Vignesh Gopakumar

16 papers A* 1A 1Journal 14
YearRankTypeTitle / Venue / Authors
2026 J jnl
CoRR
Vignesh Gopakumar, Ander Gray, Daniel Giles, Lorenzo Zanisi, Matt J. Kusner, Timo Betcke, Stanislas Pamela, Marc Peter Deisenroth
2025 A* conf
ICML
Vignesh Gopakumar, Ander Gray, Lorenzo Zanisi, Timothy Nunn, Daniel Giles, Matt J. Kusner, Stanislas Pamela, Marc Peter Deisenroth
2025 J jnl
CoRR
Vignesh Gopakumar, Ander Gray, Lorenzo Zanisi, Timothy Nunn, Stanislas Pamela, Daniel Giles, Matt J. Kusner, Marc Peter Deisenroth
2025 A conf
UAI
Ander Gray, Vignesh Gopakumar, Sylvain Rousseau, Sébastien Destercke
2025 J jnl
CoRR
Ander Gray, Vignesh Gopakumar, Sylvain Rousseau, Sébastien Destercke
2025 J jnl
CoRR
James Panayis, Matt Field, Vignesh Gopakumar, Andrew Lahiff, Kristian Zarebski, Aby Abraham, Jonathan L. Hodges
2025 J jnl
Comput. Phys. Commun.
Stanislas Pamela, Naomi Carey, Johannes Brandstetter, Rob J. Akers, Lorenzo Zanisi, James Buchanan, Vignesh Gopakumar, Matthias Hoelzl, Guido Huijsmans, Kamran Pentland, James Tickner, G. Antonucci
2024 J jnl
CoRR
Vignesh Gopakumar, Ander Gray, Joel Oskarsson, Lorenzo Zanisi, Stanislas Pamela, Daniel Giles, Matt J. Kusner, Marc Peter Deisenroth
2024 J jnl
CoRR
Vignesh Gopakumar, Joel Oskarrson, Ander Gray, Lorenzo Zanisi, Stanislas Pamela, Daniel Giles, Matt J. Kusner, Marc Peter Deisenroth
2023 J jnl
Mach. Learn. Sci. Technol.
Petr Mánek, Graham W. Van Goffrier, Vignesh Gopakumar, Nikolaos Nikolaou, Jonathan Shimwell, Ingo P. Waldmann
2023 J jnl
CoRR
Vignesh Gopakumar, Stanislas Pamela, Lorenzo Zanisi
2023 J jnl
CoRR
Vignesh Gopakumar, Stanislas Pamela, Lorenzo Zanisi, Zongyi Li, Ander Gray, Daniel Brennand, Nitesh Bhatia, Gregory Stathopoulos, Matt J. Kusner, Marc Peter Deisenroth, Anima Anandkumar, JOREK Team, MAST Team
2023 J jnl
CoRR
Timothy Nunn, Vignesh Gopakumar, Sebastien Kahn
2022 J jnl
CoRR
Vignesh Gopakumar, Stanislas Pamela, Debasmita Samaddar
2021 J jnl
CoRR
Petr Mánek, Graham W. Van Goffrier, Vignesh Gopakumar, Nikolaos Nikolaou, Jonathan Shimwell, Ingo P. Waldmann
2020 J jnl
Mach. Learn. Sci. Technol.
Vignesh Gopakumar, D. Samaddar
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"