Camilo Perez Quintero

20 papers A* 7A 3B 2Journal 4Unranked 4
YearRankTypeTitle / Venue / Authors
2021 J jnl
CoRR
Yuqing Du, Nicholas J. Hetherington, Chu Lip Oon, Wesley P. Chan, Camilo Perez Quintero, Elizabeth A. Croft, H. F. Machiel Van der Loos
2020 B conf
RO-MAN
Wesley P. Chan, Maram Sakr, Camilo Perez Quintero, Elizabeth A. Croft, H. F. Machiel Van der Loos
2019 A* conf
ICRA
Yuqing Du, Nicholas J. Hetherington, Chu Lip Oon, Wesley P. Chan, Camilo Perez Quintero, Elizabeth A. Croft, H. F. Machiel Van der Loos
2018 conf
HRI (Companion)
Sara Sheikholeslami, Justin W. Hart, Wesley P. Chan, Camilo Perez Quintero, Elizabeth A. Croft
2018 A conf
IROS
Camilo Perez Quintero, Sarah Li, Matthew K. X. J. Pan, Wesley P. Chan, H. F. Machiel Van der Loos, Elizabeth A. Croft
2017 J jnl
CoRR
Mennatullah Siam, Abhineet Singh, Camilo Perez Quintero, Martin Jägersand
2017 A* conf
ICRA
Camilo Perez Quintero, Masood Dehghan, Oscar A. Ramirez, Marcelo H. Ang Jr., Martin Jägersand
2017 J jnl
CoRR
Sepehr Valipour, Camilo Perez Quintero, Martin Jägersand
2017 A conf
IROS
Sepehr Valipour, Camilo Perez Quintero, Martin Jägersand
2017 J jnl
CoRR
Xuebin Qin, Shida He, Camilo Perez Quintero, Abhineet Singh, Masood Dehghan, Martin Jägersand
2017 A conf
IROS
Xuebin Qin, Shida He, Camilo Perez Quintero, Abhineet Singh, Masood Dehghan, Martin Jägersand
2016 A* conf
ICRA
Mona Gridseth, Oscar A. Ramirez, Camilo Perez Quintero, Martin Jägersand
2015 A* conf
ICRA
Huan Hu, Camilo Perez Quintero, Hanxu Sun, Martin Jägersand
2015 A* conf
ICRA
Ankush Roy, Xi Zhang, Nina Wolleb, Camilo Perez Quintero, Martin Jägersand
2015 A* conf
ICRA
Camilo Perez Quintero, Oscar A. Ramirez, Martin Jägersand
2015 B conf
RO-MAN
Camilo Perez Quintero, Romeo Tatsambon Fomena, Mona Gridseth, Martin Jägersand
2014 conf
CRV
Camilo Perez Quintero, Romeo Tatsambon Fomena, Azad Shademan, Oscar A. Ramirez, Martin Jägersand
2013 conf
Robotics: Science and Systems
Travis Dick, Camilo Perez Quintero, Martin Jägersand, Azad Shademan
2013 A* conf
ICRA
Camilo Perez Quintero, Romeo Tatsambon Fomena, Azad Shademan, Nina Wolleb, Travis Dick, Martin Jägersand
2013 conf
CRV
Romeo Tatsambon Fomena, Camilo Perez Quintero, Mona Gridseth, Martin Jägersand
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"