Ilaria Ciofini

22 papers Journal 22
YearRankTypeTitle / Venue / Authors
2024 J jnl
J. Comput. Chem.
Aurore E. F. Denjean, Jordan Rio, Ilaria Ciofini, Marie-Eve L. Perrin, Pierre-Adrien Payard
2024 J jnl
J. Comput. Chem.
Éric Brémond, Ilaria Ciofini, Frédéric Labat, Vincent Tognetti
2023 J jnl
J. Comput. Chem.
Lorenzo Briccolani-Bandini, Éric Brémond, Marco Pagliai, Gianni Cardini, Ilaria Ciofini, Carlo Adamo
2022 J jnl
J. Comput. Chem.
Feven Alemu Korsaye, Aurélien de la Lande, Ilaria Ciofini
2022 J jnl
J. Comput. Chem.
Davide Luise, Massimo Christian D'Alterio, Giovanni Talarico, Ilaria Ciofini, Frédéric Labat
2021 J jnl
J. Comput. Chem.
Bernardino Tirri, Gloria Mazzone, Alistar Ottochian, Jérôme Gomar, Umberto Raucci, Carlo Adamo, Ilaria Ciofini
2021 J jnl
J. Comput. Chem.
Éric Brémond, Alistar Ottochian, Ángel J. Pérez-Jiménez, Ilaria Ciofini, Giovanni Scalmani, Michael J. Frisch, Juan Carlos Sancho-García, Carlo Adamo
2021 J jnl
J. Comput. Chem.
Éric Brémond, Alistar Ottochian, Ángel J. Pérez-Jiménez, Ilaria Ciofini, Giovanni Scalmani, Michael J. Frisch, Juan Carlos Sancho-García, Carlo Adamo
2021 J jnl
J. Comput. Chem.
Davide Luise, Liam Wilbraham, Frédéric Labat, Ilaria Ciofini
2020 J jnl
J. Comput. Chem.
Umberto Raucci, Maria Gabriella Chiariello, Federico Coppola, Fulvio Perrella, Marika Savarese, Ilaria Ciofini, Nadia Rega
2020 J jnl
J. Comput. Chem.
Alistar Ottochian, Carmela Morgillo, Ilaria Ciofini, Michael J. Frisch, Giovanni Scalmani, Carlo Adamo
2020 J jnl
J. Comput. Chem.
Jun Su, Tao Zhu, Thierry Pauporté, Ilaria Ciofini, Frédéric Labat
2019 J jnl
J. Comput. Chem.
Juan Sanz García, Martial Boggio-Pasqua, Ilaria Ciofini, Marco Campetella
2019 J jnl
J. Comput. Chem.
Takafumi Shiraogawa, Gaëlle Candel, Ryoichi Fukuda, Ilaria Ciofini, Carlo Adamo, Akimitsu Okamoto, Masahiro Ehara
2019 J jnl
J. Comput. Chem.
Federica Maschietto, Juan Sanz García, Marco Campetella, Ilaria Ciofini
2018 J jnl
J. Comput. Chem.
Federica Maschietto, Marco Campetella, Michael J. Frisch, Giovanni Scalmani, Carlo Adamo, Ilaria Ciofini
2017 J jnl
J. Comput. Chem.
Marco Campetella, Federica Maschietto, Mike J. Frisch, Giovanni Scalmani, Ilaria Ciofini, Carlo Adamo
2017 J jnl
J. Comput. Chem.
Marika Savarese, Umberto Raucci, Ryoichi Fukuda, Carlo Adamo, Masahiro Ehara, Nadia Rega, Ilaria Ciofini
2017 J jnl
J. Comput. Chem.
Stefania Di Tommaso, Diane Bousquet, Delphine Moulin, Frédéric Baltenneck, Priscilla Riva, Hervé David, Aziz Fadli, Jérôme Gomar, Ilaria Ciofini, Carlo Adamo
2016 J jnl
J. Comput. Chem.
Davide Presti, Frédéric Labat, Alfonso Pedone, Michael J. Frisch, Hrant P. Hratchian, Ilaria Ciofini, Maria Cristina Menziani, Carlo Adamo
2013 J jnl
J. Comput. Chem.
Masahiro Ehara, Ryoichi Fukuda, Carlo Adamo, Ilaria Ciofini
2008 J jnl
J. Comput. Chem.
Denis Jacquemin, Eric A. Perpète, Ilaria Ciofini, Carlo Adamo
redb/extractors/js_extractors/js_context.py
← Index redb/extractors/js_extractors/js_context.py python
"""Per-sample shared state for the JavaScript extractor pipeline.

A `JSContext` is built exactly once per JS sample (in `workers.py`) and threaded
into every extractor that runs against that sample. It owns the disk read, the
decoded source text, the line-split cache, the Shannon text-entropy figure, the
shared `scan_source()` results, and the pyjsparser AST. Each of those is
computed lazily through `cached_property` so an extractor that doesn't need a
particular artefact does not pay for it.

Without this object, every JS extractor instance redoes the same disk read,
decode, scan, and (for any consumer) AST parse. With it, every extractor
shares one set of results.

`JSExtractor.__init__` accepts the context via a `context=` kwarg; if absent
(e.g. unit tests instantiating an extractor directly with `source=...`) it
builds a fresh context from the constructor arguments. Either path produces a
fully-populated context, so extractor code can always rely on
`self._context.scan` / `self._context.ast` / etc.
"""

from __future__ import annotations

import math
from collections import Counter
from dataclasses import dataclass
from functools import cached_property
from typing import Any, Dict, List, Optional

import chardet

from redb.extractors.js_extractors.js_patterns import scan_source


def decode_source(raw_bytes: bytes) -> str:
    """Decode raw JS bytes to text, honouring BOMs and falling back to chardet.

    Mirrors the historical `JSExtractor._decode_source` logic so existing tests
    continue to round-trip identically.
    """
    if not raw_bytes:
        return ""

    if raw_bytes[:3] == b"\xef\xbb\xbf":
        return raw_bytes[3:].decode("utf-8", errors="replace")
    if raw_bytes[:2] in (b"\xff\xfe", b"\xfe\xff"):
        return raw_bytes.decode("utf-16", errors="replace")

    try:
        return raw_bytes.decode("utf-8")
    except UnicodeDecodeError:
        pass

    try:
        detected = chardet.detect(raw_bytes)
        if detected and detected.get("encoding"):
            return raw_bytes.decode(detected["encoding"], errors="replace")
    except Exception:
        pass

    return raw_bytes.decode("latin-1", errors="replace")


def _text_entropy(text: str) -> float:
    """Shannon entropy of the character distribution of `text`, rounded to 4dp."""
    if not text:
        return 0.0
    counter = Counter(text)
    length = len(text)
    entropy = 0.0
    for count in counter.values():
        p = count / length
        if p > 0:
            entropy -= p * math.log2(p)
    return round(entropy, 4)


@dataclass
class JSContext:
    """Shared raw materials for one JS sample, consumed by every JS extractor.

    Cheap attributes (raw_bytes, source) are populated eagerly by the factory.
    Expensive ones (scan, ast) are cached_property — computed on first access
    and reused across every extractor that holds the same context.

    `content_type` is the magika label (e.g. `"javascript"`) carried alongside
    the source so the new code_text_content writer (and any future generic
    text-content writer) can record it without re-running magika. Defaults to
    `"javascript"` because by construction this context type is JS-specific;
    workers.py supplies the actual magika value when it builds the context.
    """

    filepath: str
    raw_bytes: bytes
    source: str
    log: Any = None
    content_type: str = "javascript"
    # Populated by JSStringsExtractor.extract() (the decoded/reconstructed
    # strings — hex/unicode/charcode/base64/concat unpacked into plaintext).
    # Read post-loop by the IOC plumbing in workers.py so any IOCs hidden
    # behind those encodings get scraped from the decoded form. Stays None
    # if JSStringsExtractor didn't run for this sample.
    decoded_strings: Optional[list] = None

    @cached_property
    def lines(self) -> List[str]:
        return self.source.splitlines() if self.source else []

    @cached_property
    def text_entropy(self) -> float:
        return _text_entropy(self.source)

    @cached_property
    def scan(self) -> Dict[str, Dict[str, object]]:
        """Result of running scan_source() exactly once over self.source."""
        return scan_source(self.source) if self.source else {}

    @cached_property
    def ast(self) -> Optional[Any]:
        """Lazy pyjsparser AST. Returns None if the parser is missing or fails.

        Extractors should treat None AST as "fall back to regex" — every
        AST-consuming extractor already handles that path.
        """
        if not self.source:
            return None
        try:
            import pyjsparser
            return pyjsparser.parse(self.source)
        except ImportError:
            if self.log is not None:
                self.log.debug("pyjsparser not installed, AST analysis skipped")
        except Exception as e:
            if self.log is not None:
                self.log.warning(f"AST parsing failed for {self.filepath}: {e}")
        return None

    @cached_property
    def deobfuscated(self) -> "tuple[Optional[str], Optional[str]]":
        """Run the configured JS deobfuscator (with jsbeautifier fallback) once
        per sample and cache the result. Returns `(text, normalizer_used)` or
        `(None, None)` if neither path produced output.

        Computed lazily on first access — samples whose pipeline never reads
        this don't pay the subprocess cost.
        """
        from redb.extractors.js_extractors.js_deobfuscator import deobfuscate
        return deobfuscate(self.source, self.log)

    @cached_property
    def scan_deobfuscated(self) -> Dict[str, Dict[str, object]]:
        """Result of running scan_source() exactly once over the deobfuscated
        text, keyed by PATTERNS only (FEATURE_PATTERNS are not consulted by
        the dual-pass consumers). Empty dict when there is no deobfuscated
        text or it equals the raw source.

        Two extractors consume the post-deobf API surface:
        `JSSuspiciousAPIsExtractor` (for revealed_by_deobf rows) and
        `JSDeobfuscationExtractor` (for the new_apis_found diff). Caching here
        means we scan the deobfuscated text once instead of twice per sample.
        """
        from redb.extractors.js_extractors.js_patterns import PATTERNS
        deobf_text, _ = self.deobfuscated
        if not deobf_text or deobf_text == self.source:
            return {}
        return scan_source(deobf_text, patterns=(PATTERNS,))

    @cached_property
    def xray(self):
        """Run @nodesecure/js-x-ray once per sample and cache the result.

        Returns an `XRayResult` (always — the function collapses every failure
        path to an empty result so callers don't have to special-case missing
        Node, missing package, timeouts, or parse errors). The
        `JSFeaturesExtractor` reads it for the obfuscator family name and for
        corroborating warning kinds; the heuristic falls back cleanly when
        `xray.obfuscator is None`.
        """
        from redb.extractors.js_extractors.js_xray import run
        return run(self.source, self.log)

    @classmethod
    def from_path(
        cls,
        filepath: str,
        log: Any = None,
        source: Optional[str] = None,
        raw_bytes: Optional[bytes] = None,
        content_type: str = "javascript",
    ) -> "JSContext":
        """Build a context from disk. `raw_bytes` and `source` are optional
        overrides — useful when the caller has already read or decoded the file.
        `content_type` is the magika label workers.py dispatched on; it lands
        on the context for the code_text_content writer to record.
        """
        if raw_bytes is None:
            with open(filepath, "rb") as f:
                raw_bytes = f.read()
        if source is None:
            source = decode_source(raw_bytes)
        return cls(
            filepath=filepath,
            raw_bytes=raw_bytes,
            source=source,
            log=log,
            content_type=content_type,
        )