Jakub Kozik

46 papers A* 3B 2Misc 1Journal 38Unranked 2
YearRankTypeTitle / Venue / Authors
2026 B ed.
SOFSEM
Jakub Kozik, Alexander Wolff
2024 J jnl
J. Comb. Theory B
Jakub Kozik, Piotr Micek, William T. Trotter
2024 J jnl
Electron. J. Comb.
Jakub Kozik, Bartosz Podkanowicz
2023 A* conf
ICALP
Andrzej Dorobisz, Jakub Kozik
2023 J jnl
CoRR
Andrzej Dorobisz, Jakub Kozik
2021 A* conf
ICALP
Jakub Kozik
2021 J jnl
CoRR
Jakub Kozik
2021 J jnl
CoRR
Andrzej Dorobisz, Jakub Kozik
2021 J jnl
CoRR
Lech Duraj, Jakub Kozik, Dmitry A. Shabanov
2021 J jnl
Eur. J. Comb.
Lech Duraj, Jakub Kozik, Dmitry A. Shabanov
2019 J jnl
CoRR
Jakub Kozik, Piotr Micek, William T. Trotter
2018 A* conf
ICALP
Lech Duraj, Grzegorz Gutowski, Jakub Kozik
2018 J jnl
CoRR
Lech Duraj, Grzegorz Gutowski, Jakub Kozik
2018 J jnl
Electron. J. Comb.
Jakub Kozik, Grzegorz Matecki
2016 J jnl
Electron. J. Comb.
Lech Duraj, Grzegorz Gutowski, Jakub Kozik
2016 J jnl
J. Comb. Theory B
Jakub Kozik, Dmitry A. Shabanov
2016 J jnl
Random Struct. Algorithms
Jakub Kozik
2016 J jnl
Comb.
Vida Dujmovic, Gwenaël Joret, Jakub Kozik, David R. Wood
2016 J jnl
Electron. J. Comb.
Adam Gagol, Gwenaël Joret, Jakub Kozik, Piotr Micek
2016 J jnl
CoRR
Adam Gagol, Gwenaël Joret, Jakub Kozik, Piotr Micek
2015 J jnl
Random Struct. Algorithms
Danila D. Cherkashin, Jakub Kozik
2015 J jnl
Electron. Notes Discret. Math.
Jakub Kozik, Dmitry A. Shabanov
2014 J jnl
CoRR
Jakub Kozik, Grzegorz Matecki
2014 J jnl
CoRR
Jakub Kozik, Dmitry A. Shabanov
2014 B conf
ISAAC
Grzegorz Gutowski, Jakub Kozik, Piotr Micek, Xuding Zhu
2014 J jnl
CoRR
Grzegorz Gutowski, Jakub Kozik, Piotr Micek, Xuding Zhu
2014 J jnl
Eur. J. Comb.
Jakub Kozik, Piotr Micek, Xuding Zhu
2014 J jnl
J. Comb. Theory B
Arkadiusz Pawlik, Jakub Kozik, Tomasz Krawczyk, Michal Lason, Piotr Micek, William T. Trotter, Bartosz Walczak
2013 J jnl
CoRR
Danila D. Cherkashin, Jakub Kozik
2013 J jnl
CoRR
Jakub Kozik
2013 J jnl
Random Struct. Algorithms
Jaroslaw Grytczuk, Jakub Kozik, Piotr Micek
2013 J jnl
SIAM J. Discret. Math.
Jakub Kozik, Piotr Micek
2013 J jnl
Discret. Comput. Geom.
Arkadiusz Pawlik, Jakub Kozik, Tomasz Krawczyk, Michal Lason, Piotr Micek, William T. Trotter, Bartosz Walczak
2012 J jnl
Ann. Pure Appl. Log.
Antoine Genitrini, Jakub Kozik
2012 J jnl
CoRR
Jakub Kozik, Piotr Micek
2012 J jnl
CoRR
Arkadiusz Pawlik, Jakub Kozik, Tomasz Krawczyk, Michal Lason, Piotr Micek, William T. Trotter, Bartosz Walczak
2012 J jnl
CoRR
Arkadiusz Pawlik, Jakub Kozik, Tomasz Krawczyk, Michal Lason, Piotr Micek, William T. Trotter, Bartosz Walczak
2011 J jnl
CoRR
Jaroslaw Grytczuk, Jakub Kozik, Piotr Micek
2011 J jnl
Electron. J. Comb.
Jaroslaw Grytczuk, Jakub Kozik, Marcin Witkowski
2011 J jnl
CoRR
Jaroslaw Grytczuk, Jakub Kozik, Piotr Micek
2011 J jnl
CoRR
Jaroslaw Grytczuk, Jakub Kozik, Marcin Witkowski
2011 J jnl
CoRR
Jakub Kozik, Piotr Micek, Xuding Zhu
2009 Misc conf
LFCS
Antoine Genitrini, Jakub Kozik
2009 J jnl
Log. Methods Comput. Sci.
René David, Christophe Raffalli, Guillaume Theyssier, Katarzyna Grygiel, Jakub Kozik, Marek Zaionc
2007 conf
TYPES
Antoine Genitrini, Jakub Kozik, Marek Zaionc
2004 conf
CLA
Jakub Kozik
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"