martínez Madrid
redb/extractors/js_extractors/js_strings.py
← Index redb/extractors/js_extractors/js_strings.py python
import base64
import bisect
import inspect
import re
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 STRING_PATTERNS, line_offsets

# Local aliases for the compiled patterns this extractor uses. Defined and
# compiled exactly once in js_patterns.STRING_PATTERNS.
_HEX_STRING_RE = STRING_PATTERNS["hex_escape_seq"]
_UNICODE_STRING_RE = STRING_PATTERNS["unicode_escape_seq"]
_CHARCODE_RE = STRING_PATTERNS["charcode_call"]
_BASE64_STRING_RE = STRING_PATTERNS["base64_quoted"]
_CONCAT_STRING_RE = STRING_PATTERNS["concat_chain"]

# Tokeniser used inside _reconstruct_concat to pull each quoted part out of a
# matched concat chain. Compiled once at module load (was recompiled on every
# concat match before).
_CONCAT_TOKEN_RE = re.compile(r'["\']([^"\']*)["\']')


class JSStringsExtractor(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.string_findings = None
        self.log.debug(inspect.currentframe().f_code.co_name)

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

    def _decode_hex_string(self, hex_str):
        """Decode \\x41\\x42 style hex strings."""
        try:
            # Remove \\x prefix and decode
            clean = hex_str.replace('\\x', '')
            return bytes.fromhex(clean).decode('utf-8', errors='replace')
        except Exception:
            return None

    def _decode_unicode_string(self, uni_str):
        """Decode \\u0041\\u0042 style unicode strings."""
        try:
            return uni_str.encode('utf-8').decode('unicode_escape')
        except Exception:
            return None

    def _decode_charcode(self, charcode_str):
        """Decode String.fromCharCode(72, 101, 108, ...) sequences."""
        try:
            codes = [int(c.strip()) for c in charcode_str.split(',') if c.strip().isdigit()]
            return ''.join(chr(c) for c in codes if 0 <= c <= 0x10FFFF)
        except Exception:
            return None

    def _decode_base64(self, b64_str):
        """Attempt to decode base64 string."""
        try:
            decoded = base64.b64decode(b64_str)
            # Check if result is printable text
            text = decoded.decode('utf-8', errors='strict')
            # Only return if it looks like text (>80% printable)
            printable = sum(1 for c in text if c.isprintable() or c in '\n\r\t')
            if printable / len(text) > 0.8:
                return text
        except Exception:
            pass
        return None

    def _reconstruct_concat(self, concat_match):
        """Reconstruct concatenated string parts."""
        try:
            parts = _CONCAT_TOKEN_RE.findall(concat_match)
            return ''.join(parts)
        except Exception:
            return None

    def _find_line_number(self, match_start):
        """1-indexed line number for `match_start`, looked up in O(log L) via
        bisect over `self._line_offsets` (built once per extract() call).

        Replaces the historical `self.js_source[:match_start].count('\\n') + 1`
        which was O(N) per call and quadratic across all matches in a sample.
        """
        return bisect.bisect_right(self._line_offsets, match_start)

    def _scan_text(self, text):
        """Run every encoded-string pattern over `text` and return a list of
        finding dicts. Stateless apart from the per-call `_line_offsets` cache,
        which `_find_line_number` reads — callers must reset it before invoking
        this so line numbers reference the text being scanned, not the previous
        one.
        """
        findings = []

        # Hex-encoded strings
        for m in _HEX_STRING_RE.finditer(text):
            raw = m.group()
            decoded = self._decode_hex_string(raw)
            if decoded and len(decoded) >= 4:
                findings.append({
                    'string': decoded[:4000],
                    'string_raw': raw[:4000],
                    'string_encoding': 'hex',
                    'string_offset': self._find_line_number(m.start()),
                    'string_length': len(decoded),
                    'string_raw_length': len(raw),
                    'string_entropy': self._calculate_text_entropy(decoded),
                })

        # Unicode-encoded strings
        for m in _UNICODE_STRING_RE.finditer(text):
            raw = m.group()
            decoded = self._decode_unicode_string(raw)
            if decoded and len(decoded) >= 3:
                findings.append({
                    'string': decoded[:4000],
                    'string_raw': raw[:4000],
                    'string_encoding': 'unicode',
                    'string_offset': self._find_line_number(m.start()),
                    'string_length': len(decoded),
                    'string_raw_length': len(raw),
                    'string_entropy': self._calculate_text_entropy(decoded),
                })

        # String.fromCharCode sequences
        for m in _CHARCODE_RE.finditer(text):
            raw = m.group()
            decoded = self._decode_charcode(m.group(1))
            if decoded and len(decoded) >= 4:
                findings.append({
                    'string': decoded[:4000],
                    'string_raw': raw[:4000],
                    'string_encoding': 'charcode',
                    'string_offset': self._find_line_number(m.start()),
                    'string_length': len(decoded),
                    'string_raw_length': len(raw),
                    'string_entropy': self._calculate_text_entropy(decoded),
                })

        # Base64-encoded strings
        for m in _BASE64_STRING_RE.finditer(text):
            raw = m.group(0)
            b64_val = m.group(1)
            decoded = self._decode_base64(b64_val)
            if decoded and len(decoded) >= 10:
                findings.append({
                    'string': decoded[:4000],
                    'string_raw': raw[:4000],
                    'string_encoding': 'base64',
                    'string_offset': self._find_line_number(m.start()),
                    'string_length': len(decoded),
                    'string_raw_length': len(raw),
                    'string_entropy': self._calculate_text_entropy(decoded),
                })

        # Concatenated strings (reassembled)
        for m in _CONCAT_STRING_RE.finditer(text):
            raw = m.group()
            reconstructed = self._reconstruct_concat(raw)
            if reconstructed and len(reconstructed) >= 20:
                findings.append({
                    'string': reconstructed[:4000],
                    'string_raw': raw[:4000],
                    'string_encoding': 'concat',
                    'string_offset': self._find_line_number(m.start()),
                    'string_length': len(reconstructed),
                    'string_raw_length': len(raw),
                    'string_entropy': self._calculate_text_entropy(reconstructed),
                })

        return findings

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

        # Pass 1: raw source. _line_offsets is keyed off whichever text is
        # currently being scanned so _find_line_number resolves to that text.
        self._line_offsets = line_offsets(src)
        findings = self._scan_text(src)

        # Pass 2: deobfuscated text, when the deobfuscator produced something
        # meaningfully different. Same patterns, but a different surface — for
        # samples where the encoded payload is hidden behind an outer wrapper
        # (e.g. array.join() + eval in Vjw0rm/WSH-RAT) only this pass yields
        # any rows at all.
        deobf_text, _ = self._context.deobfuscated
        if deobf_text and deobf_text != src:
            self._line_offsets = line_offsets(deobf_text)
            findings.extend(self._scan_text(deobf_text))

        if not findings:
            return None

        # Deduplicate by decoded string value (raw pass wins on collision: it
        # comes first in `findings`). A string that surfaces only in the
        # deobfuscated text still gets persisted, which is the whole point of
        # the second pass.
        seen_values = set()
        deduped = []
        for f in findings:
            val_key = f['string'][:100]
            if val_key not in seen_values:
                seen_values.add(val_key)
                deduped.append(f)

        self.string_findings = deduped[:500]  # Limit per file
        # Publish to the shared context so post-loop consumers (notably the IOC
        # plumbing in workers.py) can scrape the decoded strings without
        # holding a reference to this extractor instance.
        self._context.decoded_strings = self.string_findings
        return self.string_findings

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

            data = []
            for f in self.string_findings:
                data.append([
                    self.sha256,
                    f['string'],
                    f['string_raw'],
                    f['string_encoding'],
                    f['string_offset'],
                    f['string_length'],
                    f['string_raw_length'],
                    f['string_entropy'],
                ])

            column_names = [
                "sha256",
                "string",
                "string_raw",
                "string_encoding",
                "string_offset",
                "string_length",
                "string_raw_length",
                "string_entropy",
            ]

            column_type_names = [
                "FixedString(64)",
                "String",
                "String",
                "LowCardinality(String)",
                "UInt64",
                "UInt32",
                "UInt32",
                "Float32",
            ]

            return (data, column_names, column_type_names)

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